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

Kusum, Sumit Kumar, Reenu Sharma, S. S. Hassan, B. Pateriya
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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.
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基于收益的sentinel-2与行星影像作物分类土地信息系统比较分析
本研究提出了一种基于收入的土地信息系统,该系统与作物信息相结合。在本研究中,利用Sentinel-2和Planet图像对作物进行监督分类。哨兵2号从行星图像获得的精度分别为90.67%和82%。研究发现,水稻作物在研究区域中占有相当大的比例。结果表明,基于Murabba和Khasra的现有土地利用和土地覆盖信息以及Planet数据可以更好地与地籍数据进行调整。这种整合包括识别Khasra级别作物的基本信息和特定地块作物产量的收入基础估计。
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