Analysis of Variations on Land Surface Temperature (LST) and Total Column Water Vapor (TCWV) in East Java using Sentinel-3 Satellite

The study of spatiotemporal variations of Land Surface Temperature (LST) and Total Column Water Vapor (TCWV) in East Java Province from 2021 to 2023 using Sentinel-3 SLSTR satellite imagery is the subject of this research. This study aims to assess the seasonal climate dynamics in the tropical monsoon region and evaluate the statistical relationship between LST and atmospheric water vapour content. The remote sensing data from the Sea and Land Surface Temperature Radiometer (SLSTR) were processed to produce multitemporal LST and TCWV maps. Furthermore, the Split-Window Algorithm was utilized to obtain LST, while TCWV was obtained using the Ocean and Land Colour Instrument (OLCI) through differential absorption techniques. The data analysis employed a combination of temporal aggregation, spatial mapping techniques, and simple linear regression to evaluate the relationship between LST and TCWV. The results indicate that seasonal variations are significant. LST values peak during the dry season, particularly in September 2023, coinciding with the El Niño-induced drought, and TCWV reaches its maximum during the wet season. However, the statistical relationship between the two variables is weak, with a coefficient of determination (R²) of 0.022. This suggests that only 2.2% of LST variability can be explained by TCWV, indicating the presence of more influential surface and atmospheric factors, including land cover, vegetation density, and topographic complexity. The results of this study demonstrate the utility of Sentinel-3 data for regional climate monitoring and indicate the limitations of utilizing TCWV as the primary predictor of LST variability. The conclusions of this study demonstrate the necessity for an integrated multiparameter approach in future drought and climate assessments in the tropics.