Conference Agenda

The coastal ocean plays a critical role in the ocean carbon cycle, yet our current understanding of the different pools and fluxes of organic carbon is limited by strong local dynamics, which requires high spatial and temporal resolution of observations covering relatively large areas across the shelf seas. Satellite remote sensing of carbon pools and fluxes at high spatial resolution and daily frequency can cover this gap in observations, but new algorithms need to be developed and validated.

In the ESA project “Carbon from earth Observation between Ocean and Land (COOL)”, we aim to estimate carbon pools and fluxes for which algorithms are relatively mature, and hence we can have some certainty in their application in the coastal ocean at the global scale in the near future. These include Particulate Organic Carbon (POC), Particulate Inorganic Carbon (PIC), Dissolved Organic Carbon (DOC) and Primary Production (PP). Using data from Sentinel-3 OLCI at 300 m resolution and Sentinel-2 MSI at 60 m, we aim to produce an internally consistent coastal ocean carbon satellite dataset in selected European coastal regions. We make use of in situ datasets for evaluation and validation of these satellite products and investigate the newly produced Earth Observation datasets in the Baltic Sea and in the upwelling areas of the western Gallican coast, northwest Spain.

Satellite observations of particulate backscattering (bbp) have greatly enhanced our understanding of ocean biology and biogeochemistry on large scales, serving as proxies for phytoplankton biomass or particulate organic carbon. bbp is essential for estimating organic carbon stocks and fluxes and ocean productivity, which is subsequently incorporated into coupled physical-biogeochemical models. However, the paucity of in situ multi-band bbp data hinders efforts to quantify uncertainties in satellite bbp and its derived products. To address these gaps, Surface Velocity Programme (SVP) drifting buoys have been equipped with bio-otical and oxygen sensors, defining the Biogeochemical (BGC)-SVP drifters. The high sampling frequency combined with a Lagrangian approach enables it to overpass numerous pixels in a single day, thus providing large in situ datasets for validation activities, not achievable by other in situ platforms. Here, we present a novel Observing System (OS) to validate satellite bbp products, integrating remote sensing, Lagrangian modeling, and in situ data, for the Mediterranean Sea. Main innovations are: integration of Lagrangian simulations using a sub-grid kinematic model applied to ocean currents datasets, and the constraing of simulated trajectories with gapped satellite bbp data to assess variability and identify optimal deployment sites and time for BGCSVP drifters, maximizing match-up opportunities. Different criteria are established as the beachtime time, the total potential and bin-specific matchups. Preliminary results suggest the Ionian Sea as the best site to reduce drifter beaching but also to capture low-mid bbp values over the entire year. Higher bbp values could be captured during winter and spring in the northwestern Mediterraean Sea. The development of an OS is a foundational step from research to sustained operations. The OS framework here developed can be extended to global ocean and has potential applications for validating other ocean color variables across ongoing (e.g., Sentinel-3/OLCI, PACE/OCI) and future satellite missions (e.g., ESA CHIME).

The particulate backscattering coefficient (bbp) is an indicator of phytoplankton biomass, particulate organic carbon, and particle size distribution in the ocean. It serves as input for modeling net marine primary production and net community production. Since bbp can be estimated through satellite imagery, it plays a fundamental role in quantification primary production on a global scale and evaluating its spatial patterns. Therefore, accurate satellite-based bbp is required to constrain coupled physical and biogeochemical models, thereby improving climate projections. To date, most of the European Space Agency (ESA) Ocean Science Cluster-funded projects that utilize bbp have relied on global operative products (i.e., ESA OC-CCI). However, these products lack associated uncertainty compared to in situ measurements, limiting out understanding of their impact on ocean productivity and organic carbon export.

The ESA INSPIRE project aims to address this gap by developing an advanced Observing System (GOS) specifically tailored for validating satellite bbp products, integrating remote sensing, Lagrangian modelling and in situ data. This involves using a new generation of Surface Velocity Programme (SVP) drifting buoys equipped with bio-optical and oxygen sensor, named Biogeochemical (BGC)-SVP drifters. Designed for extended deployment periods, they offer a promising solution for collecting data in challenging marine environments by the combination of the Lagrangian approach and a high sampling frequency. This project seeks optimize drifter deployment locations to maximises the number of in situ observations usable for match-up activities. Lastly, satellite bbp products will be validated with in situ measurements collected using BGC-SVP drifters deployed both the global ocean with a particular focus in the Mediterranean Sea. The drifters launched in the Mediterranean Sea were acquired through the ITINERIS (Italian Integrated Environmental Research Infrastructures System) project.

