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Remote Sensing
Volume 17
Issue 14
10.3390/rs17142392
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Article

Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery

by
Ratovoson Robert Andriambololonaharisoamalala
1,*,
Petra Helmholz
1,
Dimitri Bulatov
2,
Ivana Ivanova
1,
Yongze Song
3,
Susannah Soon
4 and
Eriita Jones
1
1
School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia
2
Institute of Optronics, System Technologies and Image Exploitation (IOSB), 76131 Karlsruhe, Germany
3
School of Design and the Built Environment, Curtin University, Perth, WA 6845, Australia
4
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(14), 2392; https://doi.org/10.3390/rs17142392
Submission received: 18 June 2025 / Revised: 5 July 2025 / Accepted: 9 July 2025 / Published: 11 July 2025
(This article belongs to the Special Issue Remote Sensing Applications in Urban Environment and Climate)
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Abstract

Urban surface temperatures are increasing because of climate change and rapid urbanisation, contributing to the urban heat island (UHI) effect and significantly influencing local climates. Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. This limitation affects the assessments of UHI effects and hinders effective mitigation strategies. We proposed a hybrid model named “geospatial machine learning” (GeoML) to address these challenges, combining random forest and kriging downscaling techniques. This method utilises high spatial resolution data from Sentinel-2 to enhance the LST derived from Landsat 8/9 data. Tested in Perth, Australia, GeoML generated an enhanced LST with good agreement with ground-based measurements, with a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) of 2.7 °C, and a mean absolute error (MAE) of less than 2.2 °C. Validation with LST derived from another TIR sensor also provided promising outputs. The results were compared with the high-resolution urban thermal sharpener (HUTS) downscaling methods, which GeoML outperformed, demonstrating its effectiveness as a valuable tool for urban thermal studies involving high-resolution LST data.
Keywords: land surface temperature; downscaling; urban areas; random forests; kriging; ground-based data; validation land surface temperature; downscaling; urban areas; random forests; kriging; ground-based data; validation

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MDPI and ACS Style

Andriambololonaharisoamalala, R.R.; Helmholz, P.; Bulatov, D.; Ivanova, I.; Song, Y.; Soon, S.; Jones, E. Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery. Remote Sens. 2025, 17, 2392. https://doi.org/10.3390/rs17142392

AMA Style

Andriambololonaharisoamalala RR, Helmholz P, Bulatov D, Ivanova I, Song Y, Soon S, Jones E. Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery. Remote Sensing. 2025; 17(14):2392. https://doi.org/10.3390/rs17142392

Chicago/Turabian Style

Andriambololonaharisoamalala, Ratovoson Robert, Petra Helmholz, Dimitri Bulatov, Ivana Ivanova, Yongze Song, Susannah Soon, and Eriita Jones. 2025. "Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery" Remote Sensing 17, no. 14: 2392. https://doi.org/10.3390/rs17142392

APA Style

Andriambololonaharisoamalala, R. R., Helmholz, P., Bulatov, D., Ivanova, I., Song, Y., Soon, S., & Jones, E. (2025). Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery. Remote Sensing, 17(14), 2392. https://doi.org/10.3390/rs17142392

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