Conference Agenda

FLEX (FLuorescence EXplorer) is ESA’s 8th Earth Explorer mission and its development is coming to an end with the completion of the satellite, the ground segment and the preparations for a launch in September 2026.

Major milestones have been achieved in the past months with the testing and delivery of the flight hardware of the FLORIS instrument and the satellite platform in 2025. A successful calibration and characterisation campaign in the first half of 2025 confirmed the expected instrument performances. The delivery of FLORIS and its mating with the satellite platform mid-2025 marked the start of the satellite integration and test phase which is ongoing and will finish by end-April 2026. Preparations on the ground segment are all progressing nominally and will be ready in time for launch. All launcher activities are on track and the launch campaign in CSG/Kourou (French Guiana) is about to start end June.

The status of the FLEX mission development progress and its finalisation for a launch early September 2026 will be provided including an overview on the 3-month in-orbit Commissioning Phase activties.

The FLEX instrument Performance Simulator (FIPS) is a software allowing the simulation of synthetic raw data representative of the FLEX instrument radiometric, spectral and geometric performances. The FIPS simulates the optical performance of the instrument telescope and the two spectrometers. Particular emphasis was put in simulating the straylight performance of the instrument. The full acquisition chain from detectors to onboard data generation is also simulated allowing the generation of instrument source packets.

The Ground Processor Prototype (GPP) processes both synthetic and real instrument Earth Observation data, from the instrument source packet up to the Level 1B user product. The processing includes dark signal removal, smearing correction, non-linearity correction, straylight correction, absolute radiometric calibration and flat field equalization. The resulting Level 1B product includes geolocated top-of atmosphere radiances, associated data quality information and uncertainty estimates. In addition, also included are meteorological data and instrument characteristics required for further processing of the data to the Level 2.

The GPP is also designed to process data from the instrument whilst operating in various calibration modes. This functionality will enable in-flight characterization and calibration of the instrument.

The instrument radiometric calibration will be performed in-flight using a Sun diffuser. It will be further monitored and validated using regular observations of the Moon and deep convective clouds. The non-linearity of the instrument detector chain will be characterized on ground and then verified in-flight using natural targets at various level of signal and associated instrument integration times.

The in-flight spectral characterization will be based on the measurements of atmospheric absorption features as well as solar absorption lines observed on the onboard sun diffuser.

The absolute geometric performance will be monitored and corrected for through spatial feature matching of nominal EO data with a database of georeferenced high spatial resolution (30 m) images. The spatial co-registration between the high resolution and low spectral resolution spectrometers will be ensured by spatial feature matching between the two spectrometers’ data.

The ESA FLEX (Fluorescence Explorer) mission aims to study the photosynthetic activity of terrestrial vegetation by measuring Sun-Induced Fluorescence (SIF) emission and key biophysical variables for required to interpret the SIF signal. The potential applications of FLEX mission products range from advancing our understanding of the carbon cycle to supporting food security and water quality monitoring. To achieve these objectives, a large consortium of scientific experts and industrial partners forms the Data Innovation and Science Cluster (DISC). The FLEX DISC aims to develop and industrialize the Level-1C and Level-2 data processing algorithms, conduct calibration & validation activities, and provide a collaborative platform for mission data exploitation by experts and end-users.

With the first development phase of the Level-2 Processor completed, the product definition and data processing algorithms are reaching a mature and stable state. FLEX Level-1C products consist of approximately 3-minute acquisition slices (~1300x150 km²) containing top-of-atmosphere radiances from FLORIS, OLCI, and SLSTR instruments, all projected onto a common working frame (the FLORIS-HR focal plane). Each Level-1C product is accompanied by comprehensive metadata, including spectral characteristics, uncertainty estimates, geometry, and meteorological information. Level-2 products contain bio- and geo-physical variables projected in UTM coordinates using Sentinel-2 gridding. Key variables include surface apparent reflectance and at-surface solar irradiance, fluorescence emission spectra, classical biophysical parameters (leaf area index, leaf chlorophyll content, leaf carotenoid content, fraction of absorbed photosynthetically active radiation), as well as photosynthesis-related variables such as electron transport rate, fAPAR by Chlorophyll, fluorescence quantum efficiency, regulated energy distribution.

As the project enters Phase 2, the focus shifts towards maximizing algorithm accuracy and robustness, further industrializing the processing chain, and preparing for commissioning rehearsals ahead of launch.

In this presentation, we review the current status of the FLEX DISC activities, describe the characteristics of the mission products, and present the latest algorithm validation results. This contribution complements other presentations that focus on specific aspects of the data processing algorithms. Participants in the FLEX Fluorescence Workshop are expected to gain first-hand insight into the FLEX mission products, supporting their readiness for mission exploitation by end users.

The ESA FLuorescence Explorer (FLEX) mission will provide unique observations of sun-induced chlorophyll fluorescence, opening new opportunities for understanding vegetation functioning, photosynthesis, and ecosystem responses to environmental stress. To prepare the scientific community for the exploitation of FLEX data products, the FLEX Data, Innovation and Science Cluster (DISC) has developed the FLEX Collaborative Platform (CP), a cloud-based environment designed to support data access, processing, calibration, validation, and scientific analysis.

This contribution will present the FLEX Collaborative Platform as a key enabler for FLEX data exploitation, bridging the gap between algorithm development, operational processing, and scientific research. The platform provides access to FLEX products and auxiliary datasets, integrated development environments (e.g. Jupyter notebooks and containerised processing tools), and scalable computing resources, allowing users to work close to the data. By adopting a cloud-native and reproducible approach, the CP supports transparent and traceable scientific workflows throughout the FLEX mission lifecycle.

We will describe the architecture and core services of the FLEX CP, with particular emphasis on the design of workflows relevant to the fluorescence community. These will include: (i) interactive exploration of FLEX Level-2 products and derived variables; (ii) reprocessing of FLEX datasets using updated processing chains or algorithm variants; and (iii) integration of in-situ measurements for calibration and validation activities. Practical examples will illustrate how scientists can combine FLEX data with external datasets to assess product quality, perform comparative analyses, and support downstream applications.

By providing a common, cloud-based environment for data access, processing, and validation, the FLEX CP underpins a wide range of scientific studies addressed in this workshop, including vegetation stress detection, ecosystem functioning, and multi-mission data analysis. Beyond individual use cases, the FLEX CP fosters collaboration between scientists, algorithm developers, and validation teams by providing a shared environment for experimentation and knowledge exchange. This contribution will discuss how the FLEX CP supports the mission readiness ahead of launch and facilitates early science activities by lowering the barrier to data access and advanced processing.

The FLEX Collaborative Platform represents a strategic component of the FLEX ecosystem, ensuring that the community can efficiently exploit fluorescence observations and maximise the scientific return of the mission.

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The European Space Agency’s (ESA) Fluorescence Explorer (FLEX) mission is designed to operate in tandem with the Sentinel-3 mission, utilising the combined capabilities of both platforms’ instruments. On board Sentinel-3, the OLCI multispectral instrument and the SLSTR dual-view conical scanning broadband sensor are utilised to characterise key atmospheric components including aerosols and water vapour and to facilitate cloud detection and screening. FLEX, on the other hand, is equipped with the FLORIS high-resolution spectrometer covering wavelengths from 500 to 780 nm with a spectral resolution ranging from 0.3 to 3 nm and a spectral sampling interval between 0.1–2 nm. Such a high spectral resolution allows the disentangling of the solar-induced chlorophyll fluorescence (SIF) signal emitted by vegetation alongside the photosynthetic activity. The accurate estimation of the SIF signal within the 650-780 nm range is a primary FLEX mission objective. This estimation is challenging as the SIF signal – an in vivo proxy for photosynthesis – comprises only a small fraction (∼4%) of the top-of-atmosphere radiance in the red and far-red regions. Therefore, for an accurate spectrally-resolved SIF retrieval, the atmospheric correction process plays a crucial role as inaccuracies in this step can propagate into errors in the final SIF estimates.

This presentation outlines the design and implementation of FLEX’s Level-2 atmospheric correction processor developed as part of the Level 2 Prototype Processor (L2PP) for the ESA FLEX-DISC project (2024-2030). The FLEX-DISC initiative (reference 4000144004/24/I-DT) contributes to the FLEX mission ground segment preparations activities, focusing after launch on supporting FLEX’s commissioning and product validation phases. In the event that Sentinel-3 is unavailable, the processor switches to a contingency processing branch which is described.

To assess the current performance, the results of the L2PP atmospheric correction are presented. These evaluated results include the retrieved atmospheric parameters such as aerosols and water vapour and the apparent surface reflectance which is further used for the retrieval of the SIF. The atmospheric correction is presented for selected scenes from the FLEX End-To-End Mission Performance Simulator (FLEX-E) project. These scenes are suitable for testing the correction as they are created using the FLEX observation model and depict various atmospheric conditions including aerosols and clouds over a realistic surface in the Catalonia region of Spain with an optional elevation model.

[1] European Space Agency. Flex - fluorescence explorer mission, 2024. URL https://www.esa.int/Applications/Observing_the_Earth/FutureEO/FLEX. Accessed: 2024-10-29.

[2] European Space Agency. Flex: Fluorescence explorer mission – report for mission selection. Esa sp-1330/2, European Space Agency (ESA), 2008. URL https://esamultimedia.esa.int/docs/EarthObservation/SP1330-2_FLEX.pdf. Accessed: 2024-10-29.

[3] Neus Sabater, Pekka Kolmonen, Shari Van Wittenberghe, Antti Arola, and José Moreno. Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and pri region dynamics from space. Remote Sensing of Environment, 254:112226, 2021.

This contribution presents the design and implementation of the Level-2 Solar-Induced Fluorescence (SIF) retrieval processor developed for the FLuORescence Imaging Spectrometer (FLORIS) onboard ESA’s 8^th Earth Explorer FLEX mission. SIF retrieval is a core component of the Level-2 Prototype Processor (L2PP), developed within the ESA FLEX Data Innovation and Science Cluster (DISC) project. The L2PP consists of four sequential modules: (i) L1C, performing geometric, radiometric, and spectral co-registration of FLORIS and Sentinel-3 observations; (ii) L2A, providing atmospheric correction and retrieval of surface apparent reflectance (R*) and surface solar irradiance; (iii) L2B, dedicated to SIF retrieval; and (iv) L2C, which estimates biophysical and photosynthesis related parameters. This work focuses on the L2B module, which retrieves spectrally resolved SIF in the 670–780 nm range and derived products, including SIF in the O₂ absorption bands, red and far-red peak intensity and position, and spectrally integrated SIF.

The L2B module disentangles the SIF from the actual surface reflectance (R) by exploiting the outputs provided by the L2A module, namely the R* spectrum and its associated uncertainty. The inversion is performed within an Optimal Estimation framework, where the inverse problem is solved using Bayes’ theorem considering probability densities and assuming Gaussian distributions for the uncertainties. The forward model simulates R*, and the state vector is iteratively optimized by minimizing a cost function that accounts for both measurement residuals and a-priori constraints. Measurement and a-priori covariance matrices are used to weight the spectral information and to regularize the solution, respectively. Uncertainties propagated from upstream modules are ingested as inputs and consistently propagated through the L2B processing, ensuring coherent uncertainty characterization along the full processing chain.

Recent developments of the SIF retrieval module include the implementation of the Error Consistency Method (ECM) as an iterative regularization strategy. The inversion now consists of two phases: an initial non-regularized phase, followed by a regularized phase guided by ECM. In addition, a new Bidirectional Reflectance Distribution Function (BRDF) model has been introduced to improve the representation of surface reflectance anisotropy. This improvement is particularly important within the O₂ absorption bands, where accurate radiative transfer modelling is critical for reliable SIF retrieval.

The performance of the L2B processor has been evaluated using Test Data Sets generated by the Scene Generation Module and mission End-to-End simulations. Retrieval accuracy was assessed across different canopy types, atmospheric conditions, and instrumental configurations. Results show robust performance under ideal conditions, with low dispersion and high correlation between retrieved and reference SIF. When instrumental noise is introduced, an increase in dispersion is observed, leading to reduced precision. Simulations including radiometric gain uncertainties exhibit additional positive biases, highlighting the sensitivity of SIF retrieval to calibration errors.

Overall, this work demonstrates the feasibility of operational SIF full spectrum retrieval within the FLEX mission framework. Ongoing investigations focus on disentangling the impact of different instrumental noise components and improving the numerical robustness of the inversion, particularly during the initial non-regularized iterations, to mitigate noise sensitivity while preserving retrieval accuracy.

A unique feature of the Fluorescence Explorer (FLEX) mission is the combination of a wide band VNIR hyperspectral spectrometer of relatively low spectral resolution (LR) with a narrow band sub-nanometre spectrometer (HR) for fluorescence retrieval. The combination of HR and LR enables the retrieval of data products related to photosynthesis. The main goal is to differentiate the energy dissipation pathways of photochemistry and heat. In the Level-2 algorithm this is achieved by estimating the fluorescence emission efficiency (FQE) and the non-photochemical quenching (NPQ).

We present the theoretical background of the algorithm for these quantities, and review its performance. First, the fluorescence escape probability and the absorbed photosynthetically active radiation (aPAR) are retrieved with machine learning algorithms trained with a globally representative dataset of spectra generated with the model SCOPE, along with the leaf area index (LAI), leaf chlorophyll content (LCC), and leaf carotenoid content (LCCAR). Second, the fluorescence quantum efficiency (FQE) is computed by normalizing the fluorescence by the escape probability and the absorbed PAR. Third, the NPQ is retrieved by decomposing a soil-corrected reflectance spectrum with principle component analysis (PCA) into fast and slow varying components, and attributing the fast components to NPQ. Finally, the NPQ and FQE are combined using Bayasian statistics to estimate the photochemical and non-photochemical yields and electron transport rate (ETR). A weak prior relationship between NPQ and FQE stabilizes the results. The uncertainty of the data products is estimated by propagation of the uncertainty of reflectance and SIF, and inclusion of uncertainties in prior coefficients. The algorithms have been validated to synthetic FLEX data and field data collected with the FLoX instrument in several campaigns.

The FLuorescence EXplorer (FLEX) mission, developed by the European Space Agency (ESA) as its 8th Earth Explorer, will provide global maps of vegetation fluorescence as an indicator of photosynthetic activity together with the necessary parameters for deriving the amount of carbon assimilated by plants. FLEX is currently planned to be launched in 2026. The FLEX on-board instrument, the FLuORescence Imaging Spectrometer (FLORIS) sensor, will acquire data in the 500 - 780 nm spectral range with a spectral resolution between 0.3 nm (High Resolution, HR) and 1.8 nm (Low Resolution, LR). The spectral sampling interval will be from 0.1 nm in the oxygen absorption bands (748-769 nm and 686–697 nm) up to 2 nm outside the atmospheric absorption bands. The FLEX satellite will deliver data at a spatial resolution of 300 meters, with observations scheduled around 10:00 local time. It will have a swath width of 150 km and a repeat cycle of 27 days. FLEX will operate in tandem with Sentinel-3, working synchronously with the Sentinel-3 Camera 4 (nadir-looking), which has a 14-degree field of view. These characteristics make the validation of FLEX products complex and challenging, especially for those with high dynamism and significant spatial variability. In this context, this contribution provides an overview of the FLEX strategy for the calibration and validation (cal/val) of the satellite’s operational Level 1C and Level 2 science data products.

Dedicated tools and resources are being developed to support the validation and quality assessment processes for L1C and L2. An interactive portal (i.e. the FLEX Collaborative Platform, CP), is under development and it is expected to provide important validation support functionalities. The FLEX CP will provide analysis tools to enable assessment of quality indicators from specific products and address any specialized data processing requirements.

Since FLEX will provide global products, validation activities will be conducted in a wide range of global climate and vegetation conditions. Product accuracy will be assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. The input data for validation origins from different ground networks, as well as field campaigns. The Radiometric Calibration Network (RadCalNet)and VICALOPS sites will be exploited for L1C top of atmosphere radiances products, while AERONET (AErosol RObotic NETwork), HYPERNETS and the upcoming International Network of Sun Induced Chlorophyll Fluorescence (INSIF) will be exploited for L2 reflectance and fluorescence products. Sentinel 2 will be used for geolocation validation of the L2 products. For vegetation biophysical products and surface temperature, the LAND VALidation (LANDVAL), the Ground-Based Observations for Validation (GBOV), the Surface Radiation Budget Network (SURFRAD) Atmospheric Radiation Measurement (ARM) and the Advanced Surface Temperature Radiometer Network (ASTeRN) and the United States Climate Reference Network (USCRN) will be exploited

Different validation categories will be exploited, encompassing direct, indirect approaches and inter-comparison with space products, with the aim to fully validate FLEX core products according to the Committee on Earth Observation Satellites (CEOS) guidelines. A comprehensive strategy for the validation of FLEX operational products is planned. This strategy ensures the traceability to the mission requirements, and guarantee that all parameters relevant for the operational products have been adequately validated. The mission requirements are highly challenging, and they cover the end-to-end Earth observation system including high-level requirements, mission operations, data product development and processing, data distribution and data archiving. The validation of FLEX products will consider that both the FLORIS products and the ground truth measurements have inherent uncertainties and variances due to several factors. Risks associated with the validation of the FLEX products have been identified, possible mitigation scenarios have been outlined and back-up solutions proposed. Finally, the validation timeline during pre-launch preparation, commissioning, and routine operations will be detailed in the presentation.

Automated measurement networks such as INSIF and LANDHYPERNET will provide the main source of reference data for the bottom-of-atmosphere reflectance validation of FLEX. One of the main challenges for this kind of validation activities is due to the mismatch between the field of view of the in-situ instruments (between 1 and 10 m) and the FLEX pixel size (300m). In order to make the in-situ measurements representative of the entire FLEX pixel, an upscaling needs to be performed. This can be done by using a third, auxiliary, reflectance dataset covering the whole FLEX pixel with a higher spatial resolution e.g. Sentinel-2 (S2; 10 – 60 m) L2A. Here the auxiliary reflectance data at the location of the in-situ measurement is compared to the mean auxiliary reflectance over the region of interest (ROI) used for the FLEX validation (one or multiple FLEX pixels).

There are significant uncertainties in the upscaling process related to the spatial variability of the validation site being used, which can vary both in time and space due to changes to the surface within a pixel. This spatial variability can be quantified by calculating the standard deviation of the auxiliary pixels within the validation ROI. Since spatial variability varies with spatial scale, a scaling relationship can be established for each validation site by using different spatial resolutions. Which then can be used to scale the spatial variability to the two scales relevant for the upscaling (i.e. the FOV of the in-situ instrument and the pixel size of FLEX), as well as to determine the uncertainties on the upscaling factor. These uncertainties will be spectrally interpolated to the FLEX wavelengths. Results from the recent ESA-funded FRM4FLUO campaign and from commercial high-resolution satellite data have been used to validate this approach.

In addition to the spatial uncertainties, we will also discuss the uncertainties due to temporal variability (related to the small temporal mismatch between the field measurements and FLEX), as well as the main uncertainty components on the reference and FLEX reflectance data. All these are combined in a metrologically robust validation metric to determine whether or not the FLEX reflectances are consistent with expectations and mission requirements.

The ESA FLuorescence EXplorer (FLEX) mission will offer an unprecedented global view of terrestrial photosynthesis through high-resolution observations of sun-induced chlorophyll fluorescence (SIF). Realizing the full scientific potential of FLEX, however, critically depends on the availability of robust and traceable calibration and validation (Cal/Val) strategies for Level-2B (L2B) SIF products. Validating satellite-scale SIF remains inherently challenging, particularly in heterogeneous landscapes where point-based ground measurements may fail to represent the satellite footprint, and conventional airborne imaging approaches are costly and temporally constrained.

To address these limitations, the ESA DISC project explores a multi-scale FLEX Cal/Val framework that bridges ground, airborne, and satellite observations. This contribution presents the FLEX L2B validation concept and the initial findings of testing complementary validation approaches. The methodologies proposed were evaluated using both simulated datasets and real measurements acquired within the ESA FRM4FLUO project.

The FLEX validation strategy primarily relies on in situ–based approaches, which are supported by image-based methods and inter-comparisons with other satellite missions. Two complementary ground-based validation scenarios are considered: single-point validation, based on continuous tower-based spectrometer measurements at well-characterized sites, and multi-point validation, which uses spatial sampling from unmanned aerial systems (UAS) or mobile platforms to explicitly address sub-pixel heterogeneity. Scale-bridging between ground and satellite observations is achieved through transfer functions derived from high-resolution satellite data, while UAS acquisitions are synchronized with FLEX overpasses to minimise temporal mismatches.

The experimental dataset was collected during two intensive field campaigns in agricultural areas in Tuscany (Italy) in May and June 2025. Multi-scale SIF measurements were acquired using ground-based FloX systems and airborne platforms, including the lightweight UAS-mounted AirFloX system, as well as a helicopter-mounted configuration (HELiPOD). Dedicated optimisation methods supported flight planning and sampling design, maximising spatial representativeness while minimising measurement effort.

The results demonstrate that the proposed FLEX Cal/Val approaches are robust under realistic observational conditions. Tower-based measurements provide accurate local validation when supported by appropriate transfer functions, while UAS-based multi-point observations effectively capture spatial variability within the FLEX footprint, significantly improving representativeness. Overall, this study emphasises the key role of UAS-based SIF observations in bridging the gap between ground and space-based measurements, providing a practical and scalable pathway toward reliable validation of FLEX L2B SIF products.

The FLuorescence EXplorer (FLEX) mission, developed by the European Space Agency (ESA), will deliver spatially explicit maps of key biophysical and photosynthesis-related parameters, contributing to an improved understanding of the global carbon and water cycles. The FLEX Level-2 (L2) biophysical products can be grouped into two main categories: (i) traditional biophysical products, including leaf area index (LAI), leaf chlorophyll content (LCC), and the fraction of absorbed photosynthetically active radiation (fAPAR); and (ii) advanced biophysical (photosynthesis-related) products, including absorbed photosynthetically active radiation by chlorophyll a and b (APARchl), leaf carotenoid content (LCARC), fluorescence quantum efficiency (FQE), reversible energy dissipation (RED), electron transport rate (ETR), and fluorescence escape probability (fesc).

FLEX L2 biophysical products are fundamental to the future development of higher-level (L3 and L4) products and therefore require rigorous quality assessment. A comprehensive validation strategy has been developed to quantify their accuracy, consistency, and uncertainty. This strategy integrates direct and indirect validation approaches. Direct validation is supported by in situ observations acquired during dedicated field campaigns as well as by data provided by established observation networks, such as the Ground-Based Observations for Validation (GBOV) network and the forthcoming International Network of Sun-Induced Chlorophyll Fluorescence (INSIF). These datasets include measurements of multiple biophysical parameters, surface reflectance, and chlorophyll fluorescence. Indirect validation strategies include the comparison of FLEX L2 biophysical products with proxy data sources or with variables estimated using independent models or algorithms. In particular, FLEX L2 biophysical products will be evaluated against values retrieved from radiometric measurements through radiative transfer model inversion. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model will serve as the primary framework for this purpose. Another indirect validation strategy involves the systematic intercomparison of the traditional FLEX L2 biophysical products LAI and fAPAR with comparable products derived from other satellite data (e.g. Sentinel-3, Sentinel-2). This intercomparison is intended to assess the consistency, robustness, and overall performance of the FLEX L2 traditional biophysical products across different sensors, spatial resolutions, and retrieval methodologies, thereby strengthening confidence in their accuracy and long-term applicability.

This contribution will give an overview of the different validation strategies currently being developed to assess the performance of the FLEX L2 biophysical products. It further provides insights into the evaluation and refinement of the validation approaches based on data acquired during field campaigns conducted in spring and summer 2025 as part of the ESA Fiducial Reference Measurements for Fluorescence (FRM4FLUO) project.

FLEXvalGER consists of nine scientific and two industrial partners that will validate ESA‑FLEX products over Germany during FLEX commissioning/operational phases. It combines expertise in acquiring, processing and validating in‑situ, UAV, airborne and space‑borne observations. Validation of FLEX includes: (A) direct comparison of satellite data with ground measurements, (B) indirect validation through radiative‑transfer‐model inversion, (C) inter‑comparison with other satellite products (DESIS, EnMAP, Sentinel‑5P) and (D) time‑series analysis. The network of well‑established research sites across Germany hosts dedicated infrastructure such as FloX fluorescence towers, eddy-covariance (EC) flux towers and HYPERNETS installations. The validation effort addresses agriculture and forest ecosystems, airborne and space‑borne reference data, and instrument calibration.

