Algorithm parameters for retrieving land surface temperature from the SDGSAT-1 thermal infrared spectrometer

Abstract

Land surface temperature (LST) serves as a crucial indicator of the thermal state and environmental changes on the Earth’s surface, and it can be retrieved effectively from satellite thermal infrared sensors. Although algorithms for retrieving LSTs have been developed successfully for many satellites, the newly launched Sustainable Development Science Satellite 1 (SDGSAT-1), which includes three thermal infrared bands, does not yet include effective LST algorithms and parameters. Here, parameters are calibrated for retrieving LSTs from SDGSAT-1 Thermal Infrared Spectrometer (TIS) data for the split-window (SW) method and the three-channel (TC) method under both daytime and nighttime conditions. In this process, the Thermodynamic Initial Guess Retrieval (TIGR) dataset and observation data from the University of Wyoming were used. Validations were conducted using in situ LST measurements at the Guantao, Turpan, and Heihe sites in China and from the Surface Radiation Budget (SURFRAD) network in North America, covering cropland, desert and bare land, and grassland. The overall accuracies of the models are fairly good, with RMSEs of 2.507 K and 2.272 K for split-window method during daytime and nighttime respectively, and 2.847 K and 1.923 K for three-channel method. Additionally, the LST retrieval models that use observation data from the University of Wyoming had higher accuracy than those using the TIGR2000 profiles. Currently, the proposed models can be applied under different atmospheric water vapor contents and underlying surface conditions both during the day and at night, paving the way for retrieving LST products from SDGSAT-1.