High-Resolution Land Surface Temperature Retrieval from GF5-02 VIMI Data using an Operational Split-Window Algorithm

High-resolution land surface temperature (LST) product holds significant importance in quantifying surface heat, monitoring climate change, assessing environmental health, and water resource management. Therefore, accurate LST retrieval improves our understanding of detailed thermal characteristics of the Earth’s surface. In this research, we developed an operational split-window algorithm for generationg high-resolution LST products from Gaofen5-02 (GF5-02) Visible and Infrared Multispectral Imager (VIMI) data. The coefficients of the split-window algorithm are simulated utilizing the MODTRAN 5.2 atmospheric radiative transfer model, with the global atmospheric profile library of SeeBor V5.0. The land surface emissivities in VIMI bands 11 and 12 are estimated using the ASTER global emissivity dataset (GED) based on the vegetation cover method. The GF5-02 VIMI LSTs are validated using in-situ data collected from the Huailai experiment site in China. Preliminary results show the accuracy of the GF5-02 LST products is satisfactory, exhibiting a bias of -0.03 K and a Root Mean Square Error (RMSE) of 2.42 K.