Agriculture: FZJ will focus on crops while deploying FloX towers (MONI/MICRO-PAMs and other validation instruments) together with complementary UAV AirFloX and HyPlant airborne hyperspectral/fluorescence flights in parallel to FLEX overpasses close to Jülich. University of Göttingen will add a 2 m FloX tower next to an EC flux tower at the Reinshof site while recording in-situ validation parameters. GFZ will carry out HySpex and FluorSpec UAV surveys together with HYPSTAR and other reference measurements at the Heydenhof site.

Forest: University of Freiburg is running a sensor-network site with continuous FloX measurements from an EC flux tower (45 m), supplemented by in-situ validation data, UAV surveys, and additional atmospheric parameter monitoring. University of Göttingen plans to integrate a FloX on the EC tower at Leinefelde site, together with in-situ validation and targeted UAV LiDAR and spectral imaging. University of Leipzig will operate a moveable crane‑mounted FloX at 40 m at the Auwald site together with in-situ validation and destructive leaf sampling measurements. GFZ will supply HYPSTAR and RoX data from a moveable canopy crane together with complementary in situ and UAV data. JKI will install low‑cost PAR/DHP sensors at the Hakel site to collect reference measurements of LAI and fAPAR, complemented by UAV LiDAR and optical imagery.

Instrumentation & cross‑mission: DLR will compare FLEX to HyPlant/HySpex/DESIS/EnMAP, validate radiometric and spectral aspects of the FLEX L1C and L2 products, and retrieves O₂‑A SIF with detailed error models, producing high‑resolution SIF maps that are down‑scaled to FLEX pixels. Maitec will contribute to SIF retrieval and a sensor‑specific uncertainty framework for all airborne/spaceborne SIF products. MPI-BGC plans to extract Sentinel‑5P/TROPOMI footprint‑level SIF and Sentinel‑2 NIRv/fesc over 10×10 km validation zones, attempting to separate radiation, structure and physiology effects, and compares with FLEX Level‑2B/L2C. JB Hyperspectral plans for on-site calibration of FloXes, equipping them with MoMo real‑time data links and running a unified processing chain feeding data into the INSIF network. Walz operates a central PAM calibration laboratory, performs regular cross‑calibrations of devices and maintains calibrated loan units to guarantee continuous fluorescence measurements.

Validated data products include almost all FLEX L1C-L2C data products with specific emphasis on in situ data for L2C product validation. Altogether, through these interlinked activities, the consortium will deliver a fully traceable, uncertainty‑quantified validation of FLEX Level‑2 products across representative agricultural and forest ecosystems in Germany.

The SpaFLEX project, funded by the Spanish Ministry of Science and Innovation, establishes a comprehensive Calibration and Validation (Cal/Val) framework for the upcoming ESA FLEX-S3 mission in Spain. Focused on Level-2 products—specifically Sun-Induced Chlorophyll Fluorescence (SIF) and reflectance—the project defines standardized sampling protocols across three diverse Spanish ecosystems: agricultural, forest, and Mediterranean dehesa. The goal is to provide accurate ground-truth data by characterizing spatial heterogeneity through a multi-scale protocol within the 300x300 meter FLEX pixel.

A core component of the project is the spatial heterogeneity characterization strategy, which optimizes the distribution of Elementary Sampling Units (ESUs) within the 300x300 meter FLEX pixel. Using Sentinel-2 biophysical products as proxies for SIF and Non-Photochemical Quenching (NPQ), the project employs a flexible framework that adapts to the structural complexity of each site. In relatively uniform ecosystems, the strategy determines the total sample size first to ensure an unbiased global distribution. In contrast, for highly fragmented landscapes, a stratification-first approach is used to ensure field measurements capture distinct vegetation classes and environmental gradients. By incorporating semivariogram analysis to account for spatial autocorrelation, this dual-flow strategy ensures that ground-truth data accurately reflect the sub-pixel variability inherent in the FLEX footprint. The uncertainty propagation of in-situ SIF and surface reflectance for the 300x300 m area representing a FLEX pixel, is performed using the Law of Propagation of Uncertainties and Monte Carlo methods.

To bridge the gap between leaf-level mechanisms and satellite observations, the SpaFLEX project implements a multi-scale approach. This integrates leaf-level measurements (FluoWat) with canopy-level continuous monitoring (FLoX, Piccolo-FluoCat) and spectroradiometric surveys (ASD FieldSpec). These ground data are scaled using UAV-based hyperspectral imaging (Cubert S185) and airborne sensors (CASI 1500i, Headwall CFL-005) to provide an integrated, representative value for the FLEX footprint.

This methodology was tested in July 2025 during a field campaign at Doñana National Park. The site features a sharp functional diversity gradient between xerophilic (Monte Blanco) and hygrophilic (Monte Negro) habitats. By applying the optimized sampling strategy, ground-based SIF and reflectance measurements were captured and upscaled to the FLEX pixel resolution using scaling factors derived from leaf area distribution and high-resolution UAV data. This study case demonstrates a statistically sound and operationally viable methodology for validating FLEX products in complex Mediterranean environments.

The FLORA project (FLEX Leaf Observation and Retrieval via Hybrid Approaches) establishes a robust calibration and validation (Cal/Val) framework for ESA’s FLEX mission, with a focus on Level-2C vegetation products, leaf area index, fAPAR, leaf chlorophyll and carotenoid content, and Level-2B Sun-Induced Fluorescence (SIF). FLORA combines hyperspectral and LiDAR observations observed from Unmanned Aerial Vehicles (UAV), ground-based SIF systems (Piccolo-Doppio, Piccolo, FLOX), and intensive leaf-level sampling across four European reference sites: 1) the Scots pine ICOS forest at NL-Loobos, 2) the complementary Speulderbos pine forest, 3) the managed grassland site at NL-Cabauw, and 4) an alfalfa cropping system in Lombardy, Italy. Each site is sampled within a 3 × 3 km window to define statistically robust Elementary Sampling Units (ESUs). Although a 900 × 900 m area corresponds geometrically to the 300 m FLEX footprint, the larger window exploits higher-resolution Sentinel-2, PRISMA and EnMAP data to characterise sub-pixel heterogeneity, delineate ESUs, and enable robust aggregation when upscaling reflectance and SIF to the FLEX scale. TROPOMI and GOME-2C SIF products support atmospheric screening, temporal consistency and inter-sensor compatibility. During the 2026–2027 growing seasons, coordinated UAV campaigns will deploy a Nano-Hyperspec sensor, LiDAR and FluorSpec/FLOX systems to acquire high-resolution reflectance, structural and SIF data around FLEX overpasses. The timing of these airborne campaigns will be dynamically adjusted to align with FLEX mission availability, ensuring optimal temporal matching between reference measurements and satellite observations. These data will be complemented by destructive and proximal sampling of chlorophyll, carotenoids, nitrogen, water content and LAI following harmonised protocols to ensure consistency across sites. A key innovation of FLORA is the development of an open, modular inversion framework combining radiative transfer models with advanced machine-learning algorithms implemented in open-source environments. This system will generate high-resolution maps of biophysical traits and SIF from UAV and satellite data, quantify uncertainties from model structure, spectral degradation and spatial aggregation, and evaluate the sensitivity of FLEX products across forest, grassland and cropland systems. The resulting multiscale datasets and inversion tools will provide a transferable.

Observations of solar induced chlorophyll fluorescence (SIF) offer strong potential to directly assess vegetation photosynthesis from leaf to canopy and up to regional and global scales. SIF signals are comprised of red and far-red emissions released from the chloroplasts as a by-product of photosynthesis. The European Space Agency's (ESA's) Fluorescence Explorer (FLEX, to be launched in the fall of 2026) is the first mission designed to measure SIF in both regions monthly at high spatial resolution (300 m) in tandem with Sentinel 3. FLEX will provide global maps of vegetation reflectance, temperature and red and far-red SIF, to improve the understanding of the way photosynthesis affects the terrestrial carbon and water cycles. This effort facilitates the calibration, validation and interpretation for the USA of the new products FLEX will produce, by meeting the project goals: 1) to characterize the dynamic seasonal relationships between canopy photosynthetic function and vegetation chlorophyll fluorescence and reflectance, as measured continuously for tundra, boreal forest, prairie, deciduous forest, and crops at flux tower sites in the USA at leaf, proximal canopy, and satellite scales; and 2) to advance the methods for modeling, interpretation and applied use of SIF and reflectance products for estimating GPP and timely detection of stress. The research contributes to the development and testing of FLEX SIF algorithms and products for typical vegetation covers in the U.S.A.

Robust automated chlorophyll fluorimeters (i.e., MONITORING-PAM, WALZ) and proximal dual spectrometers (i.e., fluorescence box or FloX, JB-Hyperspectral) are now enabling continuous diurnal and seasonal measurements of leaf chlorophyll fluorescence metrics and canopy SIF, PAR and reflectance for extended periods. Time series of such data are being collected in the USA at large, representative of the ecosystems sites in conjunction with eddy covariance measurements of gross primary productivity (GPP), field measurements of chlorophyll and leaf area index, and airborne and satellite hyperspectral and very high spatial resolution (i.e., Planet SuperDove and World View with red-edge bands) reflectance images. The Soil Canopy Observation of Photochemistry and Energy fluxes model (SCOPE) was used to integrate the collections.

We derived estimates of leaf and canopy traits characterizing canopy chlorophyll, SIF and leaf photosynthetic efficiency (electron transport rate or ETR, photochemical and non-photochemical quenching and yield to photosystem two) diurnally and across the seasons. The variation in vegetation photosynthetic function and the associated canopy traits increased with the advancement of senescence during the fall season. Using proximal measurements, we evaluated the links between chlorophyll fluorescence, reflectance and photosynthetic function at leaf and canopy levels and across the seasons. Using canopy reflectance we derived canopy traits (e.g., Cab, LAI, SIF and GPP) and implemented partial least square regression models (PLSR) for their estimation at local, regional and larger spatial scales. Using the links between leaf chlorophyll fluorescence metrics, canopy reflectance and SIF, currently we are developing approaches for upscaling leaf photosynthetic efficiency to canopy level, which is important for enabling dynamic monitoring of photosynthetic function. This study characterized the dynamics in canopy photosynthetic function, as measured at leaf, proximal canopy and satellite levels; and developed innovative algorithms for estimation of leaf ETR and canopy GPP for tundra, black spruce dominated boreal forest, tulip poplar dominated deciduous forest and grassland prairie in the USA. We simulated photosynthetic efficiency and canopy traits, as anticipated from the forthcoming ESA FLEX and CHIME missions and other current and forthcoming airborne and spaceborne hyperspectral missions (e.g., AVIRIS, EnMAP, PRISMA, Tanger, EMIT and DESIS).

Calibration and validation of the FLEX mission require dedicated scaling approaches in space, time, and viewing angles. To achieve this, we propose integrating a tower-based dual-view hyperspectral system with a hyperspectral drone imaging system. We developed an enhanced RotaPrism system that integrates two hyperspectral sensors with a rotating prism module. The QEPro-CUS spectrometer, optimized for far-red SIF retrieval, operates at 730–786 nm with 0.15 nm FWHM resolution, while the HR2000+ES spectrometer captures full VNIR reflectance measurements across 400–900 nm with 0.44 nm FWHM resolution. The system continuously measures incoming irradiance, radiant exitance, and reflected radiance at two-minute intervals, enabling comprehensive and continuous monitoring of SIF emission and surface reflectance dynamics in both directional and hemispheric views.

To capture the spatial variability of hyperspectral reflectance and SIF, we used a mid-spectral-resolution (6 nm) drone-based hyperspectral imaging system covering 400–900 nm (Headwall Nano). To test whether the system could detect SIF, we conducted modeling experiments with SCOPE, a DCMU treatment experiment with strawberry, and disease detection in a rice paddy landscape. We have been operating the enhanced RotaPrism system and conducting regular hyperspectral drone scans at five flux tower sites in South Korea, including rice paddies, deciduous broadleaf forest, evergreen needleleaf forest, wetland, and mixed forest. These datasets will contribute to the calibration and validation of the FLEX mission.

We report several key results, including continuous hyperspectral and SIF data across ecosystems, relationships between SIF and GPP, and comparisons of surface reflectance between Sentinel-2 and hyperspectral drone data.

Effective validation of satellite-derived solar-induced chlorophyll fluorescence (SIF) products from ESA's Fluorescence Explorer (FLEX) mission demands a well-coordinated network of ground-based reference measurements. While SIF provides a direct proxy for photosynthetic activity and represents an essential observable for understanding ecosystem functioning, existing measurement infrastructures remain fragmented, lacking the standardization required for systematic satellite product validation at global scales.

The International Network of Sun-Induced Chlorophyll Fluorescence (INSIF) addresses this gap by establishing a coordinated framework for globally distributed SIF observations. Drawing on experience from existing Fluorescence Box (FloX) instrument deployments and methodologies developed within the ESA DEFLOX project, INSIF creates a sustainable architecture for delivering continuous, traceable SIF measurements across varied ecosystems and climatic zones, encompassing multiple plant functional types.

The network architecture encompasses several fundamental elements. First, measurement standardization is achieved through the deployment of FloX instrumentation equipped with high-specification spectrometers configured for autonomous SIF monitoring. Second, metrological traceability is maintained through portable calibration systems, enabling consistent inter-comparison between distributed measurement sites. Third, standardized data workflows incorporating rigorous uncertainty analysis ensure consistent data quality across the network. Fourth, a unified database infrastructure facilitates community access through open data-sharing policies.

INSIF extends beyond its primary role in FLEX mission support to serve as an integration platform connecting remote sensing and terrestrial ecology communities. The network generates continuous SIF time series complemented by spectral reflectance and vegetation indices, both linked to plant physiology (e.g., photochemical reflectance index), and vegetation biophysical properties (e.g., normalized difference vegetation index), creating datasets that can be directly compared with eddy covariance flux towers, phenological monitoring programs, and additional ecosystem measurements. This multidisciplinary framework strengthens our capacity to investigate ecosystem dynamics by establishing quantitative relationships between photosynthetic efficiency, carbon exchange, and productivity patterns across temporal and spatial dimensions.

Through systematic provision of validation-quality ground measurements, INSIF directly supports FLEX mission science objectives while simultaneously advancing fundamental ecosystem research through enhanced characterization of SIF temporal dynamics and their mechanistic connections to ecosystem processes. This contribution presents the network conceptual design, describes current deployment progress at operational sites, and discusses early findings from pilot locations, highlighting potential synergies for collaborative research and comprehensive ecosystem observation strategies.

In situ-based spectroscopy is an important component of the validation strategy for the upcoming Florescence Explorer (FLEX) satellite mission. Despite careful sensor calibration and the availability of robust retrieval schemes, data products may contain uncertainties that must be quantified and tracked to facilitate data interpretation and use. In situ spectrometer systems with a spectral sampling design comparable to that of FLEX offer comprehensive options for validating mission products, ranging from irradiance and radiance data to surface reflectance and vegetation information such as sun-induced chlorophyll fluorescence (SIF) and other plant-physiological properties. A particularity of the FLEX mission is that the validation approaches should ideally cover gradients of ecosystem representations, including structurally complex forest ecosystems. However, the acquisition of robust validation data in such structurally complex vegetation ecosystems and the interpretation of these data, particularly given their very high spectral and temporal resolution, is highly challenging and requires particular attention.

This contribution summarizes insights and experiences gained from collecting in situ spectroscopy time series across different vegetation types in Switzerland over several years. The data were acquired for FLEX data validation purposes and to advance the understanding of the inherent SIF information content related to subtle ecosystem processes. We first describe our test sites, which include the several Swiss ecosystems (e.g., a mixed temperate forest, an alpine spruce forest, an agricultural site with crop rotation). We then outline the infrastructure used, including the FloX spectrometer systems and the data processing and storage facility. We discuss aspects that complicate the operation, processing, and interpretation of resulting measurement time series. These factors include instrument degradation, intercalibration requirements, canopy heterogeneity, and illumination/shadow effects. We additionally outline strategies to compensate for the related uncertainties in the acquired data.

Our findings can help to optimize the placement of in situ spectrometers in complex forest canopies, to enable intercalibrated sensor networks, and to define strategies that account for shading and other optical effects. All these aspects are critical to facilitate the acquisition of robust and representative in situ validation data in support of FLEX validation activities.

Reliable Sun-Induced Fluorescence (SIF) retrieval at high spatial and temporal resolution is essential for understanding vegetation photosynthetic dynamics and for preparing FLEX L2B/L2C validation strategies. As part of the pre-operational activities supporting the FLEX Cal/Val campaigns planned for 2026–2027, an intensive airborne experiment was conducted in summer 2025 over potato and maize research sites under multiple nitrogen treatments. The campaign benchmarked the SIF measurement capabilities of two complementary systems: the Headwall Nano-Hyperspec VNIR imaging spectrometer and the FluorSpec ground-based SIF instrument. The experiment was not intended to upscale observations to FLEX resolutions, but to quantify the sensitivity, stability, and consistency of these systems under controlled physiological gradients, informing future ESA-approved FLORA Cal/Val workflows. SIF from Nano-Hyperspec imagery was retrieved using the Fraunhofer Line Discrimination method at the O₂-A band and validated against synchronous FluorSpec measurements. The campaign was supported by coordinated leaf-level observations, including gas exchange, chlorophyll fluorescence, pigment and nitrogen content, pigments, and leaf area index. Overall, the experiment establishes a robust testbed for assessing UAV-based SIF retrieval performance and uncertainties in preparation for FLEX Cal/Val activities.

Keywords: Gross Primary Production, Sentinel-2, Gaussian Processes, Cloud Computing, Eddy Covariance, Solar-Induced Fluorescence.

Accurate estimation of Gross Primary Productivity (GPP) at high spatial resolution is essential for understanding ecosystem carbon dynamics and for linking remote sensing observations to terrestrial photosynthesis. Optical satellite data from Sentinel-2 enable detailed monitoring of vegetation structure and phenology, yet their capacity to resolve GPP variability varies across ecosystems. GPP estimation based solely on Sentinel-2 reflectance is limited, particularly in structurally complex and evergreen canopies, highlighting the need for complementary physiological measurements at high spatial and spectral resolution, such as solar-induced fluorescence (SIF), which the upcoming FLEX mission is designed to provide.

Here, we present a plant functional type (PFT)–specific framework for high-resolution GPP estimation based solely on Sentinel-2 surface reflectance data. Ten dedicated GPP models were developed for major vegetation types using a hybrid approach that combines SCOPE radiative transfer simulations with active learning to construct compact, informative training datasets. The resulting Gaussian Process Regression (GPR) models were evaluated against eddy-covariance GPP measurements from 67 ICOS sites across Europe, with independent temporal validation (2021–2024) and additional spatial testing using AmeriFlux sites in North America. Model performance was also inter-compared with MODIS GPP products.

Results demonstrate that PFT-specific GPR modeling substantially improves GPP retrievals relative to generalized approaches, with strong performance in deciduous forests, savannas, and wetlands. In contrast, evergreen forests exhibit persistent limitations, reflecting the reduced sensitivity of optical reflectance to physiological variability in these canopies. The inclusion of meteorological variables from ERA5-Land generally did not improve model performance. Importantly, the multi-model GPR framework provides consistent epistemic uncertainty estimates, offering insight into where Sentinel-2 reflectance sufficiently constrains GPP and where it does not.

These findings, recently published in De Clerck et al. (2026), establish a robust baseline for future FLEX applications. By explicitly delineating the ecosystems and conditions under which optical reflectance alone can—or cannot—constrain GPP, this work highlights where the direct physiological signal from SIF will be critical. The presented PFT-specific framework, implemented on cloud platforms, provides a scalable reference for integrating FLEX SIF with Sentinel-2 and complementary observations to advance carbon-cycle monitoring across heterogeneous landscapes.

Reference: De Clerck, E., Reyes-Muñoz, P., Prikaziuk, E., D.Kovács, D., and Verrelst, J. (2026). High-spatial-resolution gross primary production estimation from Sentinel-2 reflectance using hybrid Gaussian processes modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 232, 172–195. https://doi.org/10.1016/j.isprsjprs.2025.11.033

The FRM4FLUO (Fiducial Reference Measurements for Fluorescence) project represents a comprehensive initiative aimed at establishing best practices and guidelines for field-based measurements supporting the validation of ESA's Fluorescence Explorer (FLEX) satellite mission products. The project addresses the critical need for standardized, high-quality ground reference data that can be confidently used for satellite product validation across multiple product levels.

The primary objective of FRM4FLUO is to provide the scientific community with robust methodological frameworks for conducting field measurements suitable for validating FLEX products, including L2A reflectance products, L2B solar-induced chlorophyll fluorescence (SIF) retrievals, and L2C higher-level biophysical parameters such as leaf area index (LAI), chlorophyll content, and photosynthetic electron transport rate. The project integrates both theoretical and experimental components to ensure that the proposed guidelines are scientifically sound and practically applicable.

The theoretical framework developed within FRM4FLUO includes formalized uncertainty propagation methodologies, enabling traceable quality assessment of field measurements and their applicability to satellite validation activities. This rigorous approach ensures that measurement uncertainties are properly characterized and can be compared against satellite product requirements.

The experimental component of FRM4FLUO involved two extensive field campaigns conducted in Tuscany, Italy, where a multi-scale measurement approach was implemented and tested. This scale-up strategy encompassed ground-based measurements using automated and portable spectrometer systems, unmanned aerial vehicle (UAV) platforms, and airborne observations, creating a comprehensive dataset that bridges the spatial gap between point measurements and satellite pixel scales. This hierarchical approach allows for investigation of spatial representativeness and upscaling methodologies essential for proper satellite validation.

The data acquisition report documents the campaign activities, measurement protocols, and data quality procedures implemented during these field experiments. Preliminary results demonstrate the feasibility of the multi-scale approach and provide initial insights into spatial variability of reflectance and fluorescence signals across different observation scales. The outcomes from these campaigns directly inform the development of best practice guidelines and highlight practical considerations for future validation activities.

FRM4FLUO contributes to establishing a standardized framework for FLEX product validation, supporting the broader goal of ensuring the scientific quality and reliability of fluorescence remote sensing data for advancing understanding of terrestrial photosynthesis and carbon cycle dynamics.

Recent advances in hyperspectral and microwave remote sensing products, particularly solar-induced chlorophyll fluorescence (SIF) and vegetation optical depth (VOD), provide complementary insights into photosynthetic activity and vegetation water status of forests, offering new opportunities for improved stress detection under climate change. To translate these observations into robust water and carbon flux estimates, mechanistic models are needed that explicitly link remote sensing signals to underlying biological processes. The FLORES research project (funded by BELSPO) addresses this objective by developing an innovative mechanistic approach through integration of multiscale SIF and VOD observations, from leaf physiology to satellite monitoring. Anchored within the ICOS network, the project leverages continuous carbon and water flux measurements, top-of-canopy SIF, and canopy water dynamics from GNSS-T–derived VOD as key calibration and validation datasets for model development and evaluation. UAV-based hyperspectral (including SIF), thermal, and LiDAR sensors will be deployed to bridge leaf-scale processes and satellite observations. FLORES will further exploit FLEX and SMAP mission data to deliver robust estimates of forest carbon and water fluxes across temperate forests, strengthening the scientific basis for sustainable forest management under a changing climate.

Sodankylä in northern Finland is dominated by boreal forests and bogs that are sensitive to ongoing climate-driven environmental change. The research site in Sodankylä hosts a set of chlorophyll fluorescence measurements that provide a multi-scale view of vegetation fluorescence. Over recent years, the site has supported drone-based, tower-based, and needle-level observations. Current measurements include active MONIPAM observations at the needle scale, as well as passive canopy-level SIF measurements using Piccolo in Scots pine forest and FloX in bog environments.

We use these datasets to illustrate different aspects of fluorescence measurements. Drone-based measurements support analyses of atmospheric effects on the SIF signal, while initial comparisons between FloX observations and satellite-derived SIF demonstrate both opportunities and challenges for high-latitude validation. Time series from bog environments reveal ecosystem-specific fluorescence dynamics, and active MONIPAM measurements provide complementary physiological information that supports SIF interpretation.

