The crop monitoring by soil adjusted vegetation index with remote sensing and geographic information systems

Abstract

Assessing vegetation health is essential for maintaining sustainable agricultural productivity and alleviating the effects of climate variability and urban development. This study assesses the efficacy of the Soil-Adjusted Vegetation Index (SAVI) in evaluating crop vitality and land-use changes in specific agricultural areas of Madurai District, Tamil Nadu. Landsat-8 surface reflectance data were analyzed utilizing Google Earth Engine (GEE), a cloud-based geospatial platform, to calculate SAVI values for the years 2019, 2022, and 2025. The preprocessing processes comprised cloud masking, radiometric correction, and image clipping to the research area boundary, hence ensuring accurate and spatially consistent outputs. SAVI results were categorized into vegetation health classifications and examined by geographic information systems (GIS) based visualization and zonal statistics. The findings indicated considerable spatiotemporal fluctuations in vegetation health, with enhancements noted in 2022 attributed to beneficial rainfall and irrigation methods, succeeded by reductions in 2025 associated with drought stress and accelerated urban development. Urbanizing regions such as Keelakarai and Mulipallam saw significant vegetation decline, whilst locations like Thumbaipatti displayed resilience, underscoring the importance of efficient water management. The integration of SAVI with other indices, such as NDVI and NDBI, offered a more thorough comprehension of land-cover dynamics. Notwithstanding constraints such as poor image resolution and restricted ground validation, the work highlights the efficacy of remote sensing and GIS-based methodologies for assessing vegetation health. The findings underscore the necessity of ongoing vegetation monitoring to facilitate precision agriculture, sustainable land management, and climate-resilient urban planning in swiftly evolving agro-ecological environments.