The present study and the use of BGC-SVP drifters could be impactful in relation to the next generation of altimetry (e.g., NASA SWOT), hyperspectral ocean color satellite missions (e.g., NASA PACE, NASA GLIMR, ESA Sentinel Next Generation, and ESA CHIME), and future lidar mission (e.g., ASI CALIGOLA) for the detection of ocean processes from fine to larges scales both in space and time.

The ocean is one of the largest sinks of anthropogenic CO₂ emissions, yet quantification of its carbon storage and distribution remains uncertain. Satellite measurements of ocean colour, sea surface temperature, salinity, altimetry and atmospheric CO₂ offer global unique coverage. However, effective and transparent integration of these diverse datasets persists as a complex problem. This study introduces an XAI prototype model that aims to predict air-sea CO₂ fluxes, using combined Earth observation and in-situ data. Over the North Atlantic, a regional test case was used where SOCAT in situ pCO₂ measurements were combined with multi-sensor satellite data from Sentinel-3 (OLCI chlorophyll-a, SLSTR SST, altimetry SSH) and ASCAT winds. Machine learning models (XGBoost) were used to predict surface pCO₂ and air-sea fluxes were computed through traditional bulk formulations.

For interpretability, SHAP values were utilized to quantify the relative contribution of environmental drivers to flux estimates. The findings show that sea surface temperature anomaly dictated variability for the 2019 marine heatwave and chlorophyll-a contributed significantly during seasonal bloom events. Outputs were then framed into a Carbon Knowledge Graph (CKG), linking flux estimates to their drivers and uncertainties. Additionally, this approach highlights the potential for scalable applications to other ocean basins in support of global climate assessments. The framework shows how explainable machine learning and knowledge organization can provide open, policy-relevant monitoring of the ocean carbon cycle.

Dissolved organic carbon (DOC) plays a crucial role in ecological and biogeochemical processes. Many models have been developed to estimate DOC in coastal waters at a local scale using ocean-colour remote-sensing data. However, there is currently no global algorithm capable of addressing the variability and complexity of DOC dynamics in coastal waters. In the Satellite-based observations of Carbon in the Ocean: Pools, fluxes and Exchange (SCOPE) project, funded by the European Space Agency (ESA), we aim to address this gap by developing a global DOC satellite retrieval algorithm for coastal waters by using daily, 4-km resolution data from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI) from 1997 to 2023, combined with sea surface salinity (GLORYS12v1) and temperature (ESA SST-CCI, version 3.0). For model development, we matched these satellite datasets with in situ DOC concentration data from the CoastDOM v1 database. Multiple statistical methods, including multiple linear regression (MLR), random forest regression (RF), and extreme gradient boosting (XGBoost), were tested, with the best performance achieved by a RF model using sea surface salinity and temperature, the remote sensing reflectance at 560 nm and total absorption at 412 nm. Although the developed algorithm showed high performance, the relatively coarse resolution of OC-CCI poses challenges, as it may fail to resolve sharp DOC gradients in dynamic coastal zones such as river plumes and estuaries, potentially reducing accuracy in those areas. Still, OC-CCI offers climate-quality data for a longer period of time compared to individual ocean-colour sensors. Expanding in situ observations, especially in underrepresented areas, will further enhance model accuracy and applicability. This work contributes to a better understanding of carbon dynamics in coastal ecosystems and provides a robust tool for future satellite-based assessments of DOC in global coastal waters.

