Using deep learning to assimilate sun-induced fluorescence satellite observations in the ISBA land surface model
Abstract
Accurate representations of the surface and vegetation are critical for simulating the terrestrial CO2 cycle in response to climate and meteorological conditions. To meet this challenge, an increasing number of satellite missions are being launched which can monitor vegetation conditions and biomass. One is the Copernicus Sentinel-5P mission, which carries the TROPOMI instrument and retrieves Sun-Induced Chlorophyll Fluorescence (SIF). As an indicator of plant photosynthetic activity, SIF can provide critical information for evaluating and parameterising gross carbon flux dynamics in surface models. This study aims to assimilate TROPOMI SIF data into the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model developed by Meteo-France, with the objective of directly correcting the representation of Leaf Area Index (LAI) and Gross Primary Production (GPP). To achieve this, we have developed a dedicated observation operator that links the modelled LAI to the TROPOMI SIF daily product. This neural network (NN) operator was developed using deep learning and was trained using observations over Europe. This operator achieved good accuracy, was implemented in a land data assimilation system (LDAS), and was used to assimilate TROPOMI SIF in ISBA using a sequential simplified extended Kalman filter. Specific experiments were conducted to study the assimilation process over the Ebro basin in Spain. This area is known for its irrigated croplands, which are not well represented by ISBA. Some experiments assimilate TROPOMI SIF, some assimilate a Copernicus Land Monitoring Service (CLMS) LAI 10 d product, and some assimilate both. This provided us with a useful point of reference for improving the vegetation simulation. Following SIF assimilation, LAI representation improved across the domain, highlighting heavily irrigated croplands. The gross primary production (GPP) derived from the analysis is closing the gap between the simulated and observed values, though a marked difference remains. When compared with other assimilation experiments, assimilating SIF alone provides a similar benefit to standard assimilation of a CLMS LAI product on LAI and GPP. The best improvements to the LAI and GPP results come from co-assimilating TROPOMI SIF with the CLMS LAI product, which combines the advantages of high-frequency SIF observations and robust 10 d LAI assimilation.
Related
- Monitoring and forecasting groundwater storage dynamics in arid Wadi systems using GRACE/GRACE-FO satellite observations and XGBoost machine learning: Wadi El-Assiuti, Egypt (2003–2030)
- Satellite observations and energy balance constrain the thermal footprint of a hyperscale data-centre cluster
- Spatial variation of aerosol properties over Europe derived from satellite observations and comparison with model calculations
- Recurrent High-Resolution Satellite Observations Quantify Facility-Resolved Annual Large-Emitter Methane Emissions in the Permian Basin
- Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data
BibTeX
@article{vanderbecken2026using,
title = {{Using deep learning to assimilate sun-induced fluorescence satellite observations in the ISBA land surface model}},
author = {Pierre Vanderbecken and Jasmin Vural and Oscar Rojas-Muñoz and Sébastien Garrigues and Bertrand Bonan and Cédric Bacour and Uwe Rascher and Bastian Siegmann and Patricia de Rosnay and Jean-Christophe Calvet},
journal = {Biogeosciences},
year = {2026},
month = sep,
doi = {10.5194/bg-23-6687-2026},
url = {https://meteofrance.hal.science/meteo-05761333v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Using deep learning to assimilate sun-induced fluorescence satellite observations in the ISBA land surface model
AU - Pierre Vanderbecken
AU - Jasmin Vural
AU - Oscar Rojas-Muñoz
AU - Sébastien Garrigues
AU - Bertrand Bonan
AU - Cédric Bacour
AU - Uwe Rascher
AU - Bastian Siegmann
AU - Patricia de Rosnay
AU - Jean-Christophe Calvet
PY - 2026
DA - 2026/09/23
JO - Biogeosciences
DO - 10.5194/bg-23-6687-2026
UR - https://meteofrance.hal.science/meteo-05761333v1
AB - Accurate representations of the surface and vegetation are critical for simulating the terrestrial CO2 cycle in response to climate and meteorological conditions. To meet this challenge, an increasing number of satellite missions are being launched which can monitor vegetation conditions and biomass. One is the Copernicus Sentinel-5P mission, which carries the TROPOMI instrument and retrieves Sun-Induced Chlorophyll Fluorescence (SIF). As an indicator of plant photosynthetic activity, SIF can provide critical information for evaluating and parameterising gross carbon flux dynamics in surface models. This study aims to assimilate TROPOMI SIF data into the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model developed by Meteo-France, with the objective of directly correcting the representation of Leaf Area Index (LAI) and Gross Primary Production (GPP). To achieve this, we have developed a dedicated observation operator that links the modelled LAI to the TROPOMI SIF daily product. This neural network (NN) operator was developed using deep learning and was trained using observations over Europe. This operator achieved good accuracy, was implemented in a land data assimilation system (LDAS), and was used to assimilate TROPOMI SIF in ISBA using a sequential simplified extended Kalman filter. Specific experiments were conducted to study the assimilation process over the Ebro basin in Spain. This area is known for its irrigated croplands, which are not well represented by ISBA. Some experiments assimilate TROPOMI SIF, some assimilate a Copernicus Land Monitoring Service (CLMS) LAI 10 d product, and some assimilate both. This provided us with a useful point of reference for improving the vegetation simulation. Following SIF assimilation, LAI representation improved across the domain, highlighting heavily irrigated croplands. The gross primary production (GPP) derived from the analysis is closing the gap between the simulated and observed values, though a marked difference remains. When compared with other assimilation experiments, assimilating SIF alone provides a similar benefit to standard assimilation of a CLMS LAI product on LAI and GPP. The best improvements to the LAI and GPP results come from co-assimilating TROPOMI SIF with the CLMS LAI product, which combines the advantages of high-frequency SIF observations and robust 10 d LAI assimilation.
SN - 1726-4170
SN - 1726-4189
ER -