Together, these observations highlight Sodankylä’s ESA SUPERSITE as a valuable platform for high-latitude fluorescence research and satellite validation, supported by extensive complementary measurements, including eddy covariance data linking fluorescence to ecosystem carbon uptake (GPP).

The Fluorescence Explorer (FLEX) mission operations are built upon a robust and highly adaptable ground segment designed to transform complex hyperspectral measurements into actionable scientific data. The data processing orchestration employed for this task evolved from a mature, operational science-mission framework within the ESA Earth Explorer programme. While missions such as EarthCARE, BIOMASS, and SWARM utilize this same underlying framework, the FLEX implementation is uniquely tailored to accommodate the specific scientific requirements and production models developed by the mission’s science clusters. The framework’s methodology emphasizes a modular approach, allowing for the seamless integration of mission-specific algorithms coded into operational processors. Currently, this processing orchestrator is deployed within the FLEX Reference Platform serving as the primary environment for the integration, verification, and validation (IV&V) of operational processors, ground segment components, and external interfaces during the mission preparation phase. This presentation details the technical implementation within a public cloud infrastructure, highlighting how the framework manages data streams and dependencies between processing levels and the tandem Sentinel-3 mission. Initial results from the preparation phase provide insights into the computational performance and resource consumption of individual production steps. By analyzing the current versions of the pre-operational processors, we demonstrate how the framework handles varying computational loads. The stability, scalability, and flexibility inherited from its operational predecessors prove ideal for the FLEX mission, and will ensure the delivery of high-quality scientific products with the reliability required for satellite-based environmental monitoring of fluorescence.

This study, within the PHY2FLEX project supporting the ESA FLEX mission, focuses on using sun-induced chlorophyll-a fluorescence to assess phytoplankton physiology, estimate chlorophyll-a concentration (Chl-a), and develop algorithms for phytoplankton characterization and satellite calibration/validation.

This algorithmic development needs in-situ data to relate radiometric measurements with the biogeochemical. PHY2FLEX has already accomplished two field campaigns to test the FloX spectrometer, developed to mimic the FLORIS sensor and hence supporting the Cal/Val activities. Two field campaigns were conducted in October 2025 with synchronous EnMAP acquisition: Venice Lagoon and near to the Acqua Alta Oceanographic Tower (7-9 October); Lake Garda (28 October). During the campaigns, radiometric measurements were acquired from the FLoX and with other spectroradiometers widely used for water applications: WISP-3/Orca, SE RS-3500, and ROX. Water samples for subsequent laboratory analysis and inherent optical properties were collected.

FLoX-derived spectra resulted to be useful for the identification of the local maximum reflectance at 685 nm, in agreement with those gathered by the other devices, proving to be an efficient instrument for fluorescence retrieval in waters. Finally, FLoX data were fitting the corresponding measurements derived by EnMAP.

This study, developed within the FLEX-ITA project, supports the FLEX mission by highlighting its potential for aquatic environments, where sun-induced chlorophyll-a (Chl-a) fluorescence (SIF) from phytoplankton serves as a proxy for Chl-a concentration and provides insight into trophic status, algal blooms, and phytoplankton physiology.

This study focuses on dataset quality control and signal-to-noise ratio assessment of airborne sensors as a prerequisite for reliable aquatic SIF retrieval. Two field campaigns over Lake Garda (2-9 July 2024 and 10-11 June 2025) combined airborne hyperspectral data from IBIS and HyPlant with synchronous PRISMA, Sentinel-3 OLCI, and extensive in situ measurements (radiometric, atmospheric, and limnological data).

The work then focuses on the evaluation of the HYPLANT-SFM algorithm, which retrieves SIF using polynomial and Voigt functions spectral fitting within oxygen absorption bands. SIF products were validated against in situ data and analyzed in combination with Chl-a maps derived from the BOMBER bio-optical model. The validation of SIF and Chl-a maps showed ~10% mean deviations, confirming robust retrievals. The spatial patterns and temporal variability in retrieved SIF provide complementary information to Chl-a on phytoplankton physiology and dynamics, supporting FLEX mission preparations and future SIF product validation for inland waters.

The detectability of genetically encoded color signals in plants from space remains an open and fundamental question in plant biotechnology and remote sensing. From a biochemical standpoint, the key challenge lies in achieving sufficient chromoprotein concentration to generate a detectable optical contrast against dominant background signals without affecting plant development and physiology. Here, we present a theoretical layout of our TRANSPARENT project. In this project, we investigate whether chromoproteins can be expressed in cotton (Gossypium hirsutum cv. Coker 312) leaves to be detected across increasing observation scales. Our strategy relies on genetically modifying cotton plants via somatic embryogenesis to stably integrate DNA constructs encoding chromoproteins under strong, leaf-specific promoters, enabling uniform pigment accumulation within the canopy while remaining compatible with plant physiology and growth. In the next steps, we will test whether the signals can be detected across increasing observation scales, starting from a laboratory setup, drones equipped with cameras, and finally satellite imagery. The data analysis we will develop AI models for signal deconvolution at any stage of the observation scales, which are validated experimentally by field work.

VICALOPS is the European Space Agency's (ESA) new service dedicated to monitoring the radiometric performance of satellite sensors. It integrates various vicarious calibration methods that leverage reflectance from bright desert surfaces, snow, deep convective clouds (DCC), atmospheric molecules, and sunglint. The system synthesizes results by combining data from these methods to provide a comprehensive overview of sensor calibration and stability.

The VICALOPS service has been set up to monitor the radiometric performance of the FLEX FLORIS sensor using stable desert sites and deep convective clouds. Seven spectral windows, selected to avoid strong gaseous absorption features, are used to ensure robust and consistent radiometric assessments. Time series derived from these windows enable the detection of radiometric drifts and support the long-term monitoring of sensor stability throughout the mission lifetime.

Sensitivity study to the spectral window choices will be presented.

Solar-induced chlorophyll fluorescence (SIF) is widely used to infer canopy photosynthesis, but its capacity to separate physiological from structural signals is uncertain. We tested whether SIF can disentangle the onset and progression of physiological drought stress from concurrent canopy biochemical/physical changes, and evaluated leaf–canopy links. In a mesocosm, we manipulated water (control vs. drought) in two herbaceous canopies with contrasting leaf-angle distributions (planophile vs. erectophile). We measured active and passive fluorescence at leaf and canopy scales, canopy traits, and used SCOPE to upscale and analyze drivers. Drought progressively reduced soil water, depressed stomatal conductance, and increased non‑photochemical quenching, lowering fluorescence yields at leaf and canopy scales. Top-of-canopy (TOC) SIF responded significantly to drought, but neither TOC nor down‑scaled SIF detected stress earlier than NIRv. Modeling and observations showed strong non‑physiological influences on TOC SIF as drought altered canopy biochemistry and structure. Leaf-scale TOC fluorescence yields were higher and weakly correlated with canopy‑scale yields; upscaling with SCOPE improved agreement. We conclude SIF reliably captures drought onset, but offers limited early‑detection advantage over greenness indices when concurrent canopy structural/biochemical changes are substantial.

Within the landscape of remote sensing signals, the unique selling point of sun-induced chlorophyll fluorescence (SIF) is its sensitivity to the energy splitting in the photosystems. However, the information on the energy splitting cannot simply be read from a SIF value on a 1:1 basis. Difficulties in establishing this link arise from (i) non-linearities in the link between the quantum yields at the photosystem level, (ii) the large within-tree variation in quenching behaviour, (iii) the upscaling from the leaf to the leaf to the landscape scale, where some leaves have a disproportionate effect on the canopy-scale SIF signal, depending on their orientation relative to the sensor. The cumulative effects of these errors might obscure any photosynthetic imprint withing the SIF signal. In this study, we investigate the conditions under which the photosynthetic imprint is still observable. The link between photosynthesis and fluorescence is evaluated at both leaf and landscape scale, for various sites and at a large range of environmental conditions.

To evaluate the strength of the LUE imprint within the φF signal, canopy-scale φF was modelled with a Random Forest in function of LUE alongside with confounding factors affecting the φF signal. The LUE SHAP values turned out more negative under high PAR conditions, with LUE SHAP approximating zero under low-PAR conditions.

Airborne nocturnal hyperspectral measurements at different altitudes were made on 6^th of July 2024 with IBIS and Hyplant sensors over an illuminated greenhouse operated by the company FRI-EL GreenHouse srl. The greenhouse is located in the vicinity of Ostellato (Ferrara, Italy) at 44.71N, 12.71W. It has a surface of 10ha and at the time of the measurements was cultivated with tomatoes in soil-less farming. During nighttime hours Illumination is provided by a large number of LED lamps having a spectrum with two major peaks in the blue and in the red and that does not extend above 700nm. The light travelling from the surface to the top of the atmosphere is the radiance reflected and emitted by the vegetation, and this was measured by two paired high-res spectrometers (IBIS and Hyplant) at 7 altitudes (2200, 3350, 4500, 5600, 6700, 8900 and 11900 ft).

The fluorescence signal from the vegetation was small, due to the relatively low illumination level (PAR< 400umol m-2 sec-1) and to incomplete soil cover but clearly detectable at all flying altitudes. The analysis of the O2A region of the spectrum showed a consistent altitude-dependent deepening of the O2A absorption band that accurately matched the prediction made with Radiative Transfer model.

This presentation highlights the value of night-time measurement for cross calibration of sensors and measurements’ sensitivity analysis.

The FLuorescence EXplorer (FLEX) mission will deliver a global Sun-Induced Fluorescence (SIF) product at 300 m spatial resolution enabling field-scale analysis. However, its low temporal sampling rate limits its utility for time-critical applications. To bridge this gap, we propose the use of a sensor-agnostic SIF retrieval method to combine the FLEX SIF product with estimates from other spaceborne hyperspectral sensors.

In this contribution, we present our ongoing efforts to extend a deep-learning-based retrieval framework (Buffat et al., 2025) to enable sensor-agnostic retrievals in the O₂-A band. The original method has yielded state-of-the-art accuracy for HyPlant and the first DESIS SIF retrievals by combining a small neural network encoder—trained via self-supervised reconstruction—and hyperspectral radiative transfer emulation (presented by Pato et al.). Though promising, the focus on individual-sensor datasets and small networks limits its generalizability.

To overcome this, we introduce a large multi-sensor dataset (HyPlant FLUO/DUAL, DESIS, EnMAP, EMIT, Aviris-NG) to train a dynamic-one-for-all foundation model (Xiong et al., 2024) as a masked autoencoder (MAE) across multiple sensors. We present preliminary results on fine-tuning this model for SIF retrieval in HyPlant and DESIS data and apply it to FLEX FLORIS simulations, assessing the method’s potential for FLEX.

Water availability strongly influences photosynthetic efficiency and crop productivity, making water stress a key challenge for crop monitoring and irrigation management. Chlorophyll fluorescence is a promising signal for the remote sensing of photosynthesis, as it reflects the partitioning of absorbed light energy among photochemical processes, non-photochemical thermal dissipation, and fluorescence emission. However, fluorescence is often interpreted assuming a linear relationship with photosynthetic efficiency, which may be misleading under stress conditions. This study analyses the non-linear dynamics between chlorophyll fluorescence and photosynthetic efficiency in tomato plants subjected to water deficit. Results from three complementary experiments conducted under controlled conditions were integrated to assess how water stress modulates the photosynthetic response, using active chlorophyll fluorescence imaging, hyperspectral spectroscopy, and infrared thermography. The results show that water deficit alters the relationship between fluorescence and photosynthetic efficiency, with non-linear dynamics emerging under specific stress conditions. These findings highlight the need to consider non-linear behaviour when interpreting fluorescence signals and reinforce the potential of chlorophyll fluorescence for early detection of water stress in remote sensing applications.

AndesFlux is a network of four eddy covariance and forest-plot sites situated along a 1200 km north-south transect in the western Amazon. This latitudinal transect closely follows precipitation and dry season length gradients and aligns with the trajectory of the South American Low-Level Jet (SALLJ), which transports moisture from north to south. Our work aims to understand the carbon and water budgets of these forests, with special emphasis on the southwestern Amazon, the least studied region in Amazonia.

This report concerns the Tambopata site (Ameriflux PE-TNR), located in southeastern Peru. We have measured water and carbon eddy-covariance fluxes on a 53 m high tower operating since 2017 in this primary forest within the Tambopata National Reserve. Since March 2025, a FloX field spectrometer (JB Hyperspectral Devices GmbH) / F760/ F687 system has also been operating on the site. Analyses of Net Ecosystem Productivity (NEP) from 2017–2024 reveal that, during the wet season (January-March), the ecosystem is a very small carbon sink, becoming a larger sink during the El Niño phase of ENSO. In the dry season (June-August), the ecosystem becomes an overall source of carbon to the atmosphere. Collected FloX data for April–December 2025 also show a marked seasonality in SIF: lower values for both far-red (F760) and red (F687) fluorescence have been observed during the dry season when compared to the transitional months between seasons (April-May and October-November), correlating well with calculated NEP and evapotranspiration values. Lower SIF values may be related to changes in canopy leaf area index and seasonal leaf shedding. Alternatively, reduced water availability and higher evaporative demand may cause stomatal limitation, which would be reflected in the SIF values. We are currently evaluating the underlying mechanisms of these observations by including leaf-level photosynthesis measurements and a functional characterization of the dynamic acclimation of the plants.

The Doñana site presents a unique biodiversity gradient where xerophilic (Monte Blanco) and hygrophilic (Monte Negro) habitats are abruptly separated by groundwater depth, which is essential for testing the sensitivity of FLEX products to functional diversity and ecosystem stability. In 2025, during the field campaign carried out within the “Spanish FLEX-S3 Mission Calibration and Validation Plan Implementation (SpaFLEXImp)”, different photoprotection measures (fluorescence and pigments, via hyperspectral signatures) were studied in both areas of Doñana as adaptive mechanisms to stressful environmental conditions and as indicators of photosynthetic efficiency and energy dissipation capacity in Mediterranean shrubland plants: Rosmarinus officinalis, Halimium halimifolium, and Phillyrea angustifolia.

Hyperspectral measurements were performed with a spectroradiometer (FieldSpec 3 Hi-Res, Analytical Spectral Devices (ASD) Inc., Boulder, USA) coupled with the FluoWat leaf clip. A spectral unmixing technique was employed to retrieve the fluorescence quantum efficiency and the flux of photons absorbed by beta-Carotene and xanthophyll pigments, key photoprotection mechanisms under stress conditions.

The results show that the increase in photoprotection mechanisms is associated with dynamic regulation of fluorescence and pigments, allowing the safe redistribution of absorbed energy and reduction of photosystem damage, although not equally across all studied species. These processes contribute significantly to stress tolerance and the physiological plasticity of plants in variable environments. The findings highlight chlorophyll fluorescence and photoprotection pigments as key tools for studying adaptive strategies and assessing the physiological status of plants under environmental stress conditions.

Heat dissipation is a crucial photoprotective mechanism that regulates plant energy balance by dissipating excess excitation energy. This process is commonly quantified through non-photochemical quenching of chlorophyll fluorescence (NPQ), which serves as an indicator for physiological status and stress responses. However, while leaf-level NPQ is well-characterized, comparative studies on estimating NPQ from canopy reflectance remain limited.

In this study, we evaluated three distinct approaches for NPQ estimation using data from a field experiment on maize. The dataset consists of canopy reflectance and intensively sampled NPQ observations at the top of the canopy. The three approaches represent physically based, semi-empirical, and data-driven methods, respectively: (1) Model Inversion (MI), which retrieves the photochemical reflectance parameter (C_x) through numerical inversion of the SCOPE model; (2) Vegetation Indices (VIs), including the photochemical reflectance index (PRI), soil-adjusted canopy PRI (SaPRI), and the corrected PRI form (ΔPRI) designed to reduce the influence of leaf pigment composition; (3) Spectral Fitting Method (SFM), which utilizes Principal Component Analysis (PCA) to extract xanthophyll-related reflectance signature, and derives a spectral score by projecting canopy reflectance onto the principal component to quantify NPQ-induced variation.

Our results indicate that the MI method showed the weakest performance, primarily due to the low sensitivity of canopy reflectance to C_x. Among the vegetation indices, PRI showed stable and accurate in NPQ estimation at the diurnal scale, and SaPRI outperformed PRI under low canopy cover. Over longer temporal scale, ΔPRI demonstrated superior accuracy by accounting for leaf pigment pool. The SFM method was slightly less effective than PRI in capturing diurnal variations and required corrections for the constitutive pigment pool to maintain accuracy over longer temporal scales.

This work provides critical insights into the development of NPQ-related products for the upcoming ESA’s Fluorescence Explorer (FLEX) mission, enhancing the capability for monitoring vegetation physiological stress from space.

Sun-induced chlorophyll-a fluorescence (SICF) and its photosynthetic quantum yield (φ_f) are key indicators of aquatic photosynthetic activity and phytoplankton stress. Optical remote sensing, with synoptic and repetitive coverage, is particularly effective for such monitoring when fine spectral measurements are available. The Fluorescence Explorer (FLEX) mission, in synergy with Sentinel-3 optical data, offers strong potential to assess phytoplankton physiological state beyond biomass-based metrics. Here, we present an elastic and inelastic scattering coupled radiative transfer (RT) model forming the basis of a retrieval framework for simultaneous estimation of SICF properties and φ_f, together with bulk optically active constituents including chlorophyll-a concentration ([Chl-a]), detrital absorption (a_dg), and particulate backscattering (b_bp), across diverse aquatic optical conditions. The forward RT model includes a depth-integrated formulation of SICF accounting for vertical re-absorption of fluorescence radiance, reducing fluorescence peak overestimation by 30–50% in productive waters ([Chl-a] > 10 mg m⁻³). Inversion is performed using the SABER (Semi-analytical Bayesian Estimate Retrieval) algorithm, a probabilistic framework applicable to both optically deep and shallow waters. Performance is evaluated using synthetic spectra from RT simulations spanning oligotrophic to hyper-eutrophic conditions, exploiting the complementarity of Sentinel-3 OLCI broad visible bands, which constrain elastic scattering and inherent optical properties, and FLEX ultra-spectral measurements resolving fine SICF features in the red to near-infrared. The combined Sentinel-3–FLEX configuration yields robust retrievals of [Chl-a] and b_bp (biases of +11.20% and −0.11%). Targeted ultra-spectral sampling of fluorescence peaks (~685 nm and ~730 nm) enables accurate φ_f retrieval (bias: +2.03%). In contrast, a_dg retrieval accuracy remains limited (bias: +52.94%), likely due to the spectral gap between Sentinel-3 blue-green bands and the FLEX operational range. Future work will address these limitations through modeling of atmospheric absorption coupled fluorescence, species-dependent optical properties, multi-temporal constraints from Sentinel-3 revisit capability, and regionally tuned detrital priors. Overall, this study demonstrates the potential of FLEX-Sentinel-3 synergy for joint retrieval of aquatic constituents and fluorescence-based physiological parameters.

Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly recognized as a key tool for ecosystem research, providing direct and mechanistic insights into photosynthetic activity across spatial and temporal scales. However, several confounding factors complicate the accurate physiological interpretation of retrieved canopy SIF. A precise understanding of these factors is essential and includes: i) absorbed photosynthetically active radiation by chlorophyll (APAR_chl), ii) complementary radiation pathways such as NPQ, iii) scattering and reabsorption of SIF within the leaf and the canopy, iv) scattering and absorption of SIF in the atmosphere, and v) sensor-related influences on the retrieved SIF. In particular, a comprehensive understanding of the processes of re-absorption and scattering of SIF within the canopy is fundamental for reliably comparing fluorescence observations across different scales and for disentangling the physiological component of SIF detected by RS sensors. In this study, we present the results of a comparative assessment of existing methods for deriving the red and far-red SIF escape probability from the leaf to the canopy scale (σ_F-LC) using a diurnal airborne dataset collected over an agricultural area.

Since the experimental determination of is highly labour-intensive, strongly affected by the measurement techniques and used instruments, and limited to specific illumination conditions, viewing angles, and leaf properties, various approaches have recently been developed making use of TOC reflectance data to correct canopy level SIF measurements for these effects. Re-absorption and scattering of SIF are strongly wavelength dependent. While red SIF (F₆₈₇) is more susceptible to re-absorption due to the overlap with chlorophyll absorption, far-red SIF (F₇₆₀) tends to be more strongly scattered. Several approaches have been proposed to derive surrogates for the product of σ_F-LC and the fraction of APAR_chl (fAPAR_chl). Most of these approaches exploit the similarity between the radiative transfer of intercepted incident light and emitted SIF to approximate σ_F-LC. In this study, we applied four methods to estimate σ_F-LC of F₆₈₇ (σ_F687-LC), namely Redv_Liu, Redv_Wie, CNL and Rom₆₈₇ and seven methods to estimate of σ_F-LC of F₇₆₀ (σ_F760-LC), namely FCVI, NIRv, NIRvH1, NIRvH2, NIRvSR, saR2F and Rom₇₆₀, using HyPlant TOC reflectance data. The resulting estimates were compared with σ_F-LC derived using the hybrid method. Specifically, σ_F-LC simulated with the SCOPE model was combined with Gaussian process regression (GPR) to generate maps of σ_F-LC for both F₆₈₇ and F₇₆₀ (σ_F687-LC SCOPE₆₈₇ and σ_F760-LC SCOPE₇₆₀) from HyPlant TOC reflectance data, which served as the reference dataset for the comparative analysis.

The data used in this study were acquired during the 2019 ESA FLEXSense campaign, a large campaign conducted in preparation for the upcoming ESA Earth Explorer 8 satellite mission FLEX. The investigated area is located in the western part of Germany (50.865228° N, 6.450074° W), 40 km west of Cologne and is composed of 102 agricultural fields. The area is characterized by the cultivation of typical regional crops, such as, winter wheat, sugar beet, potato and maize. Top of canopy (TOC) reflectance and SIF data were recorded by the HyPlant airborne imaging spectrometer on 26 June 2019. The first overflight took place in the morning at 10:45 local time, then data acquisition was continued with the second overflight close to solar noon at 13:40 and the final airborne data set was recorded in the afternoon at 16:15. Each dataset consists of a mosaic created from six flight lines, acquired at an altitude of 680 m above ground level, leading to a special resolution of 1 m.

The comparative analysis of the different approaches used to approximate σ_F760-LC indicates that NIRvH2 and saR2F yield the most consistent estimates relative to those derived from SCOPE₇₆₀. The corresponding scatterplots show a close agreement with the 1:1 line, accompanied by high R² values (> 0.81) and low RMSE (< 0.1), indicating strong overall performance for both methods. In addition, the derived maps of σ_F760-LC derived with SCOPE₇₆₀ and NIRvH2, respectively, based on the midday airborne data set exhibit comparable spatial patterns, with the highest values observed over sugar beet, followed by potato and winter wheat, whereas maize represents a special case due to its early growth stage. In contrast, the methods used to approximate σ_F687-LC show substantial deviations from the SCOPE₆₈₇ reference, as indicated by lower R² (< 0.5) and higher RMSE values (> 0.1).

Overall, the results of this study demonstrate that σ_F760-LC can be estimated reliably using multiple approaches, whereas σ_F687-LC estimates derived from the currently available methods should be interpreted with caution, as they do not show consistent agreement with SCOPE-based σ_F687-LC or with each other. Furthermore, the presented results support the validation of the FLEX σ_F product, which is based on a similar SCOPE-based approach, by identifying which of the tested methods performs best and is therefore most suitable for an independent validation.

SpaFLEX project is implementing a comprehensive Calibration and Validation (Cal/Val) plan for the FLEX-S3 mission. The key pillars of this plan are: Cal/Val test sites network, in-situ (ground, UAV and airborne) instruments characterization, fiducial in-situ measurements, plot spatial characterization and sampling protocols, and uncertainty budgets for Level-2 products

The SpaFLEX Cal/Val strategy is based on spatial characterization of Cal/Val test sites, estimating a number of Elementary Sampling Units (ESUs) and sampling points according to the Cal/Val plot heterogeneity. In-situ FLoX (fixed point and To_Go) and Piccolo (UAV and FluoCat) measurements over Cal/Val plot are carried out following spatial sampling determined. The uncertainty propagation of in-situ fluorescence (SIF) and surface reflectance (Ref) for the 300x300 m area representing a FLEX pixel is performed using the Law of Propagation of Uncertainties and Monte Carlo methods, in successives steps. First, systematic and random uncertainty components of radiance and irradiance are propagated using CoMet-Punpy tool (Community Metrology Toolkit - Propagation of UNcertainties in Python). Then, Monte Carlo simulations of SpecFit algorithm propagate the single FLoX and Piccolo SIF and Ref retrievals. Finally, the upscaled FLEX pixel SIF and Ref retrievals are propagated using Monte Carlo simulations of upscaling transfer function applied. The ultimate goal is to achieve a minimum fluorescence of 2 mW m-2 sr-1 nm-1 (10% uncertainty) and a relative reflectance change of <30% (500-650 nm) over a 300 x 300 FLEX pixel to meet ESA uncertainty requirements for Level 2 products.

