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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geoinformatics
Volume 12 - 2024 | doi: 10.3389/feart.2024.1488711
This article is part of the Research Topic Applications of Remote Sensing Over Plateau Mountainous Areas View all articles

A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas

Provisionally accepted
Ju Wang Ju Wang 1*Bo-Hui Tang Bo-Hui Tang 1,2Xinming Zhu Xinming Zhu 1Dong Fan Dong Fan 1Menghua Li Menghua Li 1Junyi Chen Junyi Chen 1
  • 1 Kunming University of Science and Technology, Kunming, China
  • 2 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Land surface temperature (LST) is a crucial factor for reflecting climate change. High spatial resolution LST is particularly significant for environmental monitoring in plateau and mountainous areas, which are characterized by rugged landscapes, diverse ecosystems, and high spatial variability in LST. Typical plateau mountainous areas in Diqing Tibetan Autonomous Prefecture and Dali Bai Autonomous Prefecture were selected as study areas. Three machine learning models, including Back Propagation Neural Network (BP), random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. Particle Swarm Optimization (PSO) was introduced to optimize hyperparameters of three machine learning methods. Regression factors suitable for plateau mountainous areas, including normalized vegetation index (NDVI), normalized multi-band drought index (NMDI), bare soil index (BSI), normalized difference snow index (NDSI), elevation, surface roughness (SR), Hillshade were selected. The performance of five models was analyzed from the perspective of different spatial resolutions and land cover types. The results revealed that the performance of machine learning models is better than traditional linear models in both study areas. Based on the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. For study area A, the results were R² = 0.891, RMSE = 2.67 K, and MAE = 1.83 K, while for study area B, the values were R² = 0.832, RMSE = 1.98 K, and MAE = 1.54 K. In addition, among different land cover types, the XGBoost model has the best performance in both study areas. Moreover, the larger the ratio of initial resolution to target resolution, the lower the accuracy of downscaled LST (DLST). In summary, the XGBoost model is more suitable for downscaling LST in plateau mountainous areas.

    Keywords: Land surface temperature, downscaling, Landsat-9, machine learning, XGBoost

    Received: 30 Aug 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Wang, Tang, Zhu, Fan, Li and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ju Wang, Kunming University of Science and Technology, Kunming, China

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