Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+ data and machine learning methods
- 1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
- 2CAS Center for Excellence in Comparative Planetology, Hefei, 230026, China
- 3Land-Atmosphere Interaction and its Climatic Effects Group, State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
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 the 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 K and 2.767 K, respectively, which were smaller than those of the MODIS product (3.625 K) and the original SC method (5.836 K).
How to cite: Wang, X., Zhong, L., and Ma, Y.: Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+ data and machine learning methods, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6093, https://doi.org/10.5194/egusphere-egu23-6093, 2023.
