Modeling and Estimating the Land Surface Temperature

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 3
• Main Authors: Mansourmoghaddam, Mohammad, Rousta, Iman, Ghafarian Malamiri, Hamidreza, Sadeghnejad, Mostafa, Krzyszczak, Jaromir, Ferreira, Carla Sofia Santos
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-01-2024
• Subjects: Algorithms; Case studies; Data mining; Decision-making; Earth resources technology satellites; Global warming; Machine learning; Remote sensing; Urban climatology; Urban heat islands; Iran; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16030454

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.