A COMPARATIVE ANALYSIS OF REVENUE-BASED LAND INFORMATION SYSTEM INTEGRATING SENTINEL-2 AND PLANET IMAGERY FOR CROP CLASSIFICATION

Abstract

The study presents a revenue-based land information system integrated with the crop information. In this study, Sentinel-2 and Planet imagery have been used for crop classification using supervised classification. The accuracy attained from the Planet image was 90.67% and 82% for Sentinel 2, respectively. The study finds that the rice crop was grown a significant portion in the study area. The result shows the Murabba and Khasra based information of the existing Land use and Land cover information and Planet data provides better adjustment with the cadastral data. This integration includes essential information for identifying crops at the Khasra level and revenue base estimation of crop yield for the particular land parcel.