Vegetation indices based farm-level mustard crop classification for the analysis of cropping pattern in Rabi 2021 and change in crop trend 2019 to 2021 of Kota District, Rajasthan

Pithani Venkatesh, Abhishek Misra, Ashutosh Panda, Prashant Bagade, Nalin Rawal, Mukesh Kumar

Abstract


Remote sensing technology is used to quickly investigate as an innovative, standardized, potentially cost-effective, and faster method for crop acreage estimation. Furthermore, when compared to previous monitoring systems, Sentinel-2 satellite data has tremendous advantages since it delivers five-day interval, topographical, and up-to-date crop info at multiple phases. The main Rabi oil seed crop in Rajasthan is rapeseed and mustard. This study explores the use of the time series NDVI based farm level acreage estimation depending on the condition of the chlorophyll content. It also studies the changes in the cropping patterns and trends in Kota district, Rajasthan using the Google Earth Engine cloud platform along with the NCMS Mobile application for ground truth. Results indicate the reliability of the developed method for estimating acreage down to the farm level. Estimated Results for found to be in close agreement with authenticated government data. Two of the studied sub-districts showed significant cropping patterns. Classification accuracy for mustard ranged between 78-90 percent, while the overall classification accuracy 80-90 percent. The study concludes with the use of technology-based acreage estimations for faster and more reliable results.

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