This work presents the uncertainty estimation of in-situ SIF and Ref measurements for two Cal/Val plots in the recent Cal/Val campaign carried out in July 2025 in SpaFLEX Cal/Val test site of Doñana (Huelva, Spain) . The two Cal/Val plots are xerophytic shrubland (Monte Blanco) and juniper woodland (Monte Negro). In both plots, Piccolo-FluoCat a FLoX-To_Go measurements were performed over corresponding ESUs and sampling points established. SpaFLEX uncertainty propagation procedure were applied for both setups, in order to evaluate the upscaled SIF and Ref FLEX pixel retrieval over these non-homogeneity Cal/Val Plots.

CO₂ uptake in the Southeast Asian tropical forests is an essential component of the global carbon cycle. Since seasonal changes in canopy structure are subtle in evergreen forests, it is challenging to detect weekly to hourly variation in photosynthetic capacity using remote sensing vegetation indices. Solar-induced chlorophyll fluorescence (SIF) is linked with photosynthetic processes and reflects the short-term dynamics of CO₂ uptake. In the present study, we examined SIF to reveal its contributions from the overstory and understory, as well as the effects of vegetation stresses on efficiency of CO₂ uptake and fluorescence. The study was conducted in the natural tropical evergreen broadleaf forest of Pasoh, lowland Peninsular Malaysia, in 2024. SIF was retrieved at 760 nm and 687 nm wavelengths with the Fraunhofer-line approach on fine spectral resolution radiance data. Data were collected from at 52m and 13m, representing overstory and understory signals. CO₂ flux has been measured with the eddy covariance method. Leaf-scale fluorescence was also measured by the pulse amplitude modulation on two overstory tree species Dipterocarpus sublamellatus and Ptychopyxis caputmedusae at 30 m height. Based on the escape probability approach, the proportion of understory SIF and top-of-canopy SIF were approximately 10% and 20% of total fluorescence at 760 nm, suggesting a substantial role of dense midstory and understory within the vertical profile of the forest. We also found that diurnal patterns were different between overstory and understory SIF in the afternoon, influenced by VPD and light conditions. These results may help us gain a deeper understanding of the fluorescence applications to the carbon cycle and environmental stress in tropical forests.

The SpaFLEX project is implementing a comprehensive Calibration and Validation (Cal/Val) plan for the FLEX-S3 mission. This plan will define Cal/Val test sites, ground, UAV and airborne instruments characterization, fiducial measurements (FRM), sampling protocols, and uncertainty budgets for Level-2 products. In order to achieve in-situ FRM measurements, SpaFLEX establishes a protocol for indoor and outdoor intercomparisons of field spectroradiometers. The aforementioned protocol aims to ensure the reliability of in situ measurements (reflectance, radiance, irradiance), serving as a robust reference for subsequent calibration and validation processes. This work presents the intercomparison experiment carried out in conjunction with the Cal/Val campaign in Doñana (Huelva, Spain) in July 2025. At a set-up with tables and tripods, sets of measurements were recorded for various targets (soil, vegetation) and reference panels with different reflectance properties (90%, 20%) under variable illumination conditions, using an ad hoc circular multi-fiber holder to keep the instrument's radiance (L) fibers close and aligned. The irradiance (E) RCR were placed high above and aligned over the measurement table. Four spectroradiometers with distinct characteristics were employed to data acquisition (ASD FieldSpec3 and Flox). At the final step of the intercomparison protocol, in order to evaluate the consistency, stability, and interoperability of the instruments employed during the campaign the L and E measurements were checked radiometrically and spectrally against LibRadtran radiative transfer code L and E simulations. The results indicate that there are significant differences between the ASD Fieldspec 3 and FLOX measurements. For white reference panel, the irradiance measured by the ASD instruments was substantially higher than that recorded by FLOX, with differences ranging approximately 20%. Similarly, radiance measurements over the gray panel (target) exhibited large discrepancies. In contrast, intra-instrument comparisons revealed greater agreement, with the two FLOX systems recording values with minimal differences around 2% for both radiance and irradiance. Likewise, comparisons between the two ASD instruments showed slightly moderate variability. These intercomparisons highlight the need to employ LibRadtran-based radiative transfer simulations as an independent and physically consistent reference for radiometric and spectral checking for field measurements.

The satellite data products of solar induced fluorescence (SIF) of the TROPOMI mission are of high temporal resolution (daily), while still having a relatively high spatial resolution (3x7 km). Two products have been published so far: TROPOSIF (743-748 nm and 735-748 nm), and CalTech SIF (740 nm). These SIF products are complementary to the coming FLEX data, but also to solar reflectance data. In our study we jointly use solar reflectance and SIF to derive vegetation data products. This approach has similarities with the FLEX Level-2 processor, where hyperspectral reflectance is used together with SIF.

In the Vegetation-CCI project, we use OptiSAIL, a system to simultaneously retrieve soil and vegetation properties from surface reflectance by automatic differentiation of a radiative transfer model, to produce vegetation data products including leaf area index (LAI) from multiple satellite missions combined. A cost function comprising a data and prior component is minimized using a smart selection of available measurements in a moving 10-day window interval. The output also includes diagnostic (forward simulated) data products including the fraction of absorbed photosynthetically active radiation (fAPAR).

We present an extension of this approach with TROPOSIF. We developed a forward simulator for SIF in OptiSAIL, using radiative transfer components of the model SCOPE. The OptiSAIL-SIF inversion outputs the additional vegetation data product of the quantum efficiency of fluorescence (FQE) at TROPOMI spatial resolution, and the diagnostic (forward simulated) product of angularly normalized nadir SIF and SIF in TROPOMI observation geometry at the 1 km resolution of the Vegetation-CCI grid that is smoother in time than TROPOSIF, but otherwise consistent with TROPOSIF.

In our presentation, we demonstrate the algorithm, show results for selected locations, and address the challenges. The challenges include noisiness of the SIF data, the coarser resolution of the SIF data, and computational demands of the inversion.

Validating FLEX-S3 mission products requires statistically robust sampling strategies to ensure spatial representativeness of field measurements at 300 × 300 m pixel resolution. The mission's strict uncertainty requirements (<30% overall, ~10% for solar-induced fluorescence, ~1% for reflectance) necessitate rigorous protocols connecting in situ observations to satellite-scale retrievals. This work presents a comprehensive validation methodology implemented across diverse ecosystem types within the Spanish Cal/Val framework.

The study was conducted at four sites: two holm oak forests in Teruel Province (north-east Spain) — Sarrión (low tree density) and Manzanera (high tree density) — and two in Doñana National Park (south-west Spain): Monteblanco (Mediterranean xerophytic shrubland) and Montenegro (humid shrubland). This ecological diversity provided an ideal testing ground for evaluating methodological adaptability across different spatial heterogeneity regimes.

The first component establishes the statistical framework for determining optimal elementary sampling unit (ESU) numbers (20 × 20 m), integrating finite population correction, spatially weighted k-means clustering with semivariogram-based spatial autocorrelation, and Neyman optimal allocation. Variable selection prioritises proxies exhibiting maximum heterogeneity and minimum spatial clustering using Moran's index and standard deviation analysis.

The second component quantifies within-ESU variability using high-resolution hyperspectral imagery (4 cm, Cubert S185, 125 bands, 450–950 nm). Optimal measurements per ESU are determined by applying finite population sampling formulas to PRI and ChIRE independently as reflectance and fluorescence proxies, considering sensor field-of-view constraints and variable-specific accuracy requirements.

The third component provides multi-resolution upscaling via a hierarchical chain weighted by measurement representativeness. Stratified pixel-level estimation accounts for stratum-specific variability and size, while spatial interpolation addresses incomplete coverage, ensuring consistency and preservation of physically based biophysical variable relationships.

Combined with uncertainty propagation analysis, this framework provides a statistically rigorous, operationally feasible approach adaptable to alternative satellite validation missions requiring similar spatial scaling methodologies.

Scheduled for launch in 2026, the FLuorescence EXplorer (FLEX) mission will deliver unprecedented observations of sun-induced chlorophyll fluorescence (SIF) at 300 × 300 m spatial resolution. Robust calibration and validation (cal/val) strategies are crucial to ensure the accuracy of FLEX products. The Spanish SpaFLEXImp project aims to establish a network of permanent validation sites for FLEX Level-2 reflectance and fluorescence products. This study proposes the Doñana Biological Reserve (RBD) as a cal/val supersite and describes the extensive field activities carried out in 2025.

Doñana offers optimal conditions for satellite validation due to its flat topography, which minimizes terrain effects, low cloud cover frequency, and strong radiometric contrast among land cover types. The site is integrated into international environmental monitoring networks such as eLTER, FLUXNET, and LifeWatch, and hosts eddy covariance flux towers across Mediterranean ecosystems, providing continuous carbon and energy flux measurements essential to assess the relationship between fluorescence and photosynthesis.

Field campaigns in July and September 2025 were conducted. July focused on two contrasting vegetation types: xerophytic shrubland (“Monte Blanco”) and juniper woodland (“Monte Negro”), both instrumented with flux towers. A multi-sensor, multi-scale sampling strategy was implemented following established SpaFLEX protocols. Elementary Sampling Units (ESUs) were defined using Sentinel-2 imagery and UAV-based hyperspectral data (Cubert S185) to characterize spatial heterogeneity within FLEX pixels. Ground observations included continuous fluorescence measurements from tower-mounted FLOX systems, spatial sampling with mobile FLOX instruments, and leaf-level measurements using FluoWat devices coupled with ASD FieldSpec spectroradiometers. A cable-cam system equipped with a Piccolo spectrometer enabled transect measurements and instrument intercomparison. In September focused on airborne sensor validation. Ground reference data were collected using ASD spectroradiometers and UAV-mounted sensors to characterize surface reflectance over vegetation, bare soil and calibration tarpaulins supporting the validation of airborne hyperspectral imagery. The campaigns demonstrate Doñana as a permanent FLEX cal/val site, combining long-term standardized monitoring with the capability to conduct intensive, multi-scale measurements across heterogeneous Mediterranean ecosystems.

Physiological interpretation of vegetation spectral signals under biotic stress remains a key challenge in precision agriculture remote sensing. Pest and virus infections alter the functioning of the photosynthetic apparatus, affecting photochemical efficiency and energy dissipation mechanisms. These changes are often not detected by traditional reflectance-based spectral indices, particularly during early stages of infection. This study evaluates the potential of chlorophyll fluorescence and photoprotective pigment-related traits for early detection of virus-induced stress in Ipomoea batatas. A multi-instrumental leaf-level approach was applied, combining high spectral resolution reflectance and passive fluorescence, active fluorescence measurements, and gas exchange. The relationship between fluorescence parameters and photoprotective pigment estimates was examined in relation to photosynthetic efficiency. Infected plants exhibited reduced photochemical efficiency and increased energy dissipation, as indicated by fluorescence signals. However, structural indices such as NDVI were unable to distinguish between healthy and infected plants. The results demonstrate the value of high-resolution reflectance and chlorophyll fluorescence in the early monitoring of biotic stress.

Airborne imaging spectroscopy systems such as HyPlant provide meter-scale measurements of solar-induced chlorophyll fluorescence (SIF), bridging the gap between ground-based and satellite observations and enabling spatially detailed assessments of forest photosynthesis. However, canopy-level SIF is strongly influenced by illumination, scattering, and reabsorption processes and therefore cannot be directly interpreted as a physiological proxy for photosynthetic performance. This study presents a physically based approach that combines airborne imaging spectroscopy, 3D Discrete Anisotropic Radiative Transfer (DART) modelling, and machine learning to downscale HyPlant top-of-canopy (TOC) SIF radiance to leaf and photosystem levels in deciduous, coniferous, and mixed forest stands.

TOC SIF radiance was retrieved from HyPlant FLUO data using the Spectral Fitting Method extended with a neural network (SFMNN). Forest structure was derived from high-resolution panchromatic imagery and LiDAR data, enabling the delineation of individual tree crowns and the estimation of canopy height. Based on these data, a set of 20 × 20 m virtual forest scenes was constructed in DART, spanning variability in canopy cover and leaf area index, and incorporating 3D representations of beech and spruce trees derived from terrestrial laser scanning point clouds. The DART virtual stands were used to generate a look-up table (LUT) linking HyPlant-like reflectance signals to known structural and leaf biochemical properties. This LUT serves to train neural networks that retrieve tree-level biophysical and biochemical traits from HyPlant hyperspectral reflectance image data. The retrieved traits are subsequently incorporated into DART to parameterize a 3D representation of the entire study area. Forward DART SIF simulations, using the exact illumination conditions and sensor geometry of the HyPlant acquisitions, are then conducted to retrieve spatial maps of photosystem fluorescence quantum efficiency (FQE). In addition, DART radiative budget simulations quantify the fraction of absorbed photosynthetically active radiation (fAPAR) and the canopy SIF escape probability (σ_F), enabling SIF downscaling to the leaf level. For comparison, σ_F is also estimated from reflectance-based optical indices, such as the Fluorescence Correction Vegetation Index (FCVI).

Preliminary results show pronounced spatial variability in σ_F derived from the FCVI–fAPAR approach, with higher values in beech stands and lower values in spruce stands, reflecting contrasting canopy architectures and scattering regimes. By accounting for these structural effects, the TOC-SIF₇₆₀ is downscaled to the leaf level, revealing higher leaf-level SIF₇₆₀ values. Ongoing analysis compares index-based and DART-based estimates of σ_F to assess if spectral indices can adequately represent complex forest canopy APAR and SIF radiative transfer processes. In addition, photosystem’s FQEs retrieved from DART at the individual tree level will be compared with index-based leaf SIF efficiencies, providing a systematic evaluation of their performance as photosynthetic performance indicators. Such a systematic comparison is currently unexplored but critical for robust interpretation of canopy-level SIF observed by airborne and satellite sensors.

The FLEX bottom of atmosphere products surface apparent reflectance and at-surface solar irradiance will be validated using autonomous ground reference data supplied by the LANDHYPERNET and INSIF networks. The validation itself is based on a comparison metric, where the difference between the FLEX products and the reference datasets should be smaller than the combined extended uncertainty of both. When computing the difference between satellite and ground reference data, it is important that the measurands are as similar as possible, i.e. they must be harmonised spatially, spectrally, angularly and temporally.

LANDHYPERNET is an automated network of hyperspectral radiometers, providing multi-angular reflectance measurements for satellite validation. The instrument consists of 2 sensors, covering VNIR to SWIR range. The VNIR data is collected at a wavelength range of 380 – 1000 nm with 0.5 nm sampling and 3 nm resolution, while the SWIR sensor measures in between 1000 – 1680 nm with 3 nm sampling and 10 nm resolution. Reflectance measurements are taken with a field of view (FOV) of 5° and irradiance measurements with a 180° FOV.

INSIF (International Network of Sun Induced Chlorophyll fluorescence) is a network providing processed FLOX (fluorescence box) measurements. It provides measurements from 650 – 800 nm with a 0.17 nm sampling and 0.3 nm resolution and from 400 – 950 nm with 0.65 nm sampling and 1.5 nm resolution. FLOX has a dual FOV allowing it to measure upwelling radiance (FOV: 25°) and downwelling irradiance (FOV: 180°) simultaneously.

The harmonisation approach will require the following steps (fully for reflectance, partially for irradiance): In the spectral harmonisation the dataset with the finer resolution will be convolved to the one with the coarser resolution, i.e. FLEX to HYPERNETS or FLOX to FLEX. For spatial harmonisation, Sentinel-2 data will be analysed to find the least variable, hence best suited region of interest (ROI) and quantify the spatial variability. The angular and temporal correction will be done using a BRDF model and linear interpolation. Each of those steps will introduce additional uncertainties, which will propagated together with the input uncertainties of FLEX, FLOX and HYPERNETS.

In this contribution, we will present the strategy and tools under development to validate the afore mentioned bottom of atmosphere FLEX products starting from ground data using FLEX simulated data.

SIFcam is a novel lightweight dual camera system deployable on uncrewed aerial vehicles (UAVs) for sensing solar-induced chlorophyll fluorescence (SIF) at 760 nm. First introduced in Kneer et al. 2023, it has a potential for scaling SIF observations, specifically for characterizing spatial variation. Unlike previously deployed SIF sensors on UAVs, which are point spectrometers with a wide spectral range but no information on the vegetation structure. SIFcam captures vegetation structural information from images with centimeter spatial resolution. SIFcam has two co-mounted, simultaneously triggered cameras equipped with optical interference filters centered at 760.7 nm and 757.9 nm (1 nm FWHM), enabling SIF retrieval at 760 nm using the standard Fraunhofer line discriminator method. Image pre-processing includes correction for dark current and flat-field effects, conversion of raw digital numbers to radiance, estimation of downwelling radiance using Lambertian reference panels visible in the imagery, and atmospheric correction using the empirical line method.

Since the introduction of SIFcam, the data processing workflow has been refined, and further assessments of data accuracy have been performed. A key challenge is generating robust orthomosaics from overlapping images. To address this, an established photogrammetric workflow was applied, alongside the development and testing of an alternative, customizable processing workflow.

For the first workflow (WF1), the small spatial misalignment between image pairs is corrected within the photogrammetric processing after mosaic generation. In contrast, the second workflow (WF2) applies feature-based image matching and planar transformations to align image pairs prior to mosaicking. During mosaic generation, WF1 blends all overlapping pixels, while WF2 chooses a single pixel value, constrained by quality criteria of the available pixel value for each location e.g. avoiding pixel values influenced by oblique viewing geometries. The final WF2 mosaic is therefore constructed from deliberately selected single-image pixels rather than blended values.

To assess the measurement error and uncertainty of SIF, SIFcam observations were directly compared to (Air)FloX and HyPlant sensors and a systematic assessment of sensor uncertainties was performed on simulated data. During the Fiducial Reference Measurements for Fluorescence (FRM4FLUO) campaign SIFcam was compared to ground-based and airborne FloX and HyPlant observations. In addition, SIFcam observations were explored in a winter wheat field experiment to evaluate their added value for phenotyping, alongside UAV-based multispectral vegetation indices, in distinguishing new and old winter wheat cultivars based on structural and pigment differences. The 3D Discrete Anisotropic Radiative Transfer model (DART) was employed to simulate SIFcam images of different spatial resolutions for a variety of structurally contrasting canopies, varying solar zenith angles and for variations in sensor viewing angle. Analyzing these simulations with known radiance and SIF emissions as well as the signal recorded by SIFcam allows us to pinpoint the most crucial drivers of measurement uncertainty as well as the most suitable parameters for SIFcam image acquisition depending on vegetation canopy structure.

Solar-induced chlorophyll fluorescence (SIF) provides a direct, non-invasive proxy of photosynthetic activity and has become a central observable for the interpretation of vegetation functioning across scales. In the context of the upcoming FLEX mission, there is a strong demand for proximal and airborne reference data that bridge the gap between leaf-level measurements, airborne imaging spectroscopy, and satellite observations. Here, we present the SIFcam, a compact snapshot imaging system designed to measure SIF under natural illumination with high spatial and temporal resolution.

SIFcam is based on a dual-camera architecture equipped with ultra-narrow bandpass filters sampling the oxygen absorption band at 760.7 nm and an adjacent reference band at 757.9 nm (FWHM 1 nm, 90% transmissivity). Camera optics feature a 25 mm lens, resulting in a 29.8° field of view in horizontal and vertical direction. Spatial pixel binning enables sensor integration times suitable for Uncrewed Aerial Vehicle (UAV) applications. This configuration enables quantitative retrieval of emitted SIF at 760 nm, using the standard Fraunhofer line descrimination method, while maintaining centimeter-scale spatial resolution. The system is lightweight (ca. 2 kg), UAV-deployable (154x90x195 mm), and suitable for both mobile campaigns and stationary time-series observations. Previous field experiments have demonstrated robust SIF retrievals and a strong agreement with established ground-based and airborne reference instruments, confirming the system’s capability to capture physiologically meaningful signals.

Floronics, a research-driven start-up, is advancing SIFcam from a validated research prototype towards a scalable, flexible platform over the next two years. Planned developments include further hardware miniaturization, improved functionality such as geotagging and WiFi interface, stable radiometric performance, and a consolidated processing chain for automated SIF mapping. These efforts aim to provide standardized, robust, high-resolution SIF data sets that support FLEX calibration and validation activities, process-based model development, and applications in plant phenotyping and precision agriculture.

Detecting physiological drought stress in forests is crucial for anticipating drought-induced declines in productivity and resilience. However, most operational approaches use broadband vegetation indices (VIs) that only indirectly indicate tree physiological function. Here we evaluate whether a multi-sensor combination of SIF, LST and hyperspectral reflectance–based VIs improves drought-stress detection, leveraging their sensitivity to photochemical regulation, photosynthetic efficiency and stomatal closure–driven reductions in transpiration.

The study is conducted in Pfynwald, Switzerland, a drought-prone ~100-year-old Pinus sylvestris forest with a long-term irrigation manipulation. Airborne data were acquired during three campaigns capturing early and high drought stress conditions. We generate 2 m products from DUAL (hyperspectral reflectance), HyPlant FLUO (hyperspectral fluorescence for SIF retrieval) and TASI (thermal infrared for LST) sensors. Ground measurements acquired close to the flight dates, including soil water potential, tree water deficit, leaf water potential and leaf-level gas exchange, quantify drought responses.

We apply a CNN regression to predict each ground-measured drought proxy from single- and multi-sensor feature sets. Reflectance features include narrowband VIs related to photochemical regulation and plant water status alongside broadband VIs. Top-of-canopy SIF is retrieved using the SFMNN approach and FQE is estimated using the saR2F method. We evaluate performance using pooled out-of-fold R², range-normalized RMSE and Spearman’s ρ under spatially independent 10-fold cross-validation. Using the best-performing models, we aggregate target-specific predictions into a dimensionless ensemble drought index.

Model skill is moderate to high for soil water potential (R² ≈ 0.4–0.7) and lower for tree- and leaf-level variables (R² ≈ 0.1–0.6), consistent with scale mismatches and limited sample size. Multi-sensor models that include FQE and LST outperform single-sensor approaches: FQE most improves predictions of leaf-level gas exchange, whereas LST is important for soil water potential. The ensemble index separates seasonal and treatment-driven drought stress, demonstrating the value of integrating SIF, LST and hyperspectral VIs to move beyond greenness-based drought monitoring toward improved detection of forest physiological stress. This points to new opportunities from the upcoming satellite mission FLEX.

The FLEX mission aims to measure Sun-Induced Fluorescence (SIF) in vegetation, a robust indicator of photosynthetic activity and plant stress. The calibration and validation of FLEX require the use of airborne systems capable of providing estimates of the SIF observable. In order to strengthen its capabilities, INTA has implemented the Headwall Chlorophyll Fluorescence Sensor (CFL) into a remote sensing system designed for manned aerial platforms. The general objective of SpaFLEX project is to develop a Spanish Cal/Val plan for FLEX products. This plan includes the selection of representative test sites, measurement protocols, and uncertainty budgets for Level-2 products. Airborne campaigns provide essential medium-scale data over large areas, with high efficiency, to improve algorithms and models estimating fluorescence and reflectance propagation. Three Cal/Val sites were established across diverse Iberian ecosystems: Doñana (Huelva), La Roda (Albacete), and Sarrión (Teruel), offering variability in vegetation cover, geometry, and environmental conditions.

