Urban Surface Temperature Inversion from SDGSAT-1 Satellite

The urban land surface temperature (LST) is very important in urban development, changes and local climate in the city, etc. Many methods have been proposed to inverse LST from satellite remotely sensed data. In this study, an inversion method of combining deep learning and physical model was proposed to estimate the urban surface temperature from CHINESE SDGSAT-1 satellite, i.e., an improved temperature and emissivity separation (TES) algorithm based on sky view factor (SVF). Finally, two data sets were selected to evaluate the accuracy of the proposed algorithm. Results show that the root mean squared errors (RMSEs) using the proposed algorithm are approximately 0.39K for LST and 0.023 for emissivity, respectively. The proposed algorithm is applied to inverse the LST/LSE of Beijing and Wuhan, China. Compared with Landsat-8 satellite products, the proposed algorithm has consistent accuracy. The cross-validated RMSEs with the Landsat-8 surface temperature product in Wuhan and Beijing were 2.18 K and 1.11 K, respectively.