Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chi...
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Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chinese Atmospheric Environment Monitoring Satellite (DQ-1) is equipped with the wide swath imager (WSI), which includes three thermal infrared bands capable of providing global LST retrieval. This article introduces a nonlinear hybrid algorithm that combines the split-window (SW) algorithm and the temperature and emissivity separation (TES) algorithm, and the accuracies of the three algorithms, including hybrid, SW and TES algorithm are analyzed. The results demonstrated that the root mean square errors of LST for SW, TES, and hybridalgorithm are approximately 2.11, 1.78, and 1.64 K, with mean absolute errors (of 1.72, 1.40, and 1.21 K using in situ measurements from the SURFRAD sites. Cross-validation with moderate-resolution imaging spectroradiometer (MODIS) LST products showed that the hybridalgorithm outperforms the SW and TES algorithms in retrieving LST, achieving reductions in LST error of 0.43 and 0.16 K at the Qinghai Lake site, and 0.67 and 0.06 K at the Dunhuang site, respectively. In summary, this study demonstrates that the nonlinear hybrid algorithm can accurately estimate LST from DQ1/WSI data.
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