Pierre Vanderbecken

The knowledge of land-surface variables (LSVs) is essential for an accurate description of the carbon cycle and, hence, for deriving the fraction of anthropogenic CO ₂ emissions contributing to global warming. For a better representation of LSVs like the leaf-area index (LAI), we assimilate the observations of the satellite microwave sensors SMAP and AMSR2 with our land-data assimilation system (LDAS) on global cropland. Dedicated observation operators using a neural network (NN) are developed to enable a direct assimilation of the measured brightness temperatures. Direct assimilation is not yet an established method due to the incomplete representation of physical emission processes and the associated computational constraints of physical observation operators. We derive an optimal set of predictors for both instruments resulting in a good match between model equivalent and the observations in the testing period, with correlations up to 0.87/0.93 and a total RMSE of 8.2/3.6 K for SMAP/AMSR2, respectively. The implementation of the derived weights into the LDAS is straightforward and is found to lead to a reasonably good performance of the assimilation system, with a stronger improvement of the departures for SMAP than for AMSR2, which can be attributed to the characteristics of the microwave bands of the observations. For the best assimilation experiments, the verification against LAI observations shows an improvement compared to the open loop on a global scale. In agricultural regions of the world that are particularly susceptible to drought, AMSR2 experiments outperform SMAP experiments in terms of LAI in most cases. We also investigated the impact on the Gross Primary Productivity (GPP) and soil-water index (SWI) anomalies and found relatively little impact with mixed results. Nevertheless, the seasonal and sub-seasonal variability is still not well represented due to systematic errors in our land surface model.

This work integrates new satellite observations into the Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model via data assimilation. This improves our understanding of the impact of droughts on forests and agricultural land at global and regional scales. First, innovative artificial intelligence methods are employed to generate observation operators, enabling ISBA to simulate satellite microwave radiances ranging from the L to X bands, as well as sun‐induced fluorescence (SIF) observations. Second, these observations are assimilated into ISBA sequentially. Examples of sequential data assimilation are presented for SIF and C‐band backscatter observations derived from the tropospheric monitoring instrument and the advanced scatterometer on board the Sentinel‐5P and MetOp satellites respectively. We also demonstrate how machine learning can be used to derive new variables from those calculated by the land surface model in order to monitor natural disasters related to drought, as well as to characterise the regional exposure to drought‐related hazards. We present a prototype regional application for monitoring and forecasting clay shrink–swell and wildfire danger in France.

Live Fuel Moisture Content (LFMC) is one of the most critical variables for understanding fire dynamics, particularly within forest environments. This study intends to develop a daily LFMC indicator to support operational fire danger management services in France. The product is based on in situ observations from the French National Forest Office and is generated using a lightweight expressive neural network model. The network has been designed to generalise well over time and space. It can be integrated directly into land surface models to enable real-time monitoring of vegetation’s hydric status. The modelling framework combines outputs from a physically based land surface model and satellite-derived leaf area index (LAI) observations, providing high-resolution, spatially consistent estimates of land surface over France. To evaluate the model’s generalisation capacity, we implemented complementary cross-validation strategies to test interannual robustness, spatial transferability, and to simulate an operational deployment scenario. Additionally, we performed a robustness analysis to quantify the sensitivity of predictions to training variability. The results demonstrate a strong ability to estimate the range and dynamics of LFMC across most of France. They also identify regions where additional in situ sampling or improved representation could reduce epistemic uncertainty and enhance the reliability of the model.

Accurate representations of the surface and vegetation are critical for simulating the terrestrial CO ₂ 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.

Live Fuel Moisture Content (LFMC) is one of the most critical variables for understanding fire dynamics, particularly within forest environments. This study intends to develop a daily LFMC indicator to support operational fire danger management services in France. The product is based on in situ observations from the French National Forest Office and is generated using a lightweight expressive neural network model. The network has been designed to generalise well over time and space. It can be integrated directly into land surface models to enable real-time monitoring of vegetation’s hydric status. The modelling framework combines outputs from a physically based land surface model and satellite-derived leaf area index (LAI) observations, providing high-resolution, spatially consistent estimates of land surface over France. To evaluate the model’s generalisation capacity, we implemented complementary cross-validation strategies to test interannual robustness, spatial transferability, and to simulate an operational deployment scenario. Additionally, we performed a robustness analysis to quantify the sensitivity of predictions to training variability. The results demonstrate a strong ability to estimate the range and dynamics of LFMC across most of France. They also identify regions where additional in situ sampling or improved representation could reduce epistemic uncertainty and enhance the reliability of the model.

