Studying the Impact of LULC Correspondence Between Landsat 8 and Spot 7 Data on Land Surface Temperature Estimation

Information about land surface temperature (LST) plays a crucial role in environmental studies, as it provides essential data for understanding climate patterns, assessing ecosystem health, and predicting environmental changes. Understanding the relationship between land cover types and LST is crucial across all disciplines that deal with LST data. It helps researchers identify trends in global warming, heatwaves, and cooling effects, which can influence biodiversity, agriculture, and water resources. The accuracy of LST calculations heavily depends on the quality of the data used. However, most satellite thermal data used for LST estimations are in coarse spatial resolution. This study aims to explore the complex interaction between land cover types, considering factors such as proportion and neighboring effects, and LST recalculation by integrating the estimated LST from Landsat thermal band and Spot imagery classification. A machine learning model was employed to quantify the contribution of each Spot pixel to the LST estimated from TIRS data, classifying it as either heating or cooling. The Al Morjan and Al Hamra districts in Jeddah, Saudi Arabia, were used as case studies. The results showed that Spot images achieved a classification accuracy of over 95%, whereas Landsat images did not exceed 77%. The average heating and cooling factors from neighboring pixels were 1.06 and 0.96, respectively. The study demonstrates the improved spatial distribution of LST, with overall temperature increases across all land cover classes. The findings of this study could aid in identifying environmental imbalances and developing effective solutions.