Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)

Abstract

Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3–77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108–126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and accuracy assessment of LULC maps revealed that the SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.