Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+ data and machine learning methods

ABSTRACT

Land surface temperature (LST) is an important parameter in land surface processes. Improving the accuracy of LST retrieval over the entire Tibetan Plateau (TP) using satellite images with high spatial resolution is an important and essential issue for studies of climate change on the TP. In this study, a random forest regression (RFR) model based on different land cover types and an improved generalized single-channel (SC) algorithm based on linear regression (LR) were proposed. Plateau-scale LST products with a 30 m spatial resolution from 2006 to 2017 were derived by 109,978 Landsat 7 Enhanced Thematic Mapper Plus images and the application of the Google Earth Engine. Validation between LST results obtained from different algorithms and in situ measurements from Tibetan observation and research platform showed that the root mean square errors of the LST results retrieved by the RFR and LR models were 1.890 and 2.767 K, respectively, which were smaller than that of the MODIS product (3.625 K) and the original SC method (5.836 K).

KEYWORDS:

• Google Earth Engine
• remote sensing
• machine learning
• land surface temperature
• random forest

Acknowledgments

The Landsat ETM+ data can be accessed from https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_RT. The TORP data can be accessed from https://data.tpdc.ac.cn/zh-hans/data.

Data availability statement

Supplemental data for this article can be accessed at https://doi.org/10.4121/16936909.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Second Tibetan Plateau Scientifc Expedition and Research (STEP) Program [grant number: 2019QZKK0103]; Strategic Priority Research Program of Chinese Academy of Sciences [grant number: XDA20060101]; National Natural Science Foundation of China [grant number 41875031, 41522501, 41275028, 91837208]; The Chinese Academy of Sciences [grant number QYZDJSSW-DQC019] and CLIMATE-TPE [grant number: 32070] in the framework of the ESA-MOST Dragon 4 Programme.