Estimation of Land Surface Temperature (LST) and Soil Moisture Index (SMI) using Satellite Image: A Case Study in BharatpurMunicipality, Chitwan, Nepal
Monitoring and predicting variations in land surface temperature (LST) and soil moisture index (SMI) using remote sensing technology and modeling methodologies has become essential for making knowledgeable choices regarding crop production, surface evaporation calculation, identification of potential groundwater, and sustain able land use practices. LANDSAT data has opened new possibilities for studying land processes through remote sensing. This study aims to estimate LST and SMI in the Bharatpur municipality, Chitwan, Nepal using ArcGIS software and Landsat 8 data. The four bands of Landsat 8 including band 4, band 5, band 10, and band 11 are used. Running various empirical formulas including normalized difference vegetation index, atmosphere radiance, satellite brightness temperature, land surface emissivity and using the Landsat bands within ArcGIS, processing on the geographic coordinate system (World Geodetic System 1984) and the projected coordinate system UTM (Universal Transverse Mercator) zone 45 N, facilitates the estimation of both land surface temperature and soil moisture index. This research concentrates on the ArcGIS raster function and raster calculation using Landsat 8 imagery with a 30-meter resolution captured in October 2021. The observation shows the variation in land surface temperature ranging from 20.6 ◦C to 33.7°C◦C, while the variation in soil moisture index ranged from null to 100%. The land surface temperature has a direct impact on land surface evaporation, soil moisture, and crop yield. Elevated land surface temperatures typically lead to higher rates of surface evaporation, resulting in reduced soil moisture levels and fertility. Consequently, this can potentially lead to a decline in crop production, the extent of which varies depending on the type of crop.
