Land surface temperature retrieval from SDGSAT-1 thermal infrared spectrometer images: Algorithm and validation

Abstract

Launched by China in 2021, the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) is the world's first science satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development Goals. In keeping with international aims of this 2030 agenda, the SDGSAT-1 data will be made available for open accee without any restrictions. The Thermal Infrared Spectrometer (TIS) onboard SDGSAT-1 has three thermal infrared channels with a high spatial resolution of 30 m. This enables precise monitoring of land surface temperature (LST), which is one of the most important variables measured by satellite remote sensing. This paper presents the development and validation of three split-window (SW) algorithms and the temperature and emissivity separation (TES) algorithm for SDGSAT-1 TIS data. These algorithms were rigorously tested through simulation, application, and validation to assess their retrieval accuracy and sensitivity. Simulation results indicate that the theoretical accuracy of the SW algorithms exceeds 1.0 K in most cases, and the TES algorithm shows higher retrieval accuracy with an average LST Root Mean Square Error (RMSE) of 0.60 K. With consideration of the comprehensive effects of instrument noise, land surface emissivity, and atmospheric parameter error, the LST retrieval accuracy of SW algorithms remains better than 1.7 K, and that of the TES algorithm is better than 1.5 K under most conditions. The ground validation utilized site data from the Heihe Integrated Observatory Network and the Surface Radiation budget network. The SW and TES algorithms achieved an accuracy of approximately 1.75 and 1.9 K, respectively. Additionally, a cross-validation based on Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated average RMSDs of approximately 2.25 K for SW algorithms and 3.84 K for TES algorithm. Among the algorithms, the three-channel SW algorithm SW-2 has the best overall performance and is recommended as the LST retrieval method for SDGSAT-1 data. The TES algorithm is also suitable for SDGSAT-1 images because of its ability to retrieve LST and emissivity during both daytime and nighttime.