Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation

Purpose Crop growth models (CGM) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. Methods A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. Results The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to reduce ensemble spread and improve robustness. Real-case results confirmed these findings. DA assimilation of SM especially contributed to improving overall biomass accuracy, particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation, the joint assimilation yielded consistent results. In 2022, it achieved a root-mean-square error (RMSE) of 2275.20 kg/ha, a normalized RMSE (nRMSE) of 44.99%, and a bias of 1081.90 kg/ha. In 2023, RMSE, nRMSE and bias were 1120.29 kg/ha, 14.79%, and 284.05 kg/ha, respectively. Furthermore, the joint assimilation led to a tighter ensemble spread than single source-assimilation. Conclusion The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.