Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km²) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68–2.38 gC m⁻², R²=0.87–0.77; biomass: RMSE =11.34 g m⁻², R²=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias $=-\mathrm{47}$ g m⁻², −39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes.

Ukraine was the seventh-biggest worldwide producer of wheat in 2021 before the Russian invasion. The war caused a disruption of the global wheat market but the impact on the different levels of the production chain remains unclear. Here we leverage high resolution satellite imagery and crop modeling to analyze specifically the impact of the war on the evolution of the dry aboveground biomass in wheat fields. We compare the Kherson region, a battlefield occupied by the Russian army in 2022 with the Poltava region which was not invaded. We also compare the wheat biomass in both regions in 2022 with the two previous years before the war. We find that the wheat biomass sharply declined in the Kherson region in 2022 only. We interpret this result as a consequence of the abandonment of invaded land by farmers. The lack of agricultural management, in particular the lack of fertilization and irrigation could explain the reduced crop growth in summer, thereby contributing to shortages in the wheat production chain.

Remote sensing is commonly employed in agriculture for crop monitoring and environmental studies. However, the accuracy of satellite-based products is impacted by image frequency, spectral sensibility and density, and cloud cover. This study evaluates the impact of assimilating Planet Fusion (PF) data into the AgriCarbon-EO (ACEO) crop modeling chain, alone or combined with Sentinel-2 (S2) data, compared to assimilating (S2) data only. Parallel experiments were conducted using PF data alone, or PF data in addition to S2 data in the simulations. Satellite data are used to estimate the Green Leaf Area Index (GLAI), CO₂ flux dynamics, biomass and yields of winter wheat, maize and cover crops in south-west France. We analyzed the data availability and spectral resolution impact when using PF alone, a combination of PF and S2 data, and S2 alone. We demonstrate that PF’s lack of red-edge spectral information can lead to GLAI overestimation during vegetation growth. This is mitigated by applying a statistical correction derived from S2-based GLAI. Assimilating both S2 and PF reduces the Root Mean Square Error (RMSE) and bias for biomass estimates by 19.75 g.m⁻² and 117.78 g.m⁻², respectively, in maize and by 2.15 g.m² and 13.19 g.m⁻² in cover crops, compared to using S2 data alone. However, it did not improve wheat CO₂ flux components estimates. We show that the crop biomass accuracy is more strongly affected by the spectral resolution than by image frequency. The synergistic use of S2 and PF data demonstrates potential for improving biomass accuracy, particularly in cloud-prone regions.

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation.

Soil carbon storage is a well identified climate change mitigation solution. The extensive in-situ monitoring of the soil carbon storage in cropland for agricultural policy and offset carbon markets is prohibitive, especially at intra-field scale. For this reason, comprehensive Monitoring, Reporting and Verification (MRV) of soil carbon and its explanatory variables at large scale needs to rely on remote sensing and modelling tools that provide the spatio-temporal dynamics of the carbon budget and it’s components at high resolution with associated uncertainties. In this paper, we present AgriCarbon-EO v1.0: an end-to-end processing chain that enables the estimation of carbon budget components of major crops and cover crops at intra-field resolution (10 m) and large scale (over 110×110 km) by assimilating remote sensing data in physically-based radiative transfert and agronomic models. The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines Normalised Importance Sampling (NIS) and Look-Up Table (LUT) generation. This approach propagates the 10 m uncertainties across the processing chain from the reflectances to the output variables. The chain considers as input a land cover map, multi-spectral reflectance maps from the Sentinel-2 and Landsat-8 satellites, and daily weather forcing. The PROSAIL radiative transfer model is inversed in a first step to obtain Green Leaf Area Index (GLAI). The GLAI time series are then assimilated into the SAFYE-CO2 crop model taking into consideration their uncertainty. The chain is applied over winter wheat in the south-west of France during the cropping seasons 2017 and 2019. We compare the results against the net ecosystem exchange measured at the FR-AUR ICOS site (RMSE = 1.69 - 2.4 gC m⁻² , R² = 0.88 - 0.88), biomass (RMSE = 250 g m⁻² , R² = 0.9), and combine harvester yield maps. We quantify the difference between pixel and field and pixel scale simulations of biomass (bias = -47 g m⁻² , -39 % variability), and the impact of the number of remote sensing acquisitions on the outputs (-66 % of mean uncertainty of biomass). Finally, we conduct a coherency analysis at regional scale to test the consistency of the observed patterns with soil texture, altitude and exposition variability. Results show higher biomass for higher clay soils and earlier emergence and senescence for south western exposition.