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.

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.

Although croplands are affected by climate change and contribute to it, their carbon budgets remain little known because of the lack of tools allowing estimating it at a fine resolution. In this study a modeling approach is proposed, assimilating high spatial and temporal resolution (HSTR) remote sensing optical products in a semi-physical crop model called SAFY-CO 2 to estimate crop production and the components of the carbon budget. The model correctly reproduces the green area index (GAI $) (\mathrm{R}^{2} =0.98$, rRMSE =8%), the biomass $(\mathrm{R}^{2} =0.95$, rRMSE =16%) and the components of the carbon budget (GPP: $\mathrm{R}^{2} =0.90$; RMSE =1.61 gC$.\mathrm{m}^{-2}. \mathrm{d}^{-1} ; \mathrm{R}_{eco} : \mathrm{R}^{2} =0.87$; RMSE $=0.81$ gC$.\mathrm{m}^{-2}. \mathrm{d}^{-1}$ gC$.\mathrm{m}^{-2}. \mathrm{d}^{-1}$; NEE: $\mathrm{R}^{2} =0.86$; RMSE =0.85 gC$.\mathrm{m}^{-2}. \mathrm{d}^{-1})$. This approach requires a few input parameters and no information about management practices which makes it suitable for large scale application and monitoring, reporting and verification (MRV) approaches.