Meng, XC; Cheng, J (2020). Estimating Land and Sea Surface Temperature From Cross-Calibrated Chinese Gaofen-5 Thermal Infrared Data Using Split-Window Algorithm. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 17(3), 509-513.

In this letter, the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm and the quadratic split-window (SW) algorithm were adapted to high spatial resolution thermal infrared (TIR) data of Chinese Gaofen-5 (GF5) to estimate the land surface temperature (LST) and sea surface temperature (SST), respectively. Lacking official calibration coefficients, GF5 TIR data were cross-calibrated by the well-characterized Visible Infrared Imaging Radiometer Suite (VIIRS) data. The coefficients of two SW algorithms were obtained by linear regression from the simulated data set generated via comprehensive radiative transfer modeling. The performance of the two algorithms was first evaluated by independent simulation data and then cross-validated by Moderate Resolution Imaging Spectroradiometer (MODIS) LST/SST, VIIRS LST/SST, and Advanced Himawari Imager (AHI) SST products. The preliminary results show good agreement between estimated GF5 LSTs/SSTs and referenced LST/SST products, with an average bias (root mean square error) of -0.26 (1.74), -2.48 (3.49), 0.18 (2.43), and -1.47 K (2.86 K) for VLSTO, VNP21, MYD11, and MYD21 LST products, -0.79 (1.55), -0.28(1.58), and -1.71 (2.21) K for VIIRS, MODIS, and AHI SST products. This is the first time that both LST and SST are retrieved from the real GF5 data. This letter provides a practical method to estimate LST and SST from Chinese Gaofen-5.