Improving the UAV-based yield estimation of paddy rice by using the solar radiation of geostationary satellite Himawari-8

The objectives of this study were to improve the yield estimation of paddy rice based on the unmanned aerial vehicle remote sensing (UAV-RS) and solar radiation data sets. The study used the UAV-RS-based normalized difference vegetation index (NDVI) at the heading stage, the solar radiation data of geostationary satellite Himawari-8 and the solar radiation data of polar orbiting satellite Aqua/MODIS. A comparison of two satellite-based solar radiation data sets (Himawari-8 and MODIS PAR) showed that the coefficient of determination (R2) of estimated yield based on Himawari-8 solar radiation was 0.7606 while the R2 of estimated yield based on the MODIS PAR was 0.4749. Additionally, the root mean square error (RMSE) of Himawari-8 solar radiation was 26.5 g/m2 while the RMSE of estimated yield based on the MODIS PAR was 39.2 g/m2 (The average observed yield was 489.3 g/m2). The Estimated yield based on Himawari-8 solar radiation, therefore, outperformed the MODIS PAR-based estimated yield. The improvement of the temporal resolution of the satellite-based dataset allowed by using the Himawari-8 data set contributed to the improvement of estimation accuracy. Satellite-based solar radiation data allow yield estimation based on remote sensing in regions where there are no ground observation data of solar radiation.