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Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+data and machine learning methods 被引量:2
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作者 Xian Wang Lei Zhong Yaoming Ma 《International Journal of Digital Earth》 SCIE EI 2022年第1期1038-1055,共18页
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 imp... 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). 展开更多
关键词 Google Earth Engine remote sensing machine learning land surface temperature random forest
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