Objectives Near surface air temperature (NSAT) is a key parameter in the land-atmosphere interaction process. Sparse NSAT observations from in-situ stations usually cannot fully describe the spatial distribution of NSAT, so estimating NSAT by land surface temperature (LST) and auxiliary variables has become an effective approach to obtain the spatial distribution of NSAT. Although there are some multi-source LST products published, the LST from moderate-resolution imaging spectroradiometer(MODIS), Landsat, and global land data assimilation system (GLDAS), the applicability of each LST product in NSAT estimation still needs further investigation.
Methods Taken the Yellow River Basin Region as the study area, summer NSAT from 2003 to 2019 was estimated based on the Google Earth Engine platform with random forest algorithm in this study, and the mean, maximum and minimum NSAT was estimated in two scales (30 m and 1 000 m) using three LST data sources (Landsat, MODIS and GLDAS). The observed LST from in-situ stations over the Yellow River Basin Region were compared with the estimated NSAT by the ten-cross validation method to evaluate the accuracy of different LST sources when estimating NSAT.
Results and Conclusions The results indicate that the differences of accuracy of the three LST sources are small in terms of mean NSAT. In terms of the maximum and minimum NSAT, the GLDAS LST shows the significant higher accuracy than MODIS and Landsat LST. The root mean squared error of estimated mean NSAT are smaller than maximum and minimum NSAT estimation when using the same LST source. For the spatial distribution of accuracy, the stations with higher error mainly located in the southern or western of the study region. The temporal resolution of LST source is significantly important in NSAT estimation. The GLDAS LST shows the highest accuracy in this study especially for extreme NSAT estimation. Besides, the mean NSAT estimation has higher accuracy than that of maximum or minimum NSAT for each LST source.
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