气候变化领域集成服务门户(CCPortal): Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

CCPortal

DOI	10.3390/rs16030454
	Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)
	Mansourmoghaddam, Mohammad; Rousta, Iman; Ghafarian Malamiri, Hamidreza; Sadeghnejad, Mostafa; Krzyszczak, Jaromir; Ferreira, Carla Sofia Santos
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	3
卷号	16 期号:3
英文摘要	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.
英文关键词	land surface temperature modeling; land surface parameters; machine learning; gradient boosting method
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001160044300001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303453
作者单位	Shahid Beheshti University; University of Yazd; University of Iceland; Kansas State University; Polish Academy of Sciences; Bohdan Dobrzanski Institute of Agrophysics of the Polish Academy of Sciences; Stockholm University; Stockholm University
推荐引用方式 GB/T 7714	Mansourmoghaddam, Mohammad,Rousta, Iman,Ghafarian Malamiri, Hamidreza,et al. Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)[J],2024,16(3).
APA	Mansourmoghaddam, Mohammad,Rousta, Iman,Ghafarian Malamiri, Hamidreza,Sadeghnejad, Mostafa,Krzyszczak, Jaromir,&Ferreira, Carla Sofia Santos.(2024).Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran).REMOTE SENSING,16(3).
MLA	Mansourmoghaddam, Mohammad,et al."Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)".REMOTE SENSING 16.3(2024).