Analysis of Seasonal Dominant Factors Influencing Land Surface Temperature Over the Tibetan Plateau
Land surface temperature (LST) is a crucial parameter that reflects the energy balance of the ground, playing a key role in characterizing the change of climate. LST on the Tibetan Plateau (TP) directly impacts local climate and environmental alterations. Although several factors contribute to the d...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10886932/ |
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| Summary: | Land surface temperature (LST) is a crucial parameter that reflects the energy balance of the ground, playing a key role in characterizing the change of climate. LST on the Tibetan Plateau (TP) directly impacts local climate and environmental alterations. Although several factors contribute to the distribution of LST of the TP, there is still limited understanding regarding the main driving factors and seasonal variations. This study focuses on the TP, redefining the four seasons based on the “Universal Plateau Season Division Method.” Five LST datasets were selected, including two from commercial satellites and three independently developed and published by researchers, to examine the factors influencing LST across various seasons. Geodetector (GD), random forest (RF), and SHAP methods were utilized for analysis. The findings indicate that air temperature (AT), permafrost thermal stability (PTS), and elevation (ELE) significantly affect LST in all seasons. AT, PTS, and ELE contribute to LST by 58.60%, 35.36%, and 46.37% in spring; 51.07%, 36.16%, and 24.50% in summer; 57.90%, 44.20%, and 36.86% in autumn; and 78.31%, 44.41%, and 59.24% in winter. However, the impact of other variables fluctuates with the season. While the results offered by GD from the perspective of geographical strata differ from the results provided by RF/SHAP based on statistical regression, there is a significant resemblance between them. This study aims to offer a comprehensive guide for selecting appropriate parameters in the research of LST across various seasons by enhancing the understanding of the complex interplay between surface thermal mechanisms and the influencing factors. |
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| ISSN: | 1939-1404 2151-1535 |