Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)
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 c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/3/454 |
| _version_ | 1797318273383006208 |
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| author | Mohammad Mansourmoghaddam Iman Rousta Hamidreza Ghafarian Malamiri Mostafa Sadeghnejad Jaromir Krzyszczak Carla Sofia Santos Ferreira |
| author_facet | Mohammad Mansourmoghaddam Iman Rousta Hamidreza Ghafarian Malamiri Mostafa Sadeghnejad Jaromir Krzyszczak Carla Sofia Santos Ferreira |
| author_sort | Mohammad Mansourmoghaddam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:50:04Z |
| format | Article |
| id | doaj.art-aa6bbcde4b2c4e24b8d66bb65195a8fc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T03:50:04Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-aa6bbcde4b2c4e24b8d66bb65195a8fc2024-02-09T15:21:09ZengMDPI AGRemote Sensing2072-42922024-01-0116345410.3390/rs16030454Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)Mohammad Mansourmoghaddam0Iman Rousta1Hamidreza Ghafarian Malamiri2Mostafa Sadeghnejad3Jaromir Krzyszczak4Carla Sofia Santos Ferreira5Center for Remote Sensing and GIS Research, Shahid Beheshti University, Tehran 1983969411, IranDepartment of Geography, Yazd University, Yazd 8915813135, IranDepartment of Geography, Yazd University, Yazd 8915813135, IranDepartment of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS 66506-2904, USAInstitute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, PolandDepartment of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, SwedenThe 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.https://www.mdpi.com/2072-4292/16/3/454land surface temperature modelingland surface parametersmachine learninggradient boosting method |
| spellingShingle | Mohammad Mansourmoghaddam Iman Rousta Hamidreza Ghafarian Malamiri Mostafa Sadeghnejad Jaromir Krzyszczak Carla Sofia Santos Ferreira Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) Remote Sensing land surface temperature modeling land surface parameters machine learning gradient boosting method |
| title | Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) |
| title_full | Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) |
| title_fullStr | Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) |
| title_full_unstemmed | Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) |
| title_short | Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran) |
| title_sort | modeling and estimating the land surface temperature lst using remote sensing and machine learning case study yazd iran |
| topic | land surface temperature modeling land surface parameters machine learning gradient boosting method |
| url | https://www.mdpi.com/2072-4292/16/3/454 |
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