The research project investigates the complex interplay between underwater light dynamics and carbon cycling in the ecologically and economically significant coastal waters of the Sundarbans. These coastal ecosystems, characterized by persistently high turbidity due to sediment discharge from the Ganges-Brahmaputra-Meghna river system, pose unique challenges for environmental monitoring and resource management. The concentration of Total Suspended Solids (TSS), often exceeding 210 mg/L, significantly limits the availability of Photosynthetically Active Radiation (PAR), which is critical for phytoplankton growth—the foundation of aquatic food webs. In this study, we employ direct, high-accuracy in-situ measurements and laboratory analyses to accurately characterize the underwater light environment and carbon dynamics. We have conducted field studies during peak monsoon conditions to capture maximum turbidity effects and assess vertical profiles of PAR, turbidity, and chlorophyll-a (Chl-a) across multiple sampling stations, notably around Dublar Char and Kuakata. The data collected evaluates how variations in light characteristics and carbon pools influence ecosystem productivity indicators that are vital for fisheries, aquaculture, and overall water quality.

Integrating satellite remote sensing data provides a broader spatial context and allows validation against our field observations, thereby enhancing the reliability of the results. This research aligns with national developmental goals by addressing critical environmental challenges and promoting sustainable resource management practices. It actively contributes to global Sustainable Development Goals (SDGs) related to climate action, marine resource sustainability, and food security. By bridging the data gap in this optically complex region, our study produces actionable insights that guide policy development and climate resilience initiatives crucial for the livelihoods of coastal communities.

Keywords: Light dynamics, Carbon cycling, Aquatic productivity, Remote sensing, Coastal management.

Accurate estimation of phytoplankton functional types (PFTs) from space remains challenging in optically complex waters, where global algorithms often fail to capture regional bio-optical variability. We present advances in regionalized algorithm development and validation for two contrasting yet similarly challenging basins: the Baltic Sea and the Black Sea. In the Baltic Sea, we refined empirical diagnostic pigment (DP)-based approaches using High-Performance Liquid Chromatography (HPLC) datasets from multiple sub-basins, enabling improved estimation of phytoplankton size classes (PSCs) and key functional groups such as cryptophytes, green algae, and dinoflagellates. Nanoplankton dominated basin-wide (~46% of chlorophyll a), while picoplankton prevailed offshore and microplankton peaked in nearshore regions. In the Black Sea, we combined hierarchical clustering, principal component analysis, and network-based community detection to derive region-specific coefficients for PFT–pigment relationships from HPLC measurements at 690 stations collected across 12 bio-optical campaigns. Applying these algorithms to multi-decadal satellite chlorophyll-a datasets (1998–2024), we reconstructed spatial and temporal patterns of PFTs, with microplankton dominating nutrient-rich coastal zones (70–80% of chlorophyll a), nanoplankton showing broad distribution (~30–40%), and picoplankton prevailing offshore (>60%). Both regionalized models significantly reduced errors compared to global approaches, particularly for cryptophytes, haptophytes, and prochlorophytes, and showed consistency with microscopy-based observations. These results demonstrate the potential of tailored algorithms to enhance the monitoring of phytoplankton community structure in coastal seas, thereby advancing our capacity to assess ecosystem dynamics and their role in regional carbon cycling.

The Yangtze River Estuary in China – one of the most turbid estuaries worldwide - is a biogeochemical transformer of autochthonous and allochthonous dissolved organic carbon (DOC) that shapes coastal ecosystem functioning and bio-diversity. Here, we used almost one decade of satellite data from Sentinel-3/OLCI, to examine, for the first time, spatial patterns, seasonal cycles, and interannual variabilities of DOC across this important and complex ecosystem from space. Four atmospheric correction (AC) approaches (C2RCC, ACOLITE, MUMM, and POLYMER) were first evaluated for OLCI using a rich field radiometric data collected across the estuary over the past ten years. We found that ACOLITE was the best-performing AC method, with mean absolute percent difference of 15%. An ocean color DOC algorithm was then developed using a machine-learning (random forest) approach for the Yangtze River Estuary based on a comprehensive bio-optical data collected in this system, and applied to generate a long-term DOC data record from OLCI (2016-2025). Higher DOC concentrations were consistently observed at the estuary mouth, strongly influenced by river discharge, while sharp gradients and distinct DOC plumes were captured on the inner East China Sea continental shelf, consistent with freshwater riverine export and monsoon. This study offered the first comprehensive observation of DOC dynamics across the estuary from the space and an analysis of the key factors driving biogeochemical variability at seasonal and interannual scales.

