This paper aims at estimating the yield of sunflower at field scale by testing different ways to control an agrometeorological model, using the leaf area index (LAI) and/or the dry mass (DM) of the crop. The LAI is derived from multitemporal satellite images acquired in the visible, near-infrared, and microwave wavelengths by five sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2), over a region located in southwestern France, while the DM is measured in situ. Results show that the temporal dynamics of LAI and DM can be accurately simulated over the five studied working farms (R _DM ² > 0.85 and R _LAI ² > 0.94 with a relative root-mean-square error (RMSE) <; 30%), when the model is controlled by both the DM (measured over only one field) and the LAI (estimated from the modified triangular vegetation index (MTVI2) or σ° _{C -VV} ). The seed yield is then accurately estimated (R ² > 0.85) over 140 ha, with low errors ranging from 0.20 to 0.54 t·ha ^-1 by using both the LAI derived from microwave and optical data (LAI _MTVI2 and LAI _σ°C-VV ), in order to reduce the overestimations related to the excessive development of the LAI during the first phenological stages, or to the presence of dew on the vegetation, respectively, affecting the LAI derived from the MTVI2 or σ° _{C -VV} .

Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r²) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha⁻¹, surpassing results obtained from methods using single-image snapshots (r² of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application.

Today societies face an unprecedented challenge to limit global warming and restore agricultural soils. Recent studies show that the introduction of cover crops over Europe could result in a cooling impact due to an increase in soil organic carbon stocks, a decrease in the use of fertilizers, and an increase in surface albedo of the croplands. Based on the use of remote sensing data, land cover database, meteorological data, national agricultural statistics, and ground measurements, a generic model was developed to simulate the radiative forcing following the change in surface albedo. This article analyzes the impact of the introduction of cover crops in Europe during the fallow periods. Compared to previous studies, this work discusses: (i) The maximum greening potential in Europe and the associated indirect surface properties changes (ii) for snowfall episodes, and (iii) due to an increase in organic matter. This study shows that the mitigation potential of cover crops through albedo effects could reach 6.74 MtCO₂-eq.yr⁻¹ by extending the periods of the introduction of the cover crops to all possible fallow periods. This mitigation could be limited to 5.68 MtCO₂-eq.yr⁻¹ if the impact of snowfalls is considered. This would be equivalent to 9.12 gCO₂.m⁻².yr⁻¹. Finally, this study investigates the feedback loop due to soil darkening with soil organic carbon content increase when cover crops are introduced, considering two scenarios. The first considers the soil organic carbon content increase following repeated incorporation of cover crop biomass into the soil, simulated with the DayCent model. The second, more conceptual and extreme scenario aims at alerting on the possible impact of a combination of carbon farming practices, such as biochar or organic amendments. Our results show that this effect could lead to a loss of 20% of the climate benefit (i.e., 5.39 MtCO₂-eq.yr⁻¹). In conclusion, this study shows that cover crops have a strong potential for climate mitigation through direct albedo effects (soil coverage). However, once introduced, cropland should be permanently covered by vegetation or straws in order to avoid this darkening feedback loop effect.

The aim of this article is to evaluate the potential of optical and multi-polarization SAR images for soybean yield estimation by their assimilations into a simple agro-meteorological model. Satellite and ground data were acquired over two sites during the MCM'10 experiment. Optical and radar images were provided by Formosat-2, Spot-4, Spot-5 and Radarsat-2 satellites during the whole vegetation cycle of soybean. Results show that the assimilation of optical or SAR offer similar performances for the estimation of crop parameters (i.e. LAI and dry biomass) and crop yield (rRMSE = 18% in the worst case). Concerning SAR data, results highlighted the interest of using backscattering coefficients acquired at VV polarization (rRMSE = 2%).

In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated σ⁰_HH and σ⁰_VV) and cross-polarization states (abbreviated σ⁰_HV and σ⁰_VH) and at incidence angles ranging from 24° to 53°. Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4–35.3%), root-mean-square height (range: 0.5–7.9cm) and clay fraction (range: 9–58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters.

This study analyzes the potential of optical and radar satellite data to monitor faba bean (Vicia faba L.) phenology over six years (2016–2021) in southwestern France. Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ⁰_VV, γ⁰_VH, and γ⁰_VH/VV) are examined to assess crop growth, detect anomalies, and evaluate the impact of climatic conditions and sowing strategies. The results show that NDVI and the radar ratio (γ⁰_VH/VV) were suited to monitor faba bean phenology, with distinct growth phases observed annually. NDVI provides a clear seasonal pattern but is affected by cloud cover, while radar backscatter offers continuous monitoring, making their combination highly beneficial. The signal γ⁰_VH/VV exhibits well-marked correlations with NDVI (r = 0.81) and LAI (r = 0.83), particularly in orbit 30, which provides greater sensitivity to vegetation changes. The analysis of individual fields (inter-field approach) reveals variations in sowing strategies, with both autumn and spring plantings detected. Fields sown in autumn show early NDVI (and γ⁰_VH/VV) increases, while spring-sown fields display delayed growth patterns. This study also highlights the impact of climatic factors, such as precipitation and temperature, on inter-annual variability. Moreover, faba beans used as an intercropping species exhibit a shorter and more intense growth cycle, with a rapid NDVI (and γ⁰_VH/VV) increase and an earlier end of the vegetative cycle compared to standard rotations. Double logistic modeling successfully reconstructs temporal trends, achieving high accuracy (r > 0.95 and rRMSE < 9% for γ⁰_VH/VV signals and r > 0.89 and rRMSE < 15% for NDVI). These double logistic functions are capable of reproducing the differences in phenological development observed between fields and years, providing a reference set of functions that can be used to monitor the phenological development of faba beans in real time. Future applications could extend this methodology to other crops and explore alternative radar systems for improved monitoring (such as TerraSAR-X, Cosmos-SkyMed, ALOS-2/PALSAR, NISAR, ROSE-L…).