Assessing land surface temperature variability over a large region: A temporal consistency approach using a single day satellite imagery

Abstract Land surface temperature (LST) is a vital parameter in various scientific disciplines, including climate studies, environmental monitoring, and urban planning. This study focuses on the crucial parameter of LST and its diverse applications in understanding Earth's dynamic systems. The study addresses the limitations of traditional LST measurement methods and emphasizes the importance of satellite remote sensing for large-scale monitoring. It explores the impact of land use and land cover changes on LST, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to achieve consistent atmospheric conditions, reducing uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST predictions during both summer and winter seasons. Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.