The central Iceland Basin (CIB) is a key region of the subpolar North Atlantic, where phytoplankton blooms drive substantial fluxes of carbon and nutrients, playing a critical role in regional biogeochemical cycles. In this study, we tuned a spectrally-resolved model of marine primary production using in situ datasets from the CIB. Measurements of phytoplankton pigments, light absorption coefficients, and photosynthesis–irradiance (P–E) parameters were obtained from several cruises of the CIB that covered various phases of the seasonal cycle, characterised by different levels of resource limitation. Matchups between in situ chlorophyll-a measurements and Level-3 satellite ocean colour data products reveal sensor-specific differences. Profiles of Chlorophyll-a fluorescence from CTD casts and BGC-Argo floats, corrected for non-photochemical quenching, were used to characterise the shape and magnitude of the biomass profile. The chlorophyll profile parameters and phytoplankton absorption measurements were incorporated into the radiative transfer model, and modelled profiles of photosynthetically active radiation (PAR) and downwelling irradiance at 412 and 490 nm were compared with measured irradiance profiles obtained from ship-based deployments and floats. Using the regionally-tuned primary production model, profiles of instantaneous production, integrated over the day, were computed. We discuss the implications of our findings for satellite-based estimates of primary production.

As a critical component of the oceanic carbon cycle, phytoplankton carbon should be monitored operationally. However, the reliability of ocean colour algorithms developed so far is yet to be fully established across different oceanic conditions. As part of the ESA-funded project ‘Satellite-based observations of Carbon in the Ocean: Pools, fluxes and Exchanges’ (SCOPE), we conducted a comprehensive intercomparison of representative algorithms developed previously for computing phytoplankton carbon from satellite ocean colour. Four categories of phytoplankton-carbon retrieval algorithms were considered based on their structure and suitability for global application: (1) particle backscattering-based empirical relationship, (2) particle backscattering-based allometric semi-analytical algorithm, (3) absorption-based allometric semi-analytical algorithm, and (4) photoacclimation-based algorithm. We compiled a large in situ database of phytoplankton carbon consisting of the available flow cytometry-based measurements and directly measured phytoplankton carbon to assess the performance of the algorithms. In situ matched-up algorithm outputs were generated using ESA’s Ocean Colour CCI v6 data archive. Results of this extensive intercomparison will be presented to assess the performance and consistency of the candidate algorithms, and the commonalities and major differences will be highlighted. The suitability of applying the candidate algorithms, either individually or in combination, for routinely estimating phytoplankton carbon in the global ocean will be discussed. Our results will contribute to enhanced understanding of the oceanic carbon cycle and the estimation of the ocean carbon budget using satellite remote sensing.

Particulate Inorganic Carbon (PIC) is an important component of the marine carbon cycle, due to its dual role in carbon sequestration and release. In the pelagic environment, PIC is produced by calcifying plankton, particularly coccolithophores. Mapping of coccolithophore bloom areas and monitoring PIC concentration have been performed from space using multispectral ocean colour satellite since the SeaWiFS mission.

Here we provide a first evaluation of the geostationary Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) data to map areas where PIC is the dominant source of scattering and to retrieve PIC concentration. In situ data were collected during a field campaign in the North Sea in June 2025, when several water types were sampled, including a coccolithophore bloom (PIC concentration between 10 and 80 mg m^-3). PIC was measured via ICP-OES from discrete water samples, and continuously during ship transit with a prototype LISST-PIC optical sensor. Previously published waveband difference algorithms were adapted to the FCI wavebands.

The classification method confidently detected areas with PIC above 40 mg m^-3 (3.3 mmol m^-3), while correctly excluding areas with a high non-coccolithophore particle load (e.g. the East Anglian plume). The PIC retrieval algorithm showed a Mean Absorlute Percentage Deviation (MAPD) of 40 % against discrete samples (N = 7) and 55 % against the continuous PIC measurements (N = 84), with most of the deviation arising from estimation bias (overestimation). While these results were obtained from a single campaign and a single coccocolithophore bloom, they suggest that the FCI can be used to detect areas where PIC is the main scatterer and for PIC quantification, warranting additional work to improve algorithm design. The high temporal frequency of FCI can potentially provide better coverage of areas with frequent cloud cover, as well as resolve tidal variations in coastal environments where coccolithophores are known to occur (e.g. English channel).