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A Combining Method for Generating Land Surface Temperature with High Spatiotemporal Resolution

文献类型： 会议论文

第一作者： Yitao Li

作者： Yitao Li 1 ; Hua Wu 1 ; Zhao-Liang Li 2 ; Caixia Gao 3 ;

作者机构： 1.State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research,, Chinese Academy of Sciences, Beijing, China|University of Chinese Academy of Sciences, Beijing, China

2.Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

3.Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

关键词： Reflectivity;Land surface;Radiometry;Prediction algorithms;Reflection;Land surface temperature;Spatiotemporal phenomena

会议名称： IEEE International Geoscience and Remote Sensing Symposium

主办单位：

页码： 3747-3750

摘要： Satellite-derived high-resolution LST observations are essential for environmental studies. However, the tradeoff between spatial and temporal resolutions largely restricts the application of current LST products. As a consequence, many spatial downscaling or spatiotemporal fusion methods were proposed to overcome this limitation. In this paper, we design a novel empirical weighting method to combine the results from the popular downscaling and fusion methods, thermal sharpening algorithm (TsHARP), and spatial and temporal adaptive reflectance fusion model (STARFM). Specifically, the error of the two methods are firstly estimated and the predictions are blended based on the inverse ratio of the corresponding error. Our method is tested with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing. Compared with the actual ASTER LST, the combining results could both enhance the accuracy and structure similarity, as our method utilizes spatial-temporal-spectral information. Moreover, our method also has the potential for generating more accurate daily high-resolution LSTs.

分类号： tp7-53

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