Seasonal Analysis of Vegetation, Moisture, Urbanization, and Land Surface Temperature (LST) Using NDVI, NDMI, NDWI, and NDBI Indices: A Case Study of Sillod, Maharashtra

The study evaluates seasonal variations in land surface temperatures (LST) and their relationship with key remote sensing indices that are the NDBI, NDVI, NDW, and NDMI. These indices help analyse LULC effects on LST using cloud-free Landsat 8 imagery from summer, winter, and rainy seasons of 2023. The focus is taluka Sillod, Dist. Chhatrapati Sambhajinagar, Maharashtra, India. LST and index values are influenced by cropping systems, vegetation cover, planting density, and growing season. NDVI is key for assessing vegetation health, NDMI monitors moisture, NDBI maps urban areas, and NDWI detects water bodies. Each index has strengths but faces challenges like atmospheric interference and mixed land cover classification. Recent advancements in high-resolution data and machine learning integration offer promising improvements for future applications in environmental monitoring.