The hyperspectral acquisition system designed for SpaFLEX was specified by INTA’s Remote Sensing Systems Division and integrated into the platform by the company Heligrafics. This system is based on the implementation of INTA’s CFL, a high–spectral-resolution fluorescence sensor (FWHM, 0.1–0.2 nm) featuring 2160 spectral bands in conjunction with a CASI-1500i VNIR sensor, together with their associated positioning systems. They were installed onboard the Partenavia P68 Observer, which provides a suitable airborne platform due to its high-wing configuration, unobstructed nadir field-of-view and adaptable cabin architecture. The flight operations were conducted over two consecutive days (7–8 October 2025) to ensure full coverage of the three target areas. The study area was subdivided into 7 polygons (~ 8 km²), delineated based on the spatial distribution of in situ instrumentation, vegetation cover and eddy covariance flux towers. Data acquisition was performed in coordination with ground-based sensors (FLOX, Piccolo) and instrumented unmanned aerial vehicles, enabling synchronized measurements and cross-platform consistency among satellite, airborne, and in situ observations. The campaign successfully generated a unique, multi-scale and multi-platform dataset for integrated analysis, that will provide a critical foundation for validating FLEX algorithms and improving Level-2 product quality.

Land cover is an essential variable for monitoring land surface and its change. While the advances in spatial resolution of global land cover maps over the past years have been remarkable, the thematic resolution (number of distinguishable classes) has lagged behind. The upcoming FLEX satellite will provide open hyperspectral data globally. This presents an opportunity to improve the thematic detail of land cover products. FLEX will provide enough detail to be able to discern plant traits and health characteristics, which in turn will allow us to track e.g. ecosystem composition at unprecedented thematic detail. The downside of FLEX is its low spatial resolution compared to satellite sensors like Sentinel-2 (300 m vs 10 m), and a lower temporal resolution (revisit time) compared to other 300 m satellite sensors like Sentinel-3.

The crucial step to increase thematic detail in land cover maps is their extraction from 300 m imagery, where most pixels cover a mix of land cover classes. To retain spatial detail and enable tracking of gradual change, I propose to use land cover fractions for this purpose. Fractions represent each land cover class as percentage area covered, instead of only naming the largest class in an entire pixel. This approach allows intuitively expressing change in area cover over time.

I propose to establish an open-source system that will build on existing land cover mapping initiatives, such as SITS and Iota², but will be oriented towards gradual change mapping and big data processing. To deal with the global scale challenge, it needs to run on demand using a cloud computing platform. ESA’s openEO platform allows upscaling by processing data in European data centers with local access to the satellite data archives, and providing user-friendly on-demand processing. I propose integrating the processing chain in openEO, allowing a combination of multiple sensors, and enabling users to generate land cover maps for their area of interest on demand.

The system would fuse information from multiple satellite sensors to: 1) extract thematic detail from high spectral but low spatial resolution of FLEX; 1) detect changes in land cover and fill gaps based on high temporal resolution sensors, like the daily Sentinel-3 imagery, 3) extract spatial details from low spectral but high spatial resolution sensors, like Sentinel-2, by pixel unmixing; and 4) merge these data streams into a single final product.

Accurate retrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements is particularly valuable for advancing our understanding of photosynthetic processes of plants and for monitoring ecosystem health at the global scale. Many existing satellite-derived SIF products generally rely on specific narrow spectral regions, such as the Solar Fraunhofer lines or oxygen absorption bands. However, the accurate retrieval of the full SIF spectrum spanning approximately from 650 to 800 nm, remains challenging due to complex radiative interactions between the atmosphere and the surface of the Earth.

A major difficulty in satellite-based SIF retrieval arises from the uncertainties in the atmospheric state parameters, such as aerosol optical depth and water vapor content. Inaccurate assumptions in the atmospheric state can introduce substantial biases in SIF estimates from top-of-atmosphere (TOA) level observations when attempting to retrieve SIF outside the specific narrow absorption bands. Therefore, any retrieval algorithm aiming to retrieve the full SIF emission spectrum is dependent on the accuracy achieved at the atmospheric correction step.

To address these challenges, we developed a novel Bayesian SIF retrieval algorithm known as SIFFI. SIFFI is designed to jointly retrieve the full SIF emission spectrum and surface reflectance without imposing parametric constraints on the spectral shape of the retrieved signal. SIFFI has been applied at top-of-canopy (TOC), tower, and TOA levels, enabling its use across a wide range of measurement platforms, from ground-based instruments to different satellite sensors.

At TOC level, SIFFI is successfully applied to measured data from a Fluorescence Box (FloX) instrument. At TOA-level, SIFFI is able to provide accurate retrievals using simulated datasets both under perfect atmospheric correction and when the atmospheric state is inaccurately characterized during the retrieval process. The latter is achieved by utilizing the Approximation Error (AE) method, which aims to marginalize the poorly known auxiliary atmospheric parameters in the forward modeling.

Overall, SIFFI represents a step toward more robust and comprehensive SIF retrievals from hyperspectral satellite observations and contributes to maximizing the scientific return of fluorescence-focused missions such as FLEX. This presentation introduces the SIFFI framework and demonstrates how the AE method can be used to mitigate the impact caused by atmospheric uncertainties on SIF retrievals.

Solar-Induced Fluorescence (SIF) is a critical signal for monitoring vegetation health and photosynthetic activity. Accurately retrieving the full SIF emission spectrum (640–850 nm) remains a major challenge due to the signal’s weakness compared to reflected radiance, generally confining reliable measurements to atmospheric absorption bands, specifically Oxygen-A (O₂A at 760 nm) and Oxygen-B (O₂B at 687 nm). Anticipating missions like ESA’s upcoming FLuorescence EXplorer (FLEX), which targets high-resolution, full-spectrum SIF retrieval, necessitates the development and validation of robust reconstruction methods.

This study compares two distinct methods for retrieving the full SIF spectrum: the established Spectral Fitting Method (SpecFit) and the novel Principal Component Reconstruction (PCR) approach. SpecFit models the SIF spectrum based on high-resolution hyperspectral radiance measurements, which are fitted to a dual Lorentzian function. In contrast, PCR uses Principal Component Analysis (PCA) applied to a large database of simulated SIF spectra (e.g., from the SCOPE radiative transfer model). This technique establishes a linear relationship between the input SIF values at the O₂A and O₂B absorption bands and the first two PCs, which capture over 99.84% of the spectral variability, allowing for computationally efficient reconstruction. Hereafter, we also developed a PCR model for leaf-level SIF using simulated databases (e.g., Fluspect-B) and evaluated it with real data obtained by leaf-clip measurements.

The methods were compared using Top-of-Canopy (TOC) SIF field data acquired by FloX spectrometers, with SIF values reconstructed by PCR and validated against SpecFit outputs. Results demonstrated high compatibility between the two techniques, particularly for integrated SIF emission (SIF total) over the 670–780 nm range, achieving a high correlation (R²=0.981) and an RMSE of 11.5 mWm⁻²sr⁻¹. Spectrally, performance varied, revealing the inherent differences in their modeling approaches. The Far-Red peak region (F₇₄₀) showed good agreement, with correlation values generally above R=0.96 and RMSE less than 0.2 mWm⁻²sr⁻¹nm⁻¹. However, the PCR method struggled more significantly in the Red peak region (F₆₈₅) and the spectral valley (695–735 nm), where correlation dropped to R<0.92 and the Normalized Root Mean Square Error (NRMSE) increased to 0.2–0.45%. This discrepancy is attributed to SpecFit's constrained dual Lorentzian structure, which yields distinct peaks and a pronounced valley, versus the smoother, physically unconstrained profile derived from the RTM-based PCA. Finally, we validated the leaf-level PCR with leaf-clip measured fluorescence, achieving correlation values generally above R=0.93 and RMSE less than 0.35 mWm⁻²sr⁻¹nm⁻¹ in full spectrum.

The PCR method proves highly promising for remote sensing applications, especially as it only requires the readily available O₂A and O₂B SIF retrievals, demonstrating comparability to methods requiring full hyperspectral data. Future work will focus on comparing the PCR and SpecFit results at the leaf-level with real data obtained by leaf-clip measurements.

The representation of gross primary production (GPP) in land surface models remains highly uncertain, despite GPP being a key driving component of the terrestrial carbon cycle (Gier et al., 2024). These uncertainties mainly arise from both the lack of direct measurements of GPP above the leaf scale and an incomplete representation of plant physiological processes (in terms of both parameter values and equations), in particular the links between carbon assimilation and nutrient availability.

Solar-induced chlorophyll fluorescence (SIF) has therefore emerged as a useful proxy of photosynthetic activity and of GPP by terrestrial ecosystems (Li et al., 2018). To further constrain parameters controlling photosynthetic activity, satellite-based SIF observations can be assimilated (e.g., from the TROPOSIF product, and, in the near future, the FLEX fluorescence product), as SIF provides information on plant physiological traits that regulate photosynthetic activity and GPP.

A fluorescence module previously developed for ORCHIDEE (Bacour et al., 2019) enables the simulation and assimilation of SIF observations. The ORCHIDEE-N land surface model now includes an explicit representation of the nitrogen cycle (Vuichard et al., 2019), allowing a more mechanistic description of photosynthesis through nitrogen limitations on key leaf traits controlling GPP, such as chlorophyll and Rubisco contents.

Integrating the fluorescence module into a model that explicitly represents leaf nitrogen limitation is expected to improve the simulation of both SIF and GPP by providing a more realistic description of chlorophyll content and photosynthetic capacity. In this study, an updated fluorescence module is implemented in ORCHIDEE-N to consistently link nitrogen availability, SIF, and photosynthetic activity.

We present a first intercomparison of these two model versions (with and without the nitrogen cycle) based on the seasonal cycles of GPP and SIF at seven observational sites in Europe. These sites are drawn from the AustroSIF database (Martini et al., in prep.), which integrates in situ measurements of eddy-covariance fluxes (used to estimate GPP), SIF, and pulse-amplitude modulated fluorescence measurements.

So far, neither the fluorescence model parameters nor those of the nitrogen-explicit module have been optimised in this new version. This preliminary study paves the way for assimilating both site-level data and satellite-derived SIF retrievals to further constrain the model.

Remotely sensed solar-induced fluorescence (SIF) is increasingly used as a proxy for vegetation photosynthetic activity at various spatial and temporal scales. However, the top-of-the-canopy (TOC) SIF radiance signal is driven by the incoming photosynthetically active radiation (iPAR) and highly impacted by other non-physiological factors, such as canopy structure as well as leaves and soil background optical properties. Methods scaling the canopy SIF radiance down to photosystems’ efficiency are, therefore, essential for extracting physiologically relevant information from TOC SIF signal. Among the existing downscaling methods, 3D radiative transfer-based approaches are uniquely suited for highly heterogeneous vegetation canopies, such as forest stands. Our recently developed method, based on the DART 3D radiative transfer model, was capable to retrieve the spatial and temporal variations of the photosystems’ fluorescence quantum efficiency (FQE) of an alfalfa crop from airborne (HyPlant) and in-situ (FloX) SIF observations. In our current research, we adapted this approach for airborne SIF images of structurally more complex forest canopies, specifically, beech and oak deciduous stands located close to Rajec in Czech Republic. Airborne LiDAR-derived voxelized 3D scenes of the two forest stands are first used to simulate hyperspectral airborne images labeled with per-pixel canopy biophysical and leaf biochemical properties. A combined radiative transfer and deep learning approach is employed by training a regressive 3D convolutional neural network (CNN) on the DART synthetic labeled images and applying it to real airborne images to retrieve per-pixel canopy properties of interest. The retrieved properties, i.e., contents of leaf chlorophyll a+b, carotenoids, and anthocyanin, leaf and wood area indices, and canopy height, are subsequently imported in DART to forward simulate the TOC SIF image. The per-pixel FQE is estimated through matching the simulated far-red SIF radiance with the observed counterpart. The retrieved FQE maps are free of confounding canopy structural impacts, providing information on light-adapted, steady-state chlorophyll fluorescence emissivity from photosystems I and II.

Solar-induced chlorophyll fluorescence (SIF) is an emission from plant leaves that can be observed from space. Chlorophyll fluorescence is one of the pathways through which absorbed radiation is lost from plant leaves. The other pathways are photosynthesis and non-photochemical quenching. Because of its link to photosynthesis, modelling of SIF with terrestrial biosphere models (TBMs) enables making use of this remotely sensed data to better understand the biogeochemical cycles.

Photosynthesis, also known as gross primary production (GPP), can be estimated using eddy covariance flux data. In this study, we explore how to use the remotely sensed SIF centered at flux tower sites to improve the model in terms of modelling SIF and GPP. For this purpose, we use SIF observations from TROPOMI, which is on board the Sentinel-5 Precursor (S5P) satellite. We are using a TBM called QUINCY, which models fully coupled biogeochemical cycles and includes a description of SIF. The SIF formulation has been tested at evergreen coniferous sites and in this study we extend our analysis to additional ecosystems equipped with eddy covariance towers (deciduous broadleaf forest, agricultural site and tree-grass savanna). Our analysis focuses on the seasonal cycle of these ecosystems and on the impact of droughts during the growing season. This work paves the way for leveraging SIF remote sensing observations with TBMs and demonstrates cases where the higher spatial resolution of FLEX will provide additional benefits.

The experimental station “Majadas de Tiétar” (ICOS/FLUXNET ES-LMa) is a well-established, high-profile observatory for monitoring ecosystem processes in an open evergreen Quercus ilex forest, representative of Iberian dehesa tree–grass systems. With a sparse tree density of ~25 trees ha⁻¹, the site constitutes a challenging testbed for studying spatial heterogeneity in carbon, water and energy fluxes. Continuous eddy covariance (EC) flux measurements, high-quality meteorological observations and extensive soil monitoring have been maintained since 2003. Since 2015, additional subcanopy EC systems, sap flow sensors and lysimeters have been deployed to quantify the relative contributions of tree and grass layers to ecosystem-scale CO₂ and H₂O fluxes.

The flux tower and associated infrastructure deliver continuous measurements of turbulent fluxes of energy, evapotranspiration (ET) and CO₂ (NEE, GPP), together with a comprehensive suite of meteorological (air temperature, relative humidity, four-component radiation, PPFD, precipitation) and soil profile variables (temperature, water content, water potential). Complementary point dendrometers, micro-tensiometers and sap flow measurements provide detailed information on tree water status and transpiration at the tree scale.

Building on this unique long-term dataset, Majadas is being upgraded to host new continuous proximal remote sensing observations in synergy with existing measurements, to support the development and validation of upcoming FLEX mission products. New instrumentation includes: (i) a FLOX system to monitor hyperspectral reflectance and sun-induced chlorophyll fluorescence; (ii) SWIR spectrometers for remote sensing of canopy water content; and (iii) a thermal camera coupled to a multispectral camera to resolve spatial patterns of surface temperature and vegetation properties across trees and grassland. All systems are mounted on a mast with a rotating arm to alternately observe oak crowns and adjacent open grass.

The newly measured variables will be integrated into TSEB/3SEB energy-balance models to better characterize hydraulic and physiological constraints on water and energy fluxes and to enhance model performance. Within the SPAFLEX project framework, Majadas will contribute to FLEX cal/val activities by testing upscaling strategies in heterogeneous dehesa ecosystems. Besides, we will produce accessible, high-quality data to foster the development of innovative FLEX-based products by the user community.

Solar-induced fluorescence (SIF) has emerged as a valuable proxy for photosynthesis and shows promise for advancing our understanding of ecosystem dynamics. To harness the full potential of SIF at the satellite level, it is crucial to quantify the temporal and spatial variability at smaller scales within satellite footprints.The Fluorescence Imaging Spectrometer (FLORIS) onboard FLEX will measure between 500-780 nm at a spectral sampling interval of 0.1 nm within the oxygen absorption bands. While the FLEX spatial resolution of 300 meters is a major improvement from past SIF products, there still exists a need to quantify spatial variability and validate SIF measurements from within the footprint.

Recent advances have significantly improved our ability to observe canopy level SIF via highly specialized hyperspectral imagers such as the Headwall SIF imager (Headwall Photonics, Fitchburg, MA, USA). This instrument is an ultra-high resolution imaging spectrometer that measures radiance between 670-780 nm at a spectral sampling interval of 0.02 nm (0.3 nm FWHM), fully resolving both the O2-A and O2-B absorption bands. We deployed the Headwall imager at a grassland in California, USA over 3 growing seasons (2022-2024), generating more than 270 images of SIF, fluorescence yield ɸF, and normalized difference vegetative index (NDVI). Our site is highly heterogeneous within an area roughly the size of the FLEX footprint. To capture the resulting variation in photosynthesis and phenology, we deployed the imager at two distinct regions within our site: one highly-dynamic and seasonally dry area and one wetter area adjacent to a creek. Each region contains vegetation plots that measure soil moisture throughout the rooting zone. We find that seasonal NDVI captures phenological changes within the two regions, with the drier area having consistently higher NDVI and being more seasonally dynamic due to the seasonal lack of soil moisture within the rooting zone. Despite lower NDVI, both SIF and ɸF are consistently higher in the wetter area where water remains within the rooting zone for longer into the dry season. It is clear from images that the imager captures significant spatial variation within a small area. We see through our study that the imager is capable of linking physiology to phenology and canopy structure. Ground-based field studies like this will be essential for advancing the ability to FLEX SIF for ecosystem productivity quantification and stress detection.

The FLuorescence EXplorer (FLEX) mission aims to globally map the Sun-Induced Fluorescence (SIF) spectrum. Accurate SIF retrieval from reflected radiance is achieved by the FLORIS imaging spectrometer using a dedicated global spectrum-fitting algorithm. The Optimal Estimation iteratively optimizes the state vector by integrating at-surface apparent reflectance observations, prior information, and their associated uncertainty covariance.

This work introduces the Error Budget Tool (EBT), a unified diagnostic framework to quantify SIF retrieval sensitivity to multiple error sources and support Level-2 processor refinement. EBT enables controlled simulations from ideal to more realistic conditions, including anisotropy and instrumental or atmospheric uncertainties. It also enables to track key diagnostics such as changes in the Averaging Kernel Matrix, the state vector, cost function and errors in key SIF metrics.

The impact of several factors was systematically evaluated: (i) surface properties, including Lambertian and anisotropic; (ii) instrument modeling, from noise-free to realistic conditions, accounting for absolute radiometric gain (ARG), relative spatial and spectral radiometric accuracy (RXRA, RSRA), photon noise, and spectral calibration errors; (iii) uncertainties in atmospheric parameters such as Aerosol Optical Thickness, Ångström exponent, and Henyey–Greenstein phase function.

Results from anisotropic simulations show that the Lambertian surface assumption in Level-2 SIF processing is inadequate for surfaces with strong bidirectional reflectance. These effects introduce residual O₂ absorption features in apparent reflectance that the Lambertian forward model cannot reproduce, causing SIF retrieval biases. To mitigate this, a simplified BRDF model was implemented in L2B, reducing systematic errors.

The EBT includes a simplified instrumental noise model accounting for systematic and random sources. Current ARG characterization (3.5%) is significant relative to mission requirements (MR). Spectral calibration errors at MR thresholds (±0.005 nm) induce notable SIF errors, especially for far-red and total SIF.

Sensitivity tests on atmospheric parameters revealed that errors in the HG parameter represent the dominant atmospheric driver of SIF retrieval errors.

This work has enabled the identification of the main error sources in SIF retrieval and the development of targeted improvements in the Level-2 SIF processor.

Monitoring declines in forest vitality caused by biotic damage is essential for implementing effective forest protection measures and for understanding ecosystem dynamics. Leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fAPAR) are key functional indicators of forest productivity and structure, directly linking canopy architecture to photosynthetic performance and decline from biotic disturbance. Unlike broad vegetation indices that provide coarse estimates of canopy greenness, LAI and fAPAR are biophysically based variables that are sensitive to changes in light interception and leaf distribution, making them suitable proxies for spatially heterogeneous patterns of forest dynamics and stress. ESA’s upcoming FLEX mission will provide sun-induced chlorophyll fluorescence (SIF) together with LAI and fAPAR as complementary Level-2 vegetation products, enabling joint assessment of structural, functional, and physiological canopy status - albeit at a relatively coarse spatial resolution of 300 m × 300 m. To accurately interpret FLEX signals and relate them to fine-scale canopy dynamics, rigorous validation will be required. Ground-based measurements must be carefully upscaled and cross-compared with FLEX observations and established satellite products to quantify spatial variability, link spectral signals to underlying physiological processes, and assess uncertainties in the retrieval of forest functional traits.

The Hakel Reserve in the northeastern Harz foothills of Saxony-Anhalt, Germany, represents an ideal natural laboratory for studying LAI and fAPAR dynamics. The oak-hornbeam forests, mixed with linden, cherry, and maple, include tree species that have been increasingly affected by biotic stressors such as the oak splendour beetle. The area contains both actively managed stands and strictly protected unmanaged zones, creating strong contrasts in structure and disturbance history.

In this study we will focus on oak-dominated stands. We will implement a multi-sensor, multi-scale monitoring approach to estimate LAI and fAPAR. At the ground level, four 50 m × 50 m plots will be equipped with digital hemispherical photography (DHP) and cosine-corrected PAR sensors beneath the canopy. LAI will be estimated from DHP-derived canopy gap fraction, using a Beer-Lambert-type formulation that accounts for leaf angle distribution and foliage clumping. fAPAR will be directly estimated from measurements of transmitted and incoming PAR, accounting for both direct and diffuse radiation.

Terrestrial measurements will be upscaled using UAV (unmanned aerial vehicle) campaigns conducted during the growing season and timed to coincide with FLEX overflights. RGB imagery supports canopy segmentation and the upscaling of plot-level DHP metrics, while LiDAR provides 3D structure for deriving leaf area distribution and gap fraction. fAPAR will be estimated both empirically from vegetation indices and through approaches that combine multispectral reflectance with LiDAR-derived structural metrics.

These UAV-based products yield spatially explicit LAI and fAPAR maps. These can be aggregated to larger areas and validated against FLEX, Sentinel-2/3, and EnMAP observations to assess consistency and uncertainties across scales. Complementary physiological and soil measurements will be conducted in accordance with FRM4VEG principles, to promote traceability and standardized uncertainty assessment. The methods developed in this project will provide a novel tool for detecting declines in forest productivity and for early detection of forest vitality loss from biotic disturbance.

We developed TECs-SIF, a terrestrial biosphere model (TBM) that explicitly couples canopy radiative transfer and plant photosynthesis to simultaneously simulate solar-induced fluorescence (SIF) and gross primary productivity (GPP). The model integrates a spectral invariant–based radiative transfer scheme across leaf and canopy scales, enabling mechanistic investigation of how the SIF–GPP relationship varies across forest ecosystems and temporal scales.

TECs-SIF was calibrated and evaluated using observations from four AmeriFlux sites spanning evergreen needleleaf and deciduous broadleaf forests (CA-Obs, US-xDJ, US-NR1, and US-UMB). The model reproduced observed SIF and GPP across multiple timescales, with strong performance at both hourly (SIF: R² = 0.48–0.87, RMSE = 0.03–0.12 W m⁻² μm⁻¹ sr⁻¹; GPP: R² = 0.60–0.79, RMSE = 1.82–5.31 μmol m⁻² s⁻¹) and daily resolutions (SIF: R² = 0.64–0.91, RMSE = 0.02–0.09 W m⁻² μm⁻¹ sr⁻¹; GPP: R² = 0.89–0.97, RMSE = 0.51–2.05 μmol m⁻² s⁻¹). The model captured nonlinear SIF–GPP behavior at sub-daily timescales and emergent linear relationships at daily and monthly scales.

Across sites, the simulated SIF–GPP relationship exhibited strong ecosystem dependence, primarily controlled by canopy structural properties (e.g., clumping index) and leaf physiological traits. These results demonstrate the capability of TECs-SIF to mechanistically resolve SIF–GPP coupling and highlight the importance of explicitly representing radiative transfer–physiology interactions in TBMs for improved ecosystem carbon cycle prediction.

Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. A linear forward model is proposed with a proper selection of its parameter settings. The evaluation of the retrieval results is performed by inter-comparison of the SIF peaks with other SIF datasets. The comparisons display similar spatial distributions for the weekly global composites for the first two weeks in June and December of 2024. Especially the comparison of the far-red SIF datasets with another dedicated far-red SIF retrievals, S5P-TROPOMI SIF Data Product (TROPOSIF), demonstrates close agreement, indicating consistency between the two retrieval approaches. The retrieval uncertainty for the weekly global composite is about 10% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. The reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function.