Surface reanalyses are an important tool for examining past natural hazards, such as wildfires, sudden floods and droughts, in great detail and on a consistent scale. With the surge in deep learning, these reanalyses could contribute to the development of new tools for identifying, characterizing and anticipating such hazards. Météo-France has just conducted a 60-year reanalysis of Western Europe using the AROME numerical weather prediction model, known as ARRA. Here, we use these atmospheric conditions to reanalyse the continental surface of the same area using the Land Data Assimilation System (LDAS-Monde). ARRA reanalyses are used to drive the Interaction Surface Biosphere Atmosphere (ISBA) land surface model via the SURFEX platform, incorporating prognostic soil moisture, leaf area index and woody biomass at kilometer scale. These modeled variables are then corrected by assimilating Copernicus Land Monitoring Service LAI satellite data and ESA-CCI above-ground biomass satellite data using a simplified extended Kalman filter. The reanalysis period will range between 2000 and 2025 to coincide with CLMS LAI observation availability. Incorporating LAI into ISBA enhances the model's representation of the carbon and water cycles, as well as addressing agricultural practices not currently modeled by ISBA. We will additionally evaluate the impact on river discharge using CTRIP river routing.The additional focus on biomass in the reanalyses stems from the need to improve the representation of living fuel moisture content and dead biomass in our model, as these are two critical variables for providing an early warning of fire ignition. To this end, above-ground biomass will be assimilated to adjust the distribution of biomass between leaves, wood, and litter.

It is now widely recognized that assimilating consolidated satellite-derived vegetation products improves the representation of land surface variables and fluxes. This raises the question of their suitability for both reanalysis products and near-real-time (NRT) applications. Among these variables, Leaf Area Index (LAI) plays a key role in controlling exchanges of water, energy, and carbon between the land surface and the atmosphere. While reprocessed and consolidated products are well suited for retrospective reanalyses, NRT applications require observational streams with low latency and sufficient consistency with delayed, higher-quality products.In this study, we assess the potential of the Copernicus Land Monitoring Service (CLMS) 300 m LAI products for land surface analysis and NRT applications within the LDAS-Monde system coupled to the ISBA land surface model for the year 2021 in a global scale. We investigate the assimilation of the different CLMS processing streams, namely RT0 and RT1, available in NRT, and RT6, which is provided as a consolidated product with a 60-day latency. A set of global offline experiments forced by ERA5 is conducted to evaluate their consistency and suitability for land data assimilation.The results show that RT1 captures the main temporal variability of the consolidated RT6 product while benefiting from a substantially reduced latency. Compared with RT0, RT1 provides a more stable and consistent signal, offering a robust compromise between timeliness and data quality. Assimilation experiments within LDAS-Monde indicate that analyses driven by RT1 are consistent with those obtained using RT6, supporting its use in systems targeting NRT land monitoring. In addition, an in situ evaluation based on observations from the ICOS FR-Tou site is conducted for selected surface variables, providing an independent benchmark to further assess whether the use of RT1 leads to comparable performance to RT6.

The knowledge of land-surface variables (LSVs) is essential for an accurate description of the carbon cycle and, hence, for deriving the fraction of anthropogenic CO₂ emissions contributing to global warming. For a better representation of LSVs like the leaf-area index (LAI), we assimilate the observations of the satellite microwave sensors SMAP and AMSR2 with our land-data assimilation system (LDAS). Dedicated observation operators using a neural network (NN) are developed to enable a direct assimilation of the measured brightness temperatures. Direct assimilation is not yet an established method due to the incomplete representation of physical emission processes and the associated computational constraints of physical observation operators. We derive an optimal set of predictors for both instruments resulting in a good match between model equivalent and the observations in the testing period, with correlations up to 0.87/0.93 and a total RMSE of 8.2/3.6 K for SMAP/AMSR2, respectively. The implementation of the derived weights into the LDAS is straightforward and is found to lead to a reasonably good performance of the assimilation system, with a stronger improvement of the departures for SMAP than for AMSR2, which can be attributed to the characteristics of the microwave bands of the observations. For the best assimilation experiments, the verification against LAI observations shows an improvement compared to the open loop on a global scale. For different global cropland regions especially prone to droughts, AMSR2 outperforms SMAP in most cases. Nevertheless, the seasonal and subseasonal variability is still not well represented though due to unsolved issues in our model.

Live Fuel Moisture Content (LFMC) is one of the most critical variables for understanding fire dynamics, particularly within forest environments. This study intends to develop a daily LFMC indicator to support operational fire danger management services in France. The product is based on in situ observations from the French National Forest Office and is generated using a lightweight expressive neural network model. The network has been designed to generalise well over time and space. It can be integrated directly into land surface models to enable real-time monitoring of vegetation’s hydric status. The modelling framework combines outputs from a physically based land surface model and satellite-derived leaf area index (LAI) observations, providing high-resolution, spatially consistent estimates of land surface over France. To evaluate the model’s generalisation capacity, we implemented complementary cross-validation strategies to test interannual robustness, spatial transferability, and to simulate an operational deployment scenario. Additionally, we performed a robustness analysis to quantify the sensitivity of predictions to training variability. The results demonstrate a strong ability to estimate the range and dynamics of LFMC across most of France. They also identify regions where additional in situ sampling or improved representation could reduce epistemic uncertainty and enhance the reliability of the model.