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

Abstract

Crop growth models (CGMs) 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. 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. 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 provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg/ha, 16.62%, and - 220.52 kg/ha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.