Global terrestrial ecosystems are showing a troubling decline in carbon sequestration—driven by nutrient shortages and intensifying drought stress—that current land-surface models struggle to reproduce. Satellite observations reveal a widening gap between real-world CO₂ uptake and model projections, highlighting deficiencies in how we represent nutrient dynamics, water limitations, and long-term vegetation acclimation. Closing this gap demands an integrated, physics-based framework that fully leverages Earth Observation—from VNIR, SWIR, TIR, and microwave sensors—to trace water–energy–carbon–nutrient interactions across scales.

To this end, we propose SPACEO (Soil-Plant-Atmosphere Continuum enhanced by Earth Observation), a digital twin framework that couples the STEMMUS-SCOPE soil-plant process model with the radiative-transfer models. By simulating signals from VNIR, SWIR, to TIR end-to-end, SPACEO uniquely links satellite measurements to the underlying ecophysiology of the soil-plant system.

SPACEO’s research is organized into three interlocking science cases:

1. Unified Forward Simulation:With STEMMUS-SCOPE, we build a single simulator that coherently predicts multi-frequency EO signals, establishing the scientific foundations for synergy of multi-satellite multi-frequency EO data.

2. Advanced Retrievals & Reference Dataset: Drawing on extensive field campaigns, we will build a FAIR-compliant Reference Dataset and develop data-driven, hybrid, and physics-informed machine-learning algorithms to estimate key ecosystem metrics—Nitrogen Use Efficiency (NUE), Water Use Efficiency (WUE), canopy and soil temperatures, and stress indicators—from optical, fluorescence, thermal, and microwave data.

3. Digital-Twin Data Assimilation: By assimilating multi-mission observations into our digital twin, we will generate self-consistent soil and plant states and fluxes across diverse biomes. We will quantify how multi-frequency data tighten model constraints versus single sensors and translate these insights into design blueprints for next-generation, multi-sensor monitoring systems.

Accurately quantifying ecohydrological processes across scales remains challenging due to uncertainties in model parameters and environmental forcings. Solar-induced chlorophyll fluorescence (SIF), which is tightly linked to photosynthesis, provides new opportunities to constrain carbon–water fluxes using remote sensing.

Here, we develop a process-based inverse modeling framework (STEMMUS-MLR) by coupling a soil water–heat transfer model with a mechanistic light response scheme, explicitly representing root-zone soil moisture dynamics and vertical heterogeneity. By assimilating ground-based or satellite SIF as an input variable, the framework directly constrains ecosystem physiology to jointly estimate gross primary productivity (GPP) and evapotranspiration (ET).

Evaluations across diverse ecosystems and a continental-scale network of AmeriFlux sites demonstrate that STEMMUS-MLR robustly reproduces daily and seasonal GPP and ET dynamics, showing strong consistency with eddy covariance observations across plant functional types. Compared with empirical linear SIF–GPP and SIF-ET relationships, the mechanistic framework improves physical consistency, scalability, and reduces parameter dependency, while enabling reliable ET partitioning.

This work highlights the critical role of integrating SIF and soil moisture constraints into land surface models to reduce uncertainty in coupled water–carbon simulations. It provides a scalable pathway for quantifying ecosystem fluxes from site to regional scales and advances the application of satellite observations for ecohydrological prediction.

MicroCarb is a UK–French bilateral satellite mission designed to map the sources and sinks of atmospheric CO₂ at the global scale. Launched in July 2026, the mission is currently in its calibration and validation phase. Among its data products, MicroCarb will provide a Level 2 solar-induced chlorophyll fluorescence (SIF) product through the central digital processing unit.

The SIF retrieval algorithm, developed by the National Centre for Earth Observation at the University of Leicester (UK), employs an optimal estimation framework to derive global SIF from MicroCarb observations. SIF is a valuable measurement in its own right: as a proxy for photosynthetic activity, it provides key insights into terrestrial carbon uptake and the functioning of the carbon cycle. In addition, SIF plays an important role as an auxiliary constraint in the retrieval of atmospheric CO₂.

We present an overview of the MicroCarb mission and describe the SIF retrieval algorithm and its processing workflow. We also present results from orbit simulation experiments, demonstrating the potential to deliver robust global SIF estimates from space across a range of scenarios, with suitable auxiliary retrieval characteristics.

The FLEX satellite mission was designed and proposed almost 20 years ago. Since then a portfolio of instruments to measure solar-induced fluorescence (SIF) was developed in various scientific studies. This portfolio includes instruments that measure SIF on the level of single leaves, top-of-canopy imaging systems to map the distribution of SIF in natural canopies, UAV-based cameras to map entire fields, tower-based systems to record time series of SIF and reflectance as well as airborne sensors for large scale mapping of SIF. These experimental approaches were applied on a wide range of ecosystems and under various environmental conditions with the goal to furher develop the scientific understanding on the mechanistic link between SIF and the actual state of vegetation photosynthesis and stress response.

With this presentation we will give a critical review on the current knowledge, the uncertainties and knowledge gaps that is based on various experimental campaigns and the activities. We assess the various experimental results and try to provide some answers on how accurately FLEX will be able to quantify actual rates of photosynthesis and vegetation stress, the two main objectives of the mission.

Actual photosynthetic rates and functional stress responses are manifested on the level of single leaves. While we have a good scientific knowledge on the acclimation and adaptation of photosynthesis and the mechanisms of stress response on this small scale, it is a scientific challenge to translate the 300x300 m FLEX pixels to the meachanistic regulation on single leaves. We will review our current knowledge on the dynamic variations that occur on the leaf level, revisit our approaches to correct and normalize for canopy structural effects, compare the variations of SIF across different plant species to finally develop an understanding on the certainties and uncertainties we have during this scaling process.

We will quantitatively describe the spatio-temporal dynamics of fluctuating light and SIF emission within natural canopies, provide an answer, which leaves contribute to the top-of-canopy SIF signal, review the variations that occur under the normal diurnal cycle and those variations, which are introduced by drought, extreme temperatures, pests and diseases, and finally touch the question how SIF emitted from different species within one pixel mixes. We will give some answers on how deeply we possibly can look into the regulatory properties of the photosyntetic machinery from the FLEX satellite plattform.

FLEX data will combine SIF with visible and near-infrared reflectance and thermal information, which in the future can be complemented with ground-based time series and mechanistic understanding of plant functioning. Thus, FLEX data shall be used in combination with other data sources to constrain models of carbon and water fluxes and to predict vegetation health and stress. We will discuss the potential for higher-level data products, which will help to better understand the functioning of our vegetation in times of global change and extreme events.

Sun-induced chlorophyll fluorescence (SIF) holds great promise as a non-invasive, remotely-sensed proxy for tracking photosynthetic activity across spatial scales. The effective translation of canopy SIF into meaningful physiological information, however, requires a deeper mechanistic understanding of the relationship between the quantum yields of fluorescence (ΦF) and photosystem II photochemistry (ΦPSII). This relationship is complicated by two major factors: (1) the confounding effects of canopy structure, which through scattering and reabsorption dictate the escape probability (fesc) of fluorescence photons, and (2) the dynamic, non-linear, and condition-dependent interaction between photochemical and non-photochemical quenching pathways that govern both ΦPSII and ΦF. These challenges are particularly relevant for the red SIF emission peak (F687), which is more directly linked to PSII activity but suffers from strong chlorophyll reabsorption. Consequently, it remains unclear which methods best retrieve the intrinsic photosystem-level signal and how the ΦPSII-ΦF relationship unfolds diurnally and seasonally across species with different functional strategies. This uncertainty complicates the physiological interpretability of snapshot satellite SIF observations, which are sampled at a single, fixed time of day.

In this study, we evaluated contemporary methods for correcting red and far-red SIF for canopy escape probability and investigated the diurnal and seasonal dynamics of the ΦPSII-ΦF relationship under a gradient of environmental stress. We collected 92 days of continuous, concurrent measurements of canopy SIF (at 687 and 760 nm) and pulse-amplitude-modulation (PAM) fluorometry on two temperate tree species with contrasting drought response strategies: the anisohydric European beech (Fagus sylvatica) and the isohydric small-leaved lime (Tilia cordata). Measurements were conducted in the ARBOfun research arboretum in Leipzig, Germany. We compared several reflectance-based approaches to estimate fesc and derived photosystem-level fluorescence yields. Daily conditions were classified into five Hydrometeorological Condition Groups (HCGs) based on vapor pressure deficit, radiation, and soil moisture to analyze stress responses. The predictive power of ΦF for ΦPSII was assessed using both fixed time windows (simulating satellite overpasses) and adaptive, condition-specific integration periods.

We used a correction for the escape probability of SIF based on the Near-Infrared Reflectance of vegetation (NIRv). This NIRv-based correction significantly strengthened the correlation between corrected photosystem-level red fluorescence (F687,PS) and both absorbed photosynthetically active radiation and electron transport rate for both species. We found that the quantum yield of red fluorescence (ΦF687) was a substantially stronger and more reliable predictor of ΦPSII than the far-red signal (ΦF760). The diurnal relationship between ΦPSII and ΦF687 was non-linear and non-monotonic, exhibiting distinct, species-specific trajectories explained by their isohydric (T. cordata) or anisohydric (F. sylvatica) strategies. Our results showed that due to these complex dynamics, seasonal predictions of ΦPSII based on snapshot measurements aligned with satellite overpass times (e.g., 10:30 or 13:30 local solar time) were poor. In contrast, using adaptive, HCG-specific integration windows that captured periods of positive relationship between ΦPSII and ΦF687 significantly improved estimation accuracy and robustness.

These findings underscore the critical importance of accounting for species-specific physiology and diurnal dynamics to accurately interpret SIF data. They highlight the stronger physiological information content of red SIF over far-red SIF and demonstrate that using fixed-time satellite snapshots risks misrepresenting photosynthetic function, especially under stress conditions. For future satellite missions like FLEX, our results call for a prioritization of red SIF retrieval and the development of interpretation frameworks that integrate knowledge of diurnal dynamics and plant functional types.

The FLEX-ITA (FLEX Inland Water and Terrestrial Airborne Measurements and Scientific Exploitation) project, funded by the Italian Space Agency, aims at establishing a network of experts in airborne and ground-based campaigns to support the validation of ESA’s FLEX mission products. While the FLEX-ITA project includes two components, concerning broadly contrasting environments: a "land component" for early detection of crop water stress and an "aquatic component" for characterizing lake phytoplankton, this contribution will present the activities and outcomes of the former.

The “land” experimental component of FLEX-ITA involved two extensive airborne and ground campaigns conducted in 2024 and 2025 in Tuscany, Italy. In 2025, the field campaign was conducted in synergy with the FRM4FLUO project, which allowed the validation of the FLEX-ITA airborne SIF maps (AisaIBIS and Hyplant, Specim, Spectral Imaging Ltd) by comparison with near-surface point measurements from permanently installed FloX systems (JB Hyperspectral Devices GmbH) and spatially distributed observations collected using the UAV-mounted AirFLOX system. Thanks to cooperation with the PRIN 2022 SCOOP project (CUP B53D23018190006), it was possible to study the response of four different species (sudangrass, sunflower, tomato and maize) to evolving water stress, and in some cases, to re-watering. To answer the question whether SIF can reliably detect early crop water stress and/or recovery of the physiological functions upon re-watering, the experimental investigations within the two above-mentioned measurement campaigns, involved a comprehensive set of measurements of plant physiological (leaf photosynthesis measured with CIRAS-4 and LI-6800 Portable Photosynthesis Systems; NPQ derived from MINI-PAM-II fluorometer; leaf water potential measured with Scholander-type pressure chamber), biochemical (leaf chlorophyll content measured with SPAD/Dualex leaf clip sensors) and biophysical (fAPAR - Sunscan, Delta-T) parameters, together with meteorological and soil water conditions.

The FLEX-ITA project results highlight the potential of airborne imaging systems for SIF satellite measurements validation. Moreover, by measuring in-situ the dynamic interplay of the three fundamental dissipation pathways of absorbed light energy in various crop species, FLEX-ITA addresses the open gap in SIF research on the mechanistic link between SIF and photosynthesis, especially under stress conditions, and provides insights regarding the universality of the SIF-GPP relationship.

Chlorophyll fluorescence is a primary signal for monitoring plant photosynthesis through remote sensing. However, the common assumption that the relationship between fluorescence and photosynthetic efficiency is linear often fails under environmental stress conditions. To address this challenge, this work synthesizes four studies—ranging from seasonal field trials to controlled diurnal monitoring—to characterize how nitrogen, water, and thermal stress drive non-linear energy partitioning.

Field experiments on durum wheat revealed that plants grown under high-light conditions exhibited low photosynthetic efficiency (between 0.10 and 0.40). Under these conditions, high activation of non-photochemical quenching (NPQ) dominated the energy dissipation pathway. This decoupled the link between photochemistry and fluorescence, causing standard linear models to fail. To further investigate these drivers, we conducted multi-species experiments on maize, wheat, camelina, tomato, and barley. These revealed a three-phase response—usage, protection, and damage—regulated by the plant's NPQ capacity. Notably, drought accelerates these phase transitions, whereas nitrogen deficiency delays them. This understanding was further refined through continuous diurnal monitoring of tomato plants under water and heatwave stress. The results confirm that water deficit shifts the interaction between fluorescence, NPQ, and photosynthetic efficiency into non-linear patterns. These effects become even more pronounced when drought is combined with thermal stress.

These findings highlight the importance of considering nonlinear dynamics when interpreting fluorescence-based remote sensing data. Moving beyond linear assumptions and exploiting nonlinear dynamics provides a more robust framework for interpreting fluorescence remote sensing data. These findings are essential for developing next-generation stress-detection algorithms for the FLEX mission.

Effective monitoring of drought impacts based on satellite observations can be achieved by combining atmospheric information with vegetation indices (VIs) derived from optical remote sensing. However, VIs generally indicate drought impacts only after vegetation damage has progressed to stages that are often irreversible. Indicators of plant physiological functioning, in contrast, offer the possibility of detecting drought stress at a much earlier stage. Solar-induced chlorophyll fluorescence (SIF), which is emitted directly from the photosynthetic apparatus, provides such physiological information (Drusch et al., 2017). Under abiotic stress conditions, enhanced dissipation of excess energy as heat through non-photochemical quenching (NPQ) leads to a reduction in fluorescence yield, which can be observed remotely as changes in SIF (Berger et al., 2022; Damm et al., 2018).

Satellite observations of top-of-canopy (TOC) SIF have been available globally since 2018 from the TROPOMI sensor aboard Sentinel-5P (Guanter et al., 2021; Köhler et al., 2018). However, TOC SIF is strongly influenced by incoming radiation and canopy structure. These influences must be accounted for in order to derive fluorescence yield at the leaf level, expressed as the quantum efficiency of fluorescence (ΦF), which more directly reflects the actual physiological state of vegetation. In this study, ΦF is calculated following Equation (1), where the vegetation index NIRv (NDVI × NIR) serves as a combined proxy for the fraction of absorbed photosynthetically active radiation (fAPAR) and the fluorescence escape probability (fesc) (Badgley et al., 2017; Dechant et al., 2020; Liu et al., 2023). SIF at 743 nm as well as the reflectance used to compute NIRv are derived from TROPOMI data, while photosynthetically active radiation (PAR) is obtained from MODIS observations.

ΦF = pi*SIF743canopy/(NIRv*PAR) (1)

This study introduces a new multi-year (2018–2023) ΦF dataset for Germany at 0.05° spatial resolution and daily temporal coverage. To evaluate ΦF as an early indicator of drought stress in agricultural and forest ecosystems, it is compared with anomalies in subsurface water storage (SSWS) derived from coupled ParFlow/CLM simulations, which serve as a proxy for plant water availability (Belleflamme et al., 2023). Periods of prolonged negative SSWS anomalies were identified and cross-referenced with watch and warning phases of the Combined Drought Indicator of the European Commission. Cross-correlation coefficients were calculated for multiple time lags using spatially aggregated daily data smoothed with a two-day rolling average.

The highest cross-correlation between ΦF and SSWS anomalies was observed at a two-day lag, with correlations decreasing thereafter, indicating a short delay of 2 days in the ΦF response to declining subsurface water availability in both agricultural and forest ecosystems. In contrast, non-normalized canopy SIF and conventional vegetation indices (NIRv and NDVI) showed weak and inconsistent correlations during drought periods within the response window of 7 days. These results demonstrate that ΦF is sensitive to early reductions in plant water availability and underline the importance of normalizing and downscaling canopy-level SIF for drought monitoring. This approach for deriving ΦF has already been applied to generate a pan-European dataset, which will enable a more comprehensive assessment of spatio-temporal drought dynamics and ecosystem-specific responses in the near future.

Sun-induced fluorescence (SIF) has emerged as a promising tool for assessing photosynthetic activity and plant stress as it directly reflects changes in the regulation of absorbed light energy within the photosynthetic apparatus. Several studies have demonstrated SIF’s ability to detect abiotic stresses, including nutrient deficiencies, drought and temperature, across leaf to canopy scales and using different platforms. In contrast, potential of SIF to detect biotic stress in real field under different environmental conditions like elevated CO₂ remains comparatively underexplored. Elevated CO₂ alters photosynthetic activity and carbon assimilation, potentially modifying plant pathogen interactions and the objective of this study is to assess whether SIF can provide insight into the underlying physiological mechanisms and final yield formation from the interaction of elevated CO₂ and biotic stress.

In this study, we conducted trials in 2024 growing season at experimental fields in Campus Klein-Altendorf, Germany using two sugarbeet genotypes with different susceptibility to cercospora infection (high and low) under elevated CO₂ concentration (600 ppm) and ambient CO₂ concentration using a free air carbon dioxide enrichment (FACE) facility and high throughput platform FieldSnake. Half of the trial was inoculated by the fungus Cercospora beticola which causes Cercospora leaf spot (CLS), and the other half was treated with fungicide to prevent CLS. Two field-based chlorophyll fluorescence methods were employed, an FloX system passively measuring reflectance-based vegetation indices and sun-induced fluorescence (SIF Red and SIF Far-Red) and active Light-Induced Fluorescence Transient (LIFT) device to obtain the operating efficiency of Photosystem II (F_q’/F_m’) both at canopy level. Yield, and quality parameters were also measured to estimate and to understand the interaction of factors for entire crop season.

Seasonal dynamics of SIF shown clear responses to elevated CO₂ and CLS progession. SIF distinguishes CLS symptoms earlier than NDVI and at early infection stages in both varieties whereas reductions in PSII efficiency were mainly observed at later disease stages. Under 600 ppm CO₂, inoculated plots exhibited stronger disease effects compared to ambient CO₂ as indicated by SIF. When accounting for reabsorption and scattering effects, SIF response was driven stronger by structural changes than by physiological responses under CLS. Far-Red SIF exhibited a strong relationship with electron transport rate highlighting its role as a proxy for canopy- scale photosynthesis. Despite disease pressure, elevated CO₂ significantly increased beet and sugar yield, partially compensating for CLS effects in both the varieties. Overall, these results demonstrate complex interactive effects of elevated CO₂ and biotic stress on canopy photosynthesis, structure, and yield formation. These findings provide a strong basis for linking ground-based fluorescence observations with satellite remote sensing and support the interpretation and validation of sun-induced fluorescence products from ESA's FLEX mission.

Estimating gross primary productivity (GPP) from top-of-canopy (TOC) optical measurements remains a major challenge due to the complex interactions among canopy structure, physiological regulation of plant photosynthesis across diurnal and seasonal timescales, and the influence of observation scale. These factors complicate the interpretation of vegetation spectral signals and limit the understanding of remotely sensed reflectance and fluorescence into reliable and direct indicators of photosynthetic function.

This study presents a strategy for transferring spectral modelling approaches previously developed under controlled laboratory conditions to field-scale applications. These methods are designed to capture subtle spectral features associated with non-photochemical quenching (NPQ) processes, which play a central role in the regulation of photosynthesis. The first approach is based on spectral fitting techniques using least-squares regression, combined with targeted refinements of spectral inputs, to quantify the contribution of photoprotective pigments at the leaf level. The second approach employs partial least squares regression (PLSR) applied to hyperspectral TOC laboratory measurements to identify spectral features and wavelength regions correlated with photoprotective mechanisms. To enable application at the ecosystem scale, we investigate short- and long-term spectral variability in relation to GPP and NPQ dynamics, as well as the representativeness of single-point FLOX measurements.

Diurnal variations in canopy reflectance are analysed in relation to concurrent GPP dynamics, with the aim of disentangling short-term physiological responses from longer-term trends. To support this separation, is investigated the influence of canopy structure on the bidirectional reflectance distribution function (BRDF) in TOC FLOX measurements. Spectral analyses employ short temporal-window PLSR models to capture early photosynthetic adjustments associated with subtle pigment dynamics, including rapid physiological responses during recovery phases that temporarily alleviate stress conditions. These short-term approaches are contrasted with seasonally aggregated models that integrate both short- and long-term spectral drivers, resulting in more complex representations of cumulative physiological regulation and structural canopy changes over time. Variable importance in projection (VIP) scores spectra from PLSR models are used to compare dominant spectral contributors across temporal scales and environmental conditions, highlighting the relative importance of specific wavelength regions and sensitivities associated with spectral co-variation with NPQ, and providing insight into how different physiological mechanisms are expressed in the canopy reflectance signal.

Spatial representativeness of point-based FLOX observations is further evaluated by comparing single-tree canopy reflectance dynamics with ecosystem-scale measurements. Further, reflectance relationships are examined between Sentinel-2 reflectance at the pixel scale and ecosystem scale from eddy-covariance tower GPP footprint, as well as between MODIS vegetation products and FLOX-derived vegetation indices, in order to assess cross-scale consistency.

Building on previous findings obtained under laboratory conditions that link changes in photosynthetic efficiency to multi-peak variations in green (500-600 nm) and red-edge (680-750 nm) canopy reflectance, this work further investigates how co-varying spectral behaviour relates to light-use efficiency and NPQ at the canopy level. Although direct temporal correlation between fluorescence signals and traditional vegetation indices is not consistently observed, preliminary modelling results indicate meaningful relationships between spectral dynamics and GPP on the short-term scale. Overall, this study aims to start the development of a GPP modelling framework based on spectral unmixing and the exploitation of co-varying spectral drivers, with potential implications for improving optical monitoring of ecosystem productivity under variable environmental conditions.

Most solar-induced fluorescence models treat Photosystem II in isolation, yet Photosystem I contributes substantially to both fluorescence emission and photosynthetic regulation. This gap limits our mechanistic understanding of the fluorescence-photosynthesis relationship, particularly at ecosystem scales where observational constraints on Photosystem I dynamics remain absent. The Johnson-Berry model addresses this gap by resolving excitation balance, cyclic electron flow, Photosystem I fluorescence, and dynamic absorption cross-section; however, it lacks ecosystem-scale validation with in-situ measurements. Here, we replaced the photosynthesis module in the Breathing Earth System Simulator with the Johnson-Berry model and incorporated a fluorescence radiative transfer module, creating a PSI-explicit framework that simulates both gross primary production and solar-induced fluorescence at the canopy scale. We evaluated this framework at a rice paddy site over 3 months. Our validation targeted both the core Photosystem I-related mechanisms and the accuracy of simulated gross primary production and solar-induced fluorescence. The framework successfully simulated both carbon and fluorescence dynamics, explaining 94% of daily gross primary production variability and 74% of daily solar-induced fluorescence variability, with Photosystem I contributing nearly half of the total fluorescence emission at 760 nm. The simulated quantum yield partitioning among photochemistry, fluorescence, and heat dissipation agreed with field measurements from pulse-amplitude modulated fluorometry. Field observations validated two PSI-explicit mechanisms in the framework: the fractional changes in P680 and P700 states maintained proportional balance, with observed maximum cytochrome b6f turnover rate reaching 210 mol e⁻ mol⁻¹ s⁻¹, supporting the excitation balance assumption; and electron transport measurements revealed cyclic electron flow activation beyond approximately 100 μmol m⁻² s⁻¹. We found that the dynamic absorption cross-section mechanism substantially influenced both non-photochemical quenching and fluorescence simulations: enabling this mechanism produced near-zero Photosystem II non-photochemical quenching, while disabling it led to fluorescence underestimation. Additionally, we found the coupling between Photosystem II reaction center openness and stomatal conductance held only under low vapor pressure deficit. These findings validate key mechanisms in the PSI-explicit framework under field conditions and demonstrate that the framework accurately simulates ecosystem-scale carbon and fluorescence dynamics, providing a mechanistic basis for understanding the relationship between solar-induced fluorescence and gross primary production.

