Proceedings Article | 11 October 2024
KEYWORDS: Vegetation, Agriculture, Rain, Crop monitoring, Analytical research, Soil moisture, Satellites, Environmental monitoring, Soil science, Satellite imaging
Monitoring crops is challenging with the help of traditional methods like manual surveys and crop-cutting experiments, which are time-consuming and require efforts to cover a larger area. Therefore, satellite remote sensing plays an imperative role in agricultural crop monitoring and analysis with the help of desktop-based software tools. Moreover, downloading and analysing satellite imagery is tedious and time-consuming. It requires high computational power and storage space for experimental operations like preprocessing, classification, and visualization of the huge dataset. Additionally, the cost of remote sensing software licenses is very high, whereas computer systems need to be more robust to perform time-series analysis of satellite imagery. Therefore, Google Earth Engine (GEE) provides geoprocessing capabilities for the timeseries dataset in cloud computing. In this research study, we have used vegetation indicators like the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index (SAVI), and the Modified Normalized Difference Water Index (MNDWI) for the crop condition and surface water bodies analysis of the Vaijapur Tehsil for three years from 2021 to 2023. The study shows that The NDVI of the year 2023, categorized as non-vegetation land, damaged, good, and moderate vegetation land, increased by 3.68%, 2.62%, 2.29%, and 1.15%, respectively, while healthy land decreased by 4.60%. The SAVI of 2023 indicated that soil moisture in non-vegetation and moderate land decreased by 1.94% and 4.57%, respectively, while the damaged cropland and good vegetation land increased by 4.62% and 1.86%. The MNDWI of the year 2023 showed that water bodies decreased by 54.96%.
