Conference Agenda

Effective and timely monitoring of crop water stress is critical for achieving agricultural sustainability. In our Dragon 6 project, we aim to synergistically utilise thermal infrared (TIR) and solar-induced fluorescence (SIF) observations from ESA and Chinese satellites to monitor crop water stress The key scientific objectives of this project are: 1) refining the land surface temperature (LST) retrieval method, 2) advancing evapotranspiration partitioning into soil evaporation and plant transpiration using an analytical model driven by LST, 3) developing models for estimating plant transpiration using SIF observations, 4) developing downscaling methods for LST and SIF data to fine spatial resolutions, and 5) enhancing crop water stress monitoring capability based on plant transpiration and SIF estimates. The progress in these five work packages (WP) for the last year is described as follows:

WP1. LST retrieval algorithm.

A new LST retrieval algorithm was developed by integrating the physical mechanisms of the split window (SW) and temperature emissivity separation (TES) algorithms into the deep learning (DL) model, constructing the DL-SW-TES framework. This new framework directly retrieves LST from easily accessible parameters without requiring any prior knowledge of LSE information and atmospheric profiles.

WP2. ET partitioning.

An advancement of the analytical surface energy balance (SEB) model STIC is envisaged. A “top-down” decomposition approach will be adopted based on the SW85 model. Such a method requires lumped estimates of energy fluxes (latent heat flux (ET), sensible heat flux, net radiation, and ground heat flux) in conjunction with air and dewpoint temperatures, LST, canopy-air vapor pressure, and aerodynamic conductance (g_A). The energy fluxes, canopy-air vapor pressure, and g_A are retrievable from STIC.

WP3. Modelling transpiration from SIF.

Two modelling approaches are planned. The first one is based on the connection of plant transpiration and photosynthesis through stomatal conductance. The second method is based on the FEST-2X2-EWB model. The FEST-2X2-EWB model computes continuously in time and distributes in space the total ET as well as the soil and vegetation components, and soil moisture at multiple soil layers. FEST-EWB computes LST as an internal variable and has physically-based non-residual formulations for the energy balance components (and can be used for model calibration or for updating the model state through data assimilation.

WP4. LST and SIF downscaling

The LST downscaling is planned to use a deep-learning based method. The estimated LST using the developed LST retrieval algorithm in WP1 will be input into the deep-learning framework. High spatial resolution LST will be obtained by combining ECOSTRESS and SEVIRI LST estimates.

To address systematic errors in SIF downscaling caused by canopy shadow, we implemented BRDF correction for vegetation indices and utilized SIF_n to minimize angular and PAR effects, generating a 500m SIF product. Subsequently, a Random Forest model was developed using VPD, LAI, and the generated SIF to estimate evapotranspiration, achieving high fitting accuracy across cropland, broadleaf forests, and needleleaf forests.

WP5. Crop water stress indicator development

we developed a novel crop water stress indicator, SIF_n, by minimizing the impact of angular effects and PAR variations on satellite-derived solar-induced fluorescence (SIF). During the 2019 North China Plain drought event, SIF_n exhibited an earlier decline and greater sensitivity in detecting the onset of water stress compared to traditional vegetation indices, raw SIF, and fluorescence quantum yield (ΦF). Furthermore, SIF_n anomalies showed a notable correlation with rainfall and meteorological factors, revealing the dynamic contributions of both canopy structure and vegetation physiology to SIF across different drought phases. These findings underscore SIF_n's potential to enhance the accuracy and timeliness of crop water stress monitoring.

Overall, these advancements in LST and plant transpiration retrieval, SIF downscaling will support the crop water stress monitoring with soil-independent information at high spatial resolutions.