Sun-induced chlorophyll fluorescence (SIF) has been widely used as a proxy for gross primary productivity (GPP) based on their strong linear relationship at large spatiotemporal scales. However, whether this correlation reflects direct physiological coupling or shared dependence on light absorption remains unclear, limiting confidence in SIF-based GPP estimation under environmental stress. Here, we examine the origin of the SIF-GPP correlation by separating contributions of absorbed photosynthetically active radiation by chlorophyll (APAR) as a shared driver, quantum yields of fluorescence (ΦF) and photochemistry (ΦP) regulated through nonphotochemical quenching, and structural versus biochemical components represented by escape fraction (fesc) and electron use efficiency (EUE). For this, we used multi-year, half-hourly observations of canopy-scale SIF and eddy covariance fluxes from five different ecosystem types in Korea including deciduous broadleaf forest, evergreen needleleaf forest, cropland, wetland, and mixed forest. We found that the positive correlation between SIF and GPP became negative after controlling for APAR, and this pattern was consistent across all five ecosystem types. The initially negative relationship exhibited distinct patterns that varied across ecosystem types under different environmental conditions, reflecting trade-offs in energy partitioning between fluorescence emission and carbon assimilation. We also found that the relationship between ΦF and ΦP with canopy conductance differed across ecosystem types. In forest ecosystems, ΦP decreased with declining canopy conductance while ΦF showed little response or even increased, suggesting that stomatal closure primarily limits photosynthetic efficiency rather than fluorescence emission. In contrast, cropland and wetland showed similar responses in both ΦF and ΦP to conductance changes. Our findings indicate that the apparent SIF-GPP relationship arises predominantly from shared APAR absorption rather than from physiological coupling. While this pattern holds consistently across contrasting ecosystem types, the site-specific variation in conductance effects suggests that universal SIF-GPP relationships may not adequately capture carbon uptake across diverse ecosystems, particularly when stomatal regulation limits photosynthesis.

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote-sensing proxy of Gross Primary Production (GPP) in terrestrial ecosystems. However, the estimation of GPP using SIF is challenging when plants experience stress, particularly during extreme climatic events whose frequency is projected to increase in the future. Recently, the feasibility of canopy-level active chlorophyll fluorescence measurements (LED-Induced chlorophyll Fluorescence (LIF)), which directly measure the apparent fluorescence yield (F_yieldLIF), has provided new perspectives on detecting the responses of plants to abiotic stress. This study was conducted during the summer 2022 European heat waves in a mixed temperate deciduous broadleaf forest, located in the French Fontainebleau-Barbeau station. Continuous measurements of carbon dioxide (CO₂) and energy exchanges, SIF, F_yieldLIF, and ancillary environmental variables were acquired. We investigated how high atmospheric dryness induced by heat-waves, measured as Vapor Pressure Deficit (VPD), affected canopy chlorophyll fluorescence (both SIF and F_yieldLIF) and GPP, as well as their relationships. At the half-hourly scale, our results revealed a decrease of the correlation between SIF and GPP (R² decreased from 0.49 to 0.17) at high atmospheric dryness. In contrast, the correlation between F_yieldLIF and GPP increased significantly under high atmospheric dryness (R² increased from 0.07 to 0.43). However, at the daily scale, the correlations between SIF and GPP and between F_yieldLIF and GPP showed an overall increase compared to the half-hourly scale, suggesting a time-scale-dependent response of these relationships to atmospheric dryness. Our tiered analysis further demonstrated that F_yieldLIF provides a significantly more robust proxy for the maximum photosynthetic rate (Amax) than SIF normalized by Photosynthetically Active Radiation (PAR), SIFy, under atmospheric stress. Specifically, under clear sky and high VPD conditions, the R² between Amax and F_yieldLIF reached 0.85, a substantial improvement compared to the R² of 0.56 observed for Amax and SIFy. This enhanced relationship is attributed to the advantage of F_yieldLIF, which, due to its stable excitation source and fixed geometry, is directly proportional to the true chlorophyll fluorescence quantum yield (), thereby directly capturing physiological regulation such as Non-Photochemical Quenching (NPQ). In contrast, SIFy remains confounded by structural and radiative transfer components, including the fraction of emitted SIF that escapes from the canopy (f_esc) and fraction of absorbed PAR by chlorophyll (f_APARchl), whose variability weakens its correlation with Amax. This study highlighted F_yieldLIF's advantage in detecting plant responses to high atmospheric dryness. We concluded that integrating canopy-level active fluorescence (like F_yieldLIF) with passive SIF measurements is essential for the accurate mechanistic interpretation and physiological validation of SIF signals, especially under future, more frequent extreme climate events.

Recent advances in passive remote sensing of solar-induced chlorophyll fluorescence (SIF) have spurred the development of SIF modules within terrestrial biosphere models (TBMs), creating a new generation of TBMs that explicitly simulate fluorescence emissions. The integration is motivated by the mechanistic link between fluorescence and photosynthesis, enabling SIF observations from tower, airborne, and satellite platforms to directly constrain photosynthetic carbon assimilation—quantified as gross primary productivity (GPP) at the ecosystem scale—and related ecosystem processes. However, substantial discrepancies in SIF simulations remain across models. The SIF Model Intercomparison Project (SIF-MIP) was designed to systematically quantify these differences by comparing tower-based SIF observations with simulations from six TBMs (BEPS, CLIMA, CLM, JULES, ORCHIDEE, and TECs) across diurnal, seasonal, and interannual timescales. Large inter-model variability was identified, with coefficients of variation (CVs) of 0.360–0.684 for absorbed photosynthetically active radiation (APAR), 0.559–1.099 for GPP, and 0.603–0.891 for SIF. Significant inconsistencies also emerged in key ecological relationships, including the GPP–SIF relationship, the coupling between GPP yield (GPP/APAR) and SIF yield (SIF/APAR), and the link between fluorescence efficiency (ϕF) and photochemical efficiency (ϕP). Assimilating satellite-based SIF into TBMs substantially improved model performance, reducing root mean square errors (RMSEs) for GPP by 30–50% and for SIF by more than 50%. These findings highlight the urgent need to improve TBM-SIF parameterizations from the leaf to canopy scale, and to better exploit satellite SIF as a model constraint—both of which are critical for improving projections of terrestrial carbon dynamics under ongoing climate change.

Investigations into the role of solar-induced fluorescence (SIF) as an indicator of photosynthesis and vegetation stress across multiple spatiotemporal scales have gained momentum in recent years, particularly in preparation for the upcoming FLEX mission. In this context, the exploitation of complementary information from the Sentinel-3 (S3) mission—flying in tandem with FLEX—is crucial for the correct interpretation of SIF dynamics. Here, we investigate the synergy between S3-derived vegetation products (S3VPs) and SIF derived from TROPOMI (TROPOSIF) as a baseline framework for the future exploitation of FLEX observations. Our objective is to assess the global capacity of SIF, in combination with S3VPs, to predict photosynthetic activity as quantified by gross primary productivity (GPP), and to explore the extent to which environmentally driven GPP dynamics are embedded within SIF and S3VPs.

We first analyze the dominant environmental drivers of GPP dynamics across major global biomes. Second, we quantify Pearson correlations (i) between an eddy-covariance-based GPP product and both TROPOSIF and S3VPs, to assess their predictive power for GPP estimation, and (ii) between TROPOSIF/S3VPs and key meteorological variables, to evaluate their role as proxies for environmental drivers in GPP prediction. We then intercompare two global GPP products derived using the same machine-learning framework (Gaussian Process Regression): one driven by satellite observations (TROPOSIF and S3VPs), hereafter referred to as the hybrid SCOPE-GPR-GPP model (1Reyes-Muñoz et al., 2024), and another driven by meteorological inputs, referred to as the data-driven EC-GPR-GPP model (2Reyes-Muñoz et al., 2025). Finally, we assess the consistency and robustness of GPP estimates derived from TROPOSIF and S3VPs at a spatial resolution of up to 300 m by analyzing predictive uncertainties across multiple variable configurations.

Results indicate that the dominant drivers of GPP in the EC-GPR-GPP model are: leaf area index (LAI), latent heat flux, incoming shortwave radiation, and soil and air temperature. In contrast, LAI, fraction of absorbed photosynthetically active radiation (FAPAR), and SIF emerge as the primary predictors in the SCOPE-GPR-GPP model. The two GPP products show strong global agreement, suggesting that SIF and FAPAR can effectively proxy a comprehensive set of meteorological drivers for GPP prediction. Furthermore, global correlation maps reveal predominantly positive Pearson correlations when each variable (SIF, LAI, and FAPAR) is individually correlated with GPP, yielding modal correlation coefficients of approximately 0.85 for SIF–GPP, 0.90 for LAI–GPP, and 0.87 for FAPAR–GPP. Regions exhibiting weak or negative correlations are primarily associated with arid ecosystems (e.g., central Australia) or complex tropical environments (e.g., the Amazon basin). These patterns suggest that: (i) limiting conditions such as drought stress or light saturation may decouple SIF emissions from carbon assimilation, highlighting SIF’s role as an energy-regulation mechanism, and (ii) tropical ecosystems exhibit subtle and rapid dynamics that may fall within the uncertainty bounds of current models, leading to weak correlations.

We conclude that canopy-leaving SIF is a robust global predictor of GPP dynamics and that TROPOSIF, in combination with S3-derived vegetation products, can effectively serve as proxies for a full suite of environmental drivers. Future work will benefit from improved spatiotemporal resolution and is expected to further advance once the FLEX–Sentinel-3 tandem mission becomes operational.

1Reyes-Muñoz, Pablo, et al. "Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models." Remote Sensing of Environment 305 (2024): 114072.

2Reyes-Muñoz, Pablo, Dávid D.Kovács, and Jochem Verrelst. "Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data." Ecological Indicators 177 (2025): 113597.

Understanding how drought impacts forest photosynthesis is critical for predicting carbon cycle responses to climate change. Solar-induced fluorescence (SIF) has emerged as a promising remote sensing tool for monitoring photosynthetic activity at ecosystem scales, yet the mechanistic relationship between SIF signals and photosynthetic processes under drought stress remains poorly understood, particularly for coniferous forests. In this study we aimed to investigate how tower-based continuous measurements of canopy SIF relate to photosynthetic performance in drought-stressed and watered Scots pine (Pinus sylvestris) canopies over an 18-month period. For this purpose we measured canopy SIF using two JB Hyperspectral FloXbox systems at the Pfynwald long-term drought manipulation experiment in Switzerland, with one system monitoring an irrigated plot and another monitoring a non-irrigated control plot experiencing natural drought conditions. From July 2024 to November 2025, we continuously measured canopy-level SIF at both the red (SIF A, 685 nm) and far-red (SIF B, 740 nm) wavelengths, alongside leaf spectral reflectance measurements. The tower-based observations were complemented by monthly ground measurements including photosynthetic gas exchange (assimilation rates and stomatal conductance), pulse-amplitude modulated (PAM) chlorophyll fluorescence to characterize photosynthetic energy partitioning, and biochemical analyses of needle chlorophyll, carotenoid, and xanthophyll cycle pigment content and composition. Preliminary findings reveal that both SIF A and SIF B successfully captured the seasonal dynamics of canopy photosynthesis, showing characteristic peaks during summer months and pronounced downregulation during winter. Vegetation indices derived from spectral reflectance data, including the near-infrared reflectance of vegetation (NIRv), photochemical reflectance index (PRI), and chlorophyll carotenoid index (CCI), exhibited consistent trends with SIF, supporting their utility as complementary proxies for gross primary productivity and photosynthetic phenology. Our analysis revealed two key findings. First, we detected a consistent increase in SIF A in the irrigated plot compared to the non-irrigated control during the dry summer months, providing clear evidence that SIF A is sensitive to drought-induced reductions in photosynthetic activity. This drought signal was particularly pronounced during periods of high vapor pressure deficit when stomatal limitations on photosynthesis are expected to be most severe. In contrast, SIF B showed limited sensitivity to drought stress, with detectable differences between treatments restricted to a brief period during summer 2025 when temperatures peaked and vapor pressure deficit reached maximum values during the observation period. Secondly, we identified a strong correspondence between temporal variation in SIF A and NIRv throughout the measurement campaign. This relationship suggests that structural changes in canopy greenness and photosynthetic capacity, as captured by NIRv, are reflected in the SIF A signal, supporting the mechanistic link between remotely sensed fluorescence and actual photosynthetic function. Next steps in our analyses will integrate leaf-level physiological and biochemical measurements to validate and mechanistically explain the observed drought responses in canopy SIF in drought stressed Scots pine.

Our current best estimate of carbon sinks under changing climate forcing comes from Earth System Model (ESM) projections, which are highly uncertain. The carbon science community has developed collaborative responses to reducing uncertainty such as the International Land Model Benchmarking (ILAMB) project, which has developed a large body of data to benchmark model projections and parameterizations. While informative, existing ILAMB datasets generally do not provide a strong constraint and leave interpretive ambiguity, do not adequately sample the heterogeneity of processes, or are limited by the proxy nature of the observations. Only Earth Observation data from remote sensing and ground-based networks combined with models can bridge the scale gap between the heterogeneity of the biosphere and global greenhouse gases. Innovative data assimilation and data science techniques can help assess consistency among multiscale observations and models, to reduce uncertainty in predictions. The CARDAMOM model-data fusion (MDF) system for the terrestrial carbon cycle combines vegetation, carbon, and water remote sensing observations with coupled carbon-water cycle processes at multiple scales needed to understand and constrain the heterogeneity of the carbon cycle and carbon-climate feedbacks with sufficient accuracy to confidently reduce uncertainty in ESM predictions. This work presents CARDAMOM as a first-of-its-kind multi-decadal terrestrial reanalysis of carbon, water, and energy fluxes, and their uncertainty, constrained by space-based observing assets, offering a self-consistent benchmarking framework for current and next generation ESMs.

Inland waters provide many important ecosystem services, yet they are under pressure due to ongoing environmental change. Monitoring these aquatic ecosystems is an essential step in understanding their response to environmental pressures and in developing management and conservation strategies. The complex optical properties of inland waters, as well as their high spatial and temporal variation, present challenges for monitoring attempts. In addition to existing satellite missions, which only partly meet the observational requirements for inland waters, the upcoming Fluorescence Explorer (FLEX) imaging spectroscopy mission complements observational capacity to eventually advance aquatic research.

This contribution outlines some recent studies based on in situ measurements in Swiss lakes and demonstrates how FLEX like data can be exploited for aquatic research. We first describe a novel approach to estimate non-photochemical quenching (NPQ) from in situ measured vertical profiles of sun-induced chlorophyll fluorescence (SIF) and chlorophyll-a (CHL-a). We then demonstrate how NPQ impacts estimates of CHL-a from SIF retrievals. We also demonstrate the capacity of hyperspectral data to discriminate phytoplankton species and track seasonal phytiolankton blooms. We discuss insights gained from the above studies related to possible application fields of FLEX mission data, e.g. for estimates of phytoplankton productivity, harmful algae bloom detection, or phytoplankton diversity assessments.

To understand marine ecosystems, assessing phytoplankton physiology and species is critical, as they affect light conversion and primary production. Satellite optical missions traditionally monitor aquatic systems and phytoplankton biomass using multispectral imaging. FLEX's FLORIS instrument will measure Top-of-Atmosphere light with the high spectral resolution (~0.3 nm) needed to isolate fluorescence. Integrating FLEX with Sentinel-3 will advance global aquatic monitoring, but requires novel radiative models and algorithms, particularly utilizing O2 absorption bands, to accurately separate fluorescence.

The FLEX satellite, with moderate spatial (300m) and very high hyperspectral resolution (~0.3 nm, 500–780 nm), will orbit in tandem with Sentinel-3 for crucial atmospheric correction and complementary spectral coverage. Algorithm development and water parameter retrieval require radiative transfer simulations mimicking this synergy. Water-leaving radiance depends on optically active constituents and their optical properties, including inelastic scattering (Raman, fluorescence). To reduce cost, we propose a parameterized look-up table model that accelerates very-high spectral resolution (0.1 nm) computations, incorporating elastic and inelastic scattering to determine radiance/reflectance at the bottom and top of the atmosphere, which is then convolved with FLEX (FLORIS) and Sentinel-3 (OLCI/SLSTR) spectral responses.

A preliminary study identified global priority areas for FLEX based on radiometric/spectral needs and maximizing scientific return, including Marine Protected Areas, aerosol deposition impact, and historical data analyses (Chlorophyll-a, Rrs at 665 nm, clear-sky availability). Seasonal priority rankings were computed, and radiative transfer simulations were tailored to these areas. Simulations are validated using hyperspectral data (PRISMA, EnMAP, PACE) at the top and bottom of the atmosphere, supplemented by field hyperspectral measurements, including HYPERNETS and our own acquisitions from radiometers like FLOX (matching FLORIS's spectral characteristics). This study investigates phytoplankton hyperspectral signatures based on synthetic or actual measurements mimicking the future Sentinel-3/FLEX synergy data. Based on these simulations and in situ data, we present the potential of the FLEX mission for hyperspectral remote sensing of aquatic environments and phytoplanktonic ecosystems.

The AquaValiX consortium brings together scientists from four German research institutes with long-standing expertise in hyperspectral remote sensing of aquatic environments and validation of satellite missions. Its aim is to validate and extend products from the ESA Fluorescence Explorer (FLEX) for inland, coastal and oceanic waters. Validation plans include (1) direct comparison of satellite‑derived reflectance with simultaneous in‑situ measurements; (2) indirect validation through inversion of radiative‑transfer models; and (3) inter‑comparison of FLEX outputs with products from Sentinel‑3 OLCI, Sentinel‑2 MSI, PACE and EnMAP. Over the next years, coordinated field campaigns are planned in Germany, other parts of Europe, the Canadian Arctic and New Zealand. Measurements will cover high-resolution spectral absorption, scattering and water-leaving reflectance, as well as phytoplankton pigment composition and inelastic scattering processes (fluorescence by chlorophyll, phycobilin, and yellow substance). These observations, complemented by drone surveys, laboratory analyses and biogeo-optical modelling, will be integrated with solar radiative-transfer simulations (SCIATRAN, WASI, Hydrolight) to provide optical closure between water constituents, their inherent optical properties and the satellite signal. By characterizing phytoplankton fluorescence and pigment signatures, the project will assess the added value of FLEX +OLCI‑3 synergy and the development of new products such as harmful‑algal‑bloom detection and phenology monitoring. The work plan is organised into four inter‑linked work packages: WP 1 ensures joint campaign coordination, data quality control and FAIR data release. WP 2 generates a public, high‑quality in‑situ reference dataset from lakes (e.g., Lake Constance) and coastal/polar waters using hyperspectral radiometers, UAV imaging, autonomous surface vehicles and comprehensive laboratory analyses. WP 3 focuses on the adaption of radiative‑transfer models to retrieve sun‑induced chlorophyll fluorescence (SIF) at 687 nm and 760 nm and the production of prototype Level‑2B products that can be upscaled with Sentinel‑2 MSI and Sentinel‑3 OLCI imagery. Finally, in WP 4, we will work towards an implementation roadmap for a permanent hyperspectral calibration/validation station at a reference lake, defining technical specifications, maintenance procedures and open-data governance.

In this contribution, we also describe the spectral features that only become visible through high-resolution spectral sensors (in situ and from space). In addition to fluorescence effects, this refers primarily to phytoplankton communities and the effects of pigments, colour, shape and size distribution of algae.

Understanding the maximum photosynthetic capacity of terrestrial plants is central to the FLEX mission because this key variable mediates the relationship between the environmental drivers of photosynthesis, the rate of gross carbon dioxide fixation, and the emission of chlorophyll fluorescence. Recent advances in experimental studies have led to the development of an invertible leaf-level model of photosynthesis that resolves two of the proteins that are key determinants of maximum photosynthetic capacity: the Cytochrome b6f complex, which controls the activity of the electron transport system, and Rubisco, which controls the activity of carbon metabolism (DOI: 10.1007/s11120-021-00840-4). In this presentation, I will discuss progress implementing this leaf-level photosynthesis model within different styles of land surface modeling frameworks, and strategies for performing inversions that are driven by fluorescence observations to retrieve estimates of maximum photosynthetic capacity. I will also discuss upcoming opportunities for collaboration to calibrate and validate FLEX-derived estimates of maximum photosynthetic capacity using in situ measurements of the photosynthetic proteins across key plant functional types. Finally, I will discuss how these activities lay the groundwork for future development of a novel FLEX data product that quantifies leaf Cytochrome b6f and Rubisco contents in a way that is complementary to the already planned products for leaf chlorophyll and carotenoid contents, and some examples of potential applications to understanding the large-scale responses of terrestrial vegetation to global environmental change.

The effects of climate warming in the Arctic are two-to-four times more prominent than at lower latitudes, thus extending the growing season and possibly increasing the vegetation productivity. Consequently, an increase in emissions of Biogenic Volatile Organic Compounds (BVOCs) is expected. BVOCs can be emitted by vegetation for different purposes, including pollinator attraction, defense, plant-to-plant communication, and as a response to biotic and abiotic stress. Specific groups of BVOC are emitted with a clear ecological function, whilst others have an uncertain purpose. Generally, the total BVOCs emissions are regulated by biomass, temperature, soil moisture and received radiation.

Isoprenoids, a group of BVOCs, are known to be driven by photosynthetic processes. For example, isoprene is synthesized during photosynthesis and protects plants against e.g. heat and oxidative stress. Our hypothesis is then that the emissions of selected BVOCs groups (in particular isoprenoids) can be linked to remotely sensed proxies of photosynthetic activity.

In this study, we propose to explore the link between Solar-Induced chlorophyll Fluorescence (SIF) in the O₂-A absorption band and BVOCs emission rates in a field spectroscopy framework (using a FOX-2 spectrometer, JB Hyperspectral Devices GmbH), so to further investigate the relationship between the reflectance properties of vegetation and emitted BVOC rates. The vegetation under examination are typical species found in low Arctic tundra: Empetrum nigrum, Betula nana, Vaccinium uliginosum, and Carex bigelowii. The concurring measurements of BVOCs emissions and spectroscopy took place in the area of Kobbefjord, Greenland, during the summer of 2024.

The performed analysis included steps of wavelength selection, feature ranking importance and construction of Structural Equation Models to verify the links between the remotely sensed variables (spectral and photosynthetic indices) and the emission rates of various BVOCs groups.

The results show that the most important wavelengths for the emissions of BVOCs are found in proximity to the O₂-A and O₂-B absorption bands for several BVOCs groups. The feature importance rankings show that SIF is moderately important for the emissions of isoprene and total BVOCs, while the most important abiotic drivers are canopy/soil temperature and PAR. Interestingly, other spectral/photosynthetic indices such as EVI, PRI and MTCI appear at the top of the ranking for monoterpenes, sesquiterpenes, and isoprene respectively. The importance of SIF for the emissions of isoprene and total BVOCs is corroborated by the built Structural Equation Models.

These findings can potentially be of aid in opening new avenues to model BVOCs emissions at larger scale, as SIF and other relevant indices can be directly derived with UAV and satellite imagery.

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The upcoming FLEX mission will provide spectrally resolved Sun-induced fluorescence (SIF) observations, opening new opportunities to quantify photosynthetic function and stress from space. To fully exploit FLEX data, these SIF measurements must be interpreted together with robust, uncertainty-aware essential vegetation traits (EVTs) products derived from the tandem-orbiting Sentinel-3 (S3) mission. These EVTs provide key structural, biochemical, and radiative context for FLEX Level-2 SIF retrievals and FLEX-related Level-3/4 products on photosynthetic activity and ecosystem productivity.

Here, we present three Gaussian Process Regression (GPR) model families, each tailored to a distinct training dataset released between 2022 and 2025, and designed for operational retrieval of EVTs from S3: (1) simulation-based top-of-atmosphere models (S3-TOA-GPR), (2) hybrid models trained on S3 SYNERGY surface reflectance (SCOPE-SYN-GPR), and (3) empirical models trained on upscaled GBOV reference data (GBOV-SYN-GPR). The models target leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), which jointly constrain canopy structure, light absorption, and photosynthetic capacity.

