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Adjustment from Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 4.7 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3021386 Kangning Li , Yunhao Chen , Haiping Xia , Adu Gong , Zheng Guo
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 4.7 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3021386 Kangning Li , Yunhao Chen , Haiping Xia , Adu Gong , Zheng Guo
Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this article proposes a method combining adjustment from annual temperature circle to reconstruct LST based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with moderate resolution imaging spectroradiometer (MODIS) LST, ground surface temperature, and in situ observations from the Heihe Watershed Allied Telemetry Experimental Research, respectively. In the strategy 1, mean root mean square error (RMSE) of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K, and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K, and 2.87 K at day, and 2.80 K, 3.61 K, and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction.
更新日期：2020-01-01
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