Towards Fine-Scale Yield Prediction of Three Major Crops of India Using Data from Multiple Satellite

There is enormous scope and prospective of crop yield prediction at finer scale for both farm-level crop management as well as for crop insurance settlement at gram panchayat (GP) level in India. Now with the advent of satellite sensors like the MSI from Sentilnel-2 with fine spatial resolution, the possibility of generating frequent information on crop condition at field scale is increasing. This study demonstrated the combined use of high-resolution data from Sentinel-2 (10 m and 20 m); moderate-resolution data from MODIS (500 m) and coarser-resolution radiation data from INSAT-3D (4 km) for estimating yield of three major crops of India at GP and taluka level using a semi-physical model based on crop-specific radiation use efficiency. The novelty of this study lies in the data fusion approach using parameters from multiple spatial resolution of Geostationary and Lower Earth Orbiting satellites within the basic semi-physical model framework. The methodology has been demonstrated in Cuttack district of Odisha for rice; Rajkot district of Gujarat for cotton; and Indore district of MP and Fatehabad district of Haryana for wheat. We validated our result at GP, taluka and district level. At GP level, the root mean square error (RMSE) was found to be 16.5% for rice and 5.8% for wheat in Indore district. At taluka level, the RMSE was found to be 15%, 5.7%, 4.4% and 7.4% for rice, wheat in Indore district, wheat in Fatehabad district and cotton, respectively. The study concluded that high resolution remote sensing data would be of immense use for finer scale yield estimation, which can be aggregated at GP and taluka level with satisfactory accuracy (p = 95%).