The S3-TOA-GPR models are trained on radiance simulations from the coupled SCOPE–6SV radiative transfer framework and implemented directly in Google Earth Engine (GEE). This allows direct retrieval of EVTs from OLCI TOA data, bypassing explicit atmospheric correction. Multi-site validation against MODIS products, comparison with Copernicus Global Land Service (CGLS) maps, and evaluation using VALERI field data demonstrate coherent phenological dynamics and spatial consistency.

To exploit the operational S3 SYNERGY Level-2 surface reflectance product (SY_2_SYN, 300 m), SCOPE-SYN-GPR models were trained using SCOPE simulations, and FAPAR/FVC models were validated against the global Copernicus Ground-Based Observations for Validation (GBOV) dataset. Intercomparison with CGLS FAPAR/FVC products shows strong agreement, with SCOPE-SYN-GPR slightly outperforming existing solutions. These models are deployed on cloud infrastructures through the openEO platform, enabling continental-scale mapping with Bayesian uncertainties and standardized programmatic access.

Finally, to address the limitations of SCOPE in capturing conditions characteristic of heterogeneous landscapes, we have transitioned to fully empirical GPR models. GBOV-SYN-GPR models (LAI/FAPAR/FVC) were trained on more than 3,650 GBOV samples across Europe and validated with independent GBOV data (2022–2024, over 1700 GBOVs samples). Performance is biome dependent, with high accuracy for croplands and persistent challenges in dense evergreen forests, consistent with expected saturation and separability limitations (as also observed in previous GPR models). A key feature of this framework is the possibility for explicit decomposition of predictive uncertainty into epistemic (model-driven) and aleatoric (observation-driven) components, revealing structured spatial patterns linked to training representativeness and observation conditions.

All three model families are implemented in the open-source PyEOGPR Python package (https://pypi.org/project/pyeogpr/), providing a reproducible, extensible, and cloud-ready toolkit for scalable vegetation trait retrieval on GEE, openEO and other EO platforms. Together, these S3 GPR-based products deliver the structural and functional information needed to contextualize FLEX SIF signals, support algorithm evaluation at Level-2, and enable the generation of FLEX-relevant Level-3/4 indicators of photosynthetic stress and productivity.

Atmospheric radiative transfer models (RTMs) simulate how light propagates through the Earth’s atmosphere by accounting for interactions with gases, aerosols, and clouds. They are fundamental to remote sensing and satellite data processing because of their physical accuracy. However, this rigor comes with a high computational cost, which limits their operational use. A common workaround is the use of look-up tables (LUTs) of stored RTM simulations. While LUT interpolation speeds up computations, it requires very large datasets to preserve accuracy, leading to high generation times and memory demands. These issues become particularly severe for hyperspectral missions, which often need mission-specific LUTs and limit the generalization of processing algorithms. A prominent example is ESA’s FLEX Earth Explorer mission, which aims to retrieve sun-induced fluorescence using high spectral resolution observations in the O2 absorption bands. Achieving the required atmospheric correction accuracy for FLEX demands densely sampled LUTs based on high-resolution RTM simulations, posing a major challenge for operational processing.

Machine-learning-based emulators have emerged as a promising alternative. Instead of interpolating LUTs, emulators statistically approximate RTM outputs through regression models, offering large gains in speed while maintaining high accuracy. However, the high dimensionality and physical complexity of RTM data make emulation challenging, leading to a wide range of proposed methods that trade off accuracy, interpretability, and computational efficiency.

To advance this field, the second Atmospheric Machine Learning Emulation Challenge (AMLEC-2) is being organized in collaboration with ESA’s phi-lab. The goal of AMLEC-2 is to engage the remote sensing and machine learning communities in developing and benchmarking RTM emulators using common datasets and standardized evaluation protocols. This edition focuses on the FLEX mission and evaluates emulators based on prediction accuracy, runtime performance, and uncertainty quantification.

AMLEC-2 is structured into four phases. First, the challenge design, datasets, and evaluation methodology are defined. Second, RTM simulation datasets are generated. Third, over three months, participants train their emulators and submit predictions, with automated evaluations and regularly updated rankings encouraging iterative improvement. In the final phase, results are analyzed and potentially presented in a peer-reviewed publication. Participants are involved only in the third phase, which includes downloading data, training models, generating predictions, and submitting results.

Although the challenge is still in the design stage at the time of writing, it will be in its third phase at the time of the FLEX Fluorescence Workshop. We will present the organisation of the challenge, the evaluation methodology, and the test datasets. We expect that this contribution will encourage the FLEX community to participate in AMLEC-2, ultimately supporting the development and implementation of novel data processing algorithms for the FLEX mission.

The retrieval of solar-induced fluorescence (SIF) from remote sensing measurements requires accurate modeling of surface, atmospheric and sensor effects in order to separate the feeble fluorescence signal from the dominant reflected light component. Radiative transfer modeling has been routinely used to achieve the required level of accuracy for SIF retrieval, but it involves time-consuming calculations typically performed offline. The necessarily slow radiative transfer step presents a fundamental obstacle to the full exploitation of modern machine and deep learning SIF retrieval algorithms, which imply training or processing of large amounts of data. This challenge is addressed here by developing a fast and accurate physics-based emulator of at-sensor radiances. We start by introducing a general-purpose simulation framework and then apply it to simulate at-sensor radiances around the O₂-A absorption band (740–780 nm) for two representative sensors: HyPlant (airborne, limited spatial coverage, spatial resolution of ~1 m, spectral resolution of ~0.25 nm) and DESIS (space-based, global spatial coverage, spatial resolution of 30 m, spectral resolution of ~3.5 nm). Millions of HyPlant and DESIS spectra are simulated across a meaningful range of atmosphere, geometry, surface and sensor characteristics. We then show that a simple polynomial model trained on these extensive datasets is an excellent forward emulator of at-sensor radiances at the O₂-A band for both HyPlant and DESIS with prediction times per spectrum as low as 10 µs and typical errors of at most 10% of a typical SIF signal. Importantly, the emulator can closely reproduce measurements acquired by HyPlant and DESIS thereby underlining its suitability for interpreting real data. These findings are of relevance for novel SIF retrieval schemes, opening the way for the swift generation of high-fidelity training datasets, the realization of a fast and accurate simulation step and the performance assessment of any retrieval method. The recently developed SFMNN SIF retrieval algorithm implements a self-supervised method employing the emulator presented here as the forward simulation step and has been shown to successfully retrieve SIF from HyPlant and DESIS data (see contribution by Buffat et al). Additionally, we quantitatively assess the SIF retrieval performance of the 3FLD method on simulated data and illustrate the optimization of a retrieval method using our framework. Finally, we comment on how our emulators can be extended to the FLORIS instrument and beyond the O₂-A band providing a promising avenue to derive SIF from FLEX data with SFMNN or other novel SIF retrieval methods.

The development, test and validation of retrieval methods for Solar Induced Fluorescence (SIF), especially full spectrum retrievals, require accurate radiative transfer models that incorporate fluorescence. Due to the small contribution of SIF to the overall signal, very high model accuracy is required.

SLOPE is a physics based, deterministic, leaf level radiative transfer model using independently derived specific absorption coefficients for leaf pigments and other constituents. Therefore, SLOPE produces accurate outputs using only biophysical variables like pigment concentrations as inputs. It does not require adjustment of tuning parameters which would require a number of simultaneous measurements of pigment concentrations and reflectance and fluorescence spectra.

SLOPE takes into account the non-homogeneous distribution of pigments across the leaf cross-section that is exhibited by most dicotyledon and some monocotyledon leaves. It also takes into account that pigments like chlorophylls are not homogeneously distributed laterally but rather concentrated (clumped) in chloroplasts and thylakoids. Also in SLOPE emphasis has been placed on accurate representation of the red absorption maximum and red-edge spectral regions.

This presentation will firstly provide a brief description of the model structure. Secondly, results of intercomparisons with reflectance, transmittance and fluorescence measurements will be given. The third part will demonstrate the influence of pigment concentrations on reflected light and fluorescence emission. Furthermore, the impact of non-homogeneous vertical and lateral distribution of pigments on fluorescence emission will be shown.

Originally, SLOPE was implemented in C. To facilitate easier and more widespread use it is currently being re-implemented in Phyton. All source code is fully open source and is not subject to any licensing restrictions.

The use of SIF to evaluate land surface models shows considerable promise to help constrain estimates of, and elucidate the processes that control Gross Primary Productivity (GPP) on large spatial scales. To use SIF effectively for this purpose, we argue that forward modelling of the observations from the land surface model – as opposed to, say, relying on empirical relationships with the modelled GPP – is desirable if we wish to understand structural deficiencies in the land surface model.

This presentation describes the prediction of SIF from JULES, the Joint UK Land Environment Simulator, which is the land surface scheme of the Hadley Centre climate models, and the UK Earth System Model (UKESM). We explain how we couple leaf-level SIF models to the biochemistry routines in JULES, and how we scale the emitted SIF to the canopy level using a vegetation radiative transfer scheme (L2SM) that is consistent with the physics inside JULES but also allows for radiative emissions within the canopy. The SIF scheme includes attenuation within the leaf, utilizing either modelled or observed leaf reflectance and transmittance spectra and can make predictions of the canopy leaving SIF at arbitrary wavelengths. Downregulation of fluorescence by water stress is also included.

We show results JULES-SIF from the recent SIFMIP exercise at site level, and also regional comparisons against TROPOSIF data. The results show a generally good agreement and are sufficiently aligned with the observations that they are able to highlight areas where JULES is not correctly modelling the relevant environmental processes. Future directions for the JULES SIF module are explained, including accounting the directional component of the canopy leaving SIF.

We present the Terrestrial Carbon Community Assimilation System (TCCAS), funded by the European Space Agency within its Carbon Science Cluster. TCCAS is built around the newly developed D&B terrestrial biosphere model (Knorr et al., 2025). D&B builds on the strengths of each component model in that it combines the dynamic simulation of the carbon pools and canopy phenology of DALEC with the dynamic simulation of water pools, and the canopy model of photosynthesis and energy balance of BETHY. A suite of observation operators allows the simulation of solar-induced fluorescence (SIF), fraction of absorbed photosynthetically active radiation, vegetation optical depth from passive microwave sensors, the slope of the backscatter-incidence angle relationship of an active microwave sensor, surface layer soil moisture, and Net Ecosystem Production. The model is embedded into a variational assimilation system that adjusts a control vector to match the observational data streams. For this purpose TCCAS is provided with efficient tangent and adjoint code. The control vector consists of a combination of initial pool sizes and process parameters in the core model and in the observation operators. The observation operator for SIF simulates the radiative transfer within the canopy-soil system by the layered two stream model (Quaife, 2025). It offers leaf-level SIF formulations according to Gu et al. (2019), van der Tol et al. (2014) or Li et al. (2022). We show assimilation experiments of the TROPOSIF product derived from Sentinel 5P using each of the three formulations and discuss their performance. TCCAS and D&B are available as open source community tools with documentation and training events.

Gu, L., Han, J., Wood, J.D., Chang, C.Y.Y. and Sun, Y., 2019. Sun‐induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions. New Phytologist, 223(3), pp.1179-1191.

Knorr, W., Williams, M., Thum, T., Kaminski, T., Voßbeck, M., Scholze, M., Quaife, T., Smallman, T. L., Steele-Dunne, S. C., Vreugdenhil, M., Green, T., Zaehle, S., Aurela, M., Bouvet, A., Bueechi, E., Dorigo, W., El-Madany, T. S., Migliavacca, M., Honkanen, M., Kerr, Y. H., Kontu, A., Lemmetyinen, J., Lindqvist, H., Mialon, A., Miinalainen, T., Pique, G., Ojasalo, A., Quegan, S., Rayner, P. J., Reyes-Muñoz, P., Rodríguez-Fernández, N., Schwank, M., Verrelst, J., Zhu, S., Schüttemeyer, D., and Drusch, M.: A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0, Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, 2025.

Li, R., Lombardozzi, D., Shi, M., Frankenberg, C., Parazoo, N.C., Köhler, P., Yi, K., Guan, K. and Yang, X., 2022. Representation of Leaf‐to‐Canopy Radiative Transfer Processes Improves Simulation of Far‐Red Solar‐Induced Chlorophyll Fluorescence in the Community Land Model Version 5. Journal of Advances in Modeling Earth Systems, 14(3), p.e2021MS002747.

Quaife, T. L. (2025). A two stream radiative transfer model for vertically inhomogeneous vegetation canopies including internal emission. Journal of Advances in Modeling Earth Systems, 17, e2024MS004712. https://doi.org/10.1029/2024MS004712

van der Tol, C., Berry, J. A., Campbell, P. K. E., and Rascher, U.: Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence, Journal of Geophysical Research: Biogeosciences, 119, 2312–2327, doi:10.1002/2014JG002713, URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/2014JG002713, 2014.

In recent years, solar-induced chlorophyll fluorescence (SIF) showed great potential for monitoring terrestrial photosynthesis, but existing SIF products retrieved from atmospheric sensors typically feature coarse spatial resolutions from several to hundreds of kilometers. Recently, the Chinese Terrestrial Ecosystem Carbon Inventory Satellite, known as Goumang satellite, launched in August 2022, carries an unique SIF Imaging Spectrometer (SIFIS), the first spaceborne sensor specifically designed for retrieving terrestrial SIF globally. SIFIS provides a substantially improved spatial resolution (along track: 370 m; across track: 800 m), representing an important advance in SIF remote sensing capabilities. Benefiting from its high spectral resolution (0.24 nm) in the spectral range of 664-786 nm, SIFIS SIF can be accurately retrieved using a data-driven approach. In addition, the SIFIS SIF showed excellent spatial and temporal agreement with airborne AisaIBIS data and independent datasets. We also show first results from the recent launched Fengyun 3H satellite which carries on a GHG instrument with potential SIF retrievals at 2km*2km.

Solar-induced chlorophyll fluorescence (SIF) provides a direct optical window into photosynthetic functioning and has emerged as a powerful proxy for gross primary productivity (GPP) from space. Yet most existing satellite SIF products are derived from polar-orbiting missions, which provide only instantaneous snapshots and therefore cannot resolve the dynamic, sub-daily evolution of photosynthesis. Although the OCO-3 mission offers observations at multiple local times, its within-day sampling remains sparse, limiting its ability to characterize full diurnal trajectories. As a result, an hourly-scale SIF product that can routinely capture diurnal photosynthetic dynamics is still lacking. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission provides a unique opportunity to fill this gap. TEMPO is a geostationary hyperspectral spectrometer over North America with hourly revisit frequency, sub-nanometer spectral resolution across the visible wavelengths, and kilometer-scale spatial resolution, enabling dense temporal sampling under nearly stable viewing geometry. Here we develop and apply a data-driven retrieval framework to derive hourly red SIF from TEMPO radiance observations across North America. The resulting product delivers spatially continuous fields of red SIF at hourly resolution, allowing systematic characterization of diurnal variability across ecosystems. Our results reveal pronounced ecosystem-dependent diurnal patterns in red SIF, including consistent afternoon depression, indicative of short-term photosynthetic downregulation. Comparisons with flux-tower GPP demonstrate that TEMPO-derived red SIF closely tracks sub-daily photosynthetic dynamics and improves the representation of diurnal variability relative to polar-orbiting SIF products. These findings highlight TEMPO’s potential to enable routine, continent-scale monitoring of photosynthetic regulation and stress through hourly red SIF observations.

In 2026, the ESA FLEX mission will provide global measurements of sun-induced fluorescence (SIF), offering insights into plant photosynthetic activity as well as plant health and stress. In this context, data from existing space-borne hyperspectral missions can play a complementary role by helping with the preparation of commissioning activities, contributing to the validation of FLEX mission products, and, more generally, supporting the analysis and interpretation of FLEX data. This contribution presents the DESIS and EnMAP hyperspectral missions, emphasizing the complementary aspects that can support the FLEX scientific objectives.

The DESIS mission is a collaboration between the German Aerospace Center (DLR) and the U.S. company Teledyne Brown Engineering. DESIS is an imaging spectrometer with 30 m spatial resolution and a swath width of 30 km, operating in the VNIR range (400 – 1000 nm). It is located on the International Space Station (ISS), which allows observations of targets in the latitude range from 55° North to 52° South at varying local times. With a spectral full width half maximum (FWHM) of 3.5 nm, DESIS has a relatively high spectral resolution compared to similar missions. This has allowed the use of DESIS data for SIF retrieval, as presented in another contribution at this workshop (see Buffat et al.).

EnMAP is a German satellite operated by DLR. EnMAP observes from a Sun-synchronous orbit (11:00 local time descending node) with a 27-day repeat cycle, which allows re-observing a target approximately every four days by varying the off-nadir tilt angle. The instrument consists of two spectrometers: a VNIR spectrometer operating in the range 420–1000 nm and a SWIR spectrometer operating in the range 900–2450 nm. The average spectral FWHM of EnMAP is 6–11 nm in the VNIR and 7–11.5 nm in the SWIR. The spatial resolution is, like in DESIS, 30 m with a swath width of 30 km.
In terms of calibration, EnMAP is equipped with several on-board calibration devices, which allow very precise monitoring of instrument performance, while DESIS relies on vicarious calibration and shows higher spectral variability. Both missions use similar processing chains that provide products at three different levels: L1B (at-sensor top-of-atmosphere radiance), L1C (orthorectified and geolocated top-of-atmosphere radiances), and L2A (orthorectified and geolocated bottom-of-atmosphere reflectances). These data can be accessed via the DLR EOWEB portal (https://eoweb.dlr.de/) and are also available for direct download from the EOC-Geoservice (https://geoservice.dlr.de/) after user registration for each mission. The EnMAP mission catalogue dates back to April 2022, while the DESIS catalogue extends back to October 2018.

In this contribution, we discuss characteristics of EnMAP and DESIS data that are relevant for FLEX, including acquisition strategies, spectral coverage, radiometric performance, spatial resolution, geolocation accuracy, and data access. Potential use cases are presented, such as the inclusion of information derived from the EnMAP SWIR range, identification of suitable validation areas, as well as mission intercomparison activities. The contribution highlights how EnMAP and DESIS data can complement FLEX observations by offering independent hyperspectral radiance and reflectance measurements with higher spatial resolution.

With the FLEX launch approaching, downscaled SIF datasets are essential to bridge coarse-resolution observations and the sub-kilometer applications envisioned for the mission. A key feature of FLEX is its tandem flight with Sentinel-3 (S3), allowing S3 observations to provide the ancillary atmospheric and vegetation information required for sub-kilometer SIF applications. Building on this tandem-mission concept and addressing the current scale gap, we developed the first global downscaled SIF (S3-SIF₇₄₃) dataset with a spatiotemporal resolution of 300 m and 4 days.

Our methodology integrates TROPOspheric Monitoring Instrument in the 743–758 nm retrieval window (TROPOSIF₇₄₃) with S3 OLCI radiances and S3-derived vegetation traits through a Random Forest regression framework implemented on Google Earth Engine (GEE). This approach uses S3's 300 m spectral capabilities to downscale coarse SIF observations, consistent with the FLEX–S3 observing strategy. Model training over Europe achieved robust performance against TROPOSIF₇₄₃ reference data (R² = 0.86, RMSE = 0.14 mWm⁻² sr⁻¹ nm⁻¹), and the approach was subsequently extended globally. Validation against ground-based tower observations across four sites spanning 2017–2021 confirmed that S3-SIF₇₄₃ effectively reproduces seasonal dynamics across diverse ecosystems, with correlation coefficients ranging from R = 0.40 to 0.70 (all P < 0.001). Pixelwise comparisons against TROPOSIF₇₄₃ in 2020 demonstrated strong spatial consistency, particularly in temperate agricultural regions, with the highest R² values in croplands (R² = 0.70) and deciduous forests (R² = 0.67–0.75). Global mapping revealed coherent patterns of photosynthetic activity, with peak values in tropical rainforests and major agricultural zones. Importantly, S3-SIF₇₄₃ reduces retrieval noise relative to TROPOSIF₇₄₃ and provides unprecedented insights into sub-kilometer spatial heterogeneity.

By utilizing S3’s spectral information and GEE’s scalable processing capabilities, S3-SIF743 provides sub-kilometer SIF information that is directly relevant to FLEX spatial scales and science objectives. Our work offers a practical framework for FLEX-related studies, while ensuring temporal continuity before and during the FLEX mission lifetime. As such, this work contributes a complementary, operational pathway that bridges current multi-mission SIF capabilities with the high spectral precision of FLEX, supporting the mission’s long-term goal of advancing quantitative monitoring of terrestrial photosynthesis.

Solar-induced chlorophyll fluorescence (SIF) is a re-emitted signal arising directly from the photosynthetic process that can currently be retrieved from instruments such as TROPOMI on-board of Sentinel-5P. We expect this signal to have some potential in diagnosing vegetation stress, and in particular before this stress is reflected by decreases in greenness indicators. To do so, we would need to disentangle the physiological component, i.e. fluorescence efficiency (ΦF), from other confounding factors present in the SIF signal. Satellite SIF observations (SIF_obs) are strongly modulated by illumination conditions. The canopy structure also determines the fraction (f_esc) of fluorescence that escapes the canopy to the sensor. This is further compounded by the spatial heterogeneity of vegetation elements within the nadir observation footprint. Methods are available to remedy these effects, but they are typically applied only after multiple instantaneous SIF observations have been forced into a convenient common grid. We hypothesize that such harmonization should be done prior to gridding, thereby ensuring these are done over the correct spatio-temporal supports. This would enable reducing uncertainties due to mismatches in space and time.

We therefore propose deriving SIF efficiency (ΦF) from TROPOMI by normalizing SIF_obs with radiation and canopy features prior to spatial gridding. We normalize SIF_obs by photosynthetically active radiation (PAR) and near-infrared reflectance of vegetation (NIRv), where NIRv serves as a proxy for vegetation greenness and canopy structure. We explore ΦF over Germany using multi-source NIRv and PAR datasets and TROPOMI SIF from three retrieval products. We assemble downward shortwave radiation as a proxy for PAR from multiple sources spanning different spatio-temporal resolutions (e.g. MSG, ERA5, TROPOMI). NIRv is derived from other sources (e.g. MODIS, Sentinel-3, Sentinel-2) and aggregated to the TROPOMI footprint, as well as from TROPOMI native TOA reflectance products.

To evaluate whether the ΦF derived from our different combinations of SIF retrievals and alternative PAR–NIRv combinations are sensitive to vegetation stress, we analyse them before and during compound hot and dry (CHD) events. We use the Dheed dataset, an ERA5-based CHD event database at 0.1° spatial resolution. The temporal consistency and sensitivity of ΦF to CHD events is assessed across biomes, with emphasis on homogeneous rainfed croplands and forests.

Capturing the sub-daily variability of terrestrial ecosystem productivity is one of the next frontiers for satellite remote sensing. The changes in the environmental drivers of an ecosystem can often be more radical within the arc of a day than over several months. Plant stress can notably kick in during the afternoon, leading to an afternoon depression of photosynthesis that would ideally be detectable from fluorescence yield measured from space. FLEX will open a new era for fluorescence measurement from space. Being the first mission specifically dedicated to this task, it will offer a comprehensive measurement of the fluorescence signal along with the confounding factors complicating its relationship to photosynthesis. By reaching a spatial resolution of 300m we will largely avoid the complexity of dealing with landscape-level structural heterogeneity. However, its morning overpass and its low revisit frequency remain a major limitation to progress towards capturing diurnal dynamics, or the diel scale as it is otherwise known. How can we capitalize on the advantage of FLEX to progress towards the goal of characterizing terrestrial ecosystem diel productivity? The present contribution discusses a roadmap to this end, being therefore directly in-line with the workshop main’s theme of “Combining multi-scale, multi-mission EO data for closing the temporal and spatial gap” and the workshop’s objective to “Plan future activities related to fluorescence and merged data sets”. It involves combining data from various missions (Meteosat Second Generation, Sentinels 2, 3, and 5P), along with downscaling and data fusion tools being developed in Horizon Europe projects Open Earth Monitor (OEMC) and NextGenCarbon, to attempt extending FLEX level 3 products of fluorescence yield to diel frequency. Furthermore, we also elaborate on the potential opportunity to retrieve SIF directly from geostationary satellites such as Sentinel 4 in order to support this goal.