Integration of Landsat-8 OLI/TIRS and Sentinel-1A PolSAR for analyzing land surface temperature and its anomalies linked to ENSO in Surakarta, Indonesia

The increase in urban buildings leads to degraded vegetated areas, resulting in higher surface radiation and air temperatures. The rise of land surface temperature (LST) is also influenced by land cover changes and global climate related to the ENSO (El Nino-Southern Oscillation) phenomena. This study is the first to combine active and passive sensors to analyze LST anomalies linked to ENSO-related land cover change on a time series approach. Conducted from August 2018 to 2023 in an urban area dominated by buildings, we used Python programming to extract LST from the Landsat-8 OLI/TIRS passive sensor with a Mono-window algorithm. Meanwhile, the land cover classification was performed by Sentinel-1A active sensor imagery using polarimetric decomposition with unsupervised Wishart. The LST and land cover results were equalized to 30 m spatial resolution for regression and anomaly analysis based on reported ENSO phenomena. The results revealed that land cover type significantly affected LST variation during the study period, proven by the significance value of each land cover type being less than 0.05 and showing a positive correlation. However, the correlation is low, meaning that land cover change is not the dominant factor causing LST change. The low correlation caused by El Nino and La Nina, contributed more to the change in LST during the study period. The integrated method can overcome the weakness of passive sensors in penetrating clouds, contribute to a broader knowledge of the factors causing LST changes, and provide effective early mitigation strategies against the threat of future climate change crises. • This study combines Sentinel-1 PolSAR and Landsat-8 to analyze land surface temperature anomalies related to ENSO phenomena. • LST and land cover show positive but minimal correlations, indicating anomalies likely driven by ENSO-induced climate change. • Google Collab with Python was used to extract LST from Landsat 8 images. The code is shared to advance research.