Rice Yield Estimation Based on SAR and Meteorological Parameters

Abstract

Estimating food production, demand, and distribution allows for timely yield prediction, crucial for managing food security. In-depth research has been published in the literature on applying vegetation indices discovered using optical remote sensing observation for yield estimation. The fundamental limitation of optical remote sensing is that it cannot penetrate cloud cover, which may contaminate the data. Therefore, a novel approach has been studied in this study employing SAR images from Sentinel-1 to construct a regression model combining vegetation index derived from SAR images and climatic variables over a 6-year time series (2017 to 2022). It is evident from the findings that the predictor variables have a non-linear relationship with the yield, which a straightforward linear regression model cannot describe. Other regression models, such as Random Forest, could be more useful in explaining such a complicated and non-linear connection. When the Multiple Linear regression mode was used for testing, it was found that the R² value was to 0.918 and the MSE was 0.513 Mt/ha. When the RF regression mode was used for testing, it was found that the R² value increased to 0.918 and the MSE improved to 0.513 Mt/ha. Furthermore, observation showed a prediction error of around 0.353 Mt/ha when employing the Spatial Error model. Therefore, rice yield estimation has considerably improved when employed in a spatial model.