Assimilating vegetation indices and canopy cover derived from remote sensing into the AquaCrop model for estimating rice yield

Abstract Accurate forecasting of rice yield using contemporary data is a paramount management tool. To achieve this objective, the AquaCrop, in conjunction with remote sensing (RS) data, was employed to predict yield in the paddy fields of SANRU. The required AquaCrop data, including ground (200 locations) and RS (Sentinel‐2, MODIS) data, were collected in two seasons, namely, 2020 and 2021. Leveraging Sentinel‐2 imagery, various vegetation indices (VIs) were computed, encompassing the normalized difference vegetation index (NDVI), rice growth vegetation index (RGVI) and soil adjusted vegetation index (SAVI). Additionally, MODIS and spatio‐temporal fusion algorithms (Spatial and Temporal Adaptive Reflectance Fusion Model [STARFM] and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model [ESTARFM]) were used to create a regular time pattern in satellite images taken on cloudy days. The results revealed that the yields observed for 2020 and 2021, on the basis of field data, were 4450 and 4370 kg/ha, respectively. Moreover, by leveraging the AquaCrop, the forecasted yield was ascertained both with and without the assimilation of RS data. Notably, the findings underscored that the incorporation of RS data significantly enhanced the model's predictive precision, particularly in estimating yield. The model's efficacy was demonstrated by its ability to forecast the end‐of‐season yield for the years 2020 and 2021, which yielded maximum RMSEs of 400 and 470 kg/ha, respectively.