A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods
文献类型：期刊论文
| 作者 | Li, Yitao; Wu, Hua; Chen, Hong; Zhu, Xinming |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 61页码:4404514 |
| ISSN号 | 0196-2892 |
| 关键词 | Land surface temperature (LST) resolution enhancement spatial downscaling spatiotemporal fusion ther-mal infrared |
| DOI | 10.1109/TGRS.2023.3283614 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are the existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and spatial and temporal adaptive reflectance fusion model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine subregions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1-0.6 and 0.4-1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8%-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale. |
| WOS关键词 | SPACEBORNE THERMAL EMISSION ; REFLECTANCE FUSION ; SATELLITE IMAGES ; ENERGY FLUXES ; DISAGGREGATION ; MODULATION ; RETRIEVAL ; SCALE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001017380100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194623]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.UDICE-French Research Universities 2.Universites de Strasbourg Etablissements Associes 3.Chinese Academy of Sciences 4.Institute of Geographic Sciences & Natural Resources Research, CAS 5.Universite de Strasbourg 6.Centre National de la Recherche Scientifique (CNRS) 7.University of Chinese Academy of Sciences, CAS |
| 推荐引用方式 GB/T 7714 | Li, Yitao,Wu, Hua,Chen, Hong,et al. A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:4404514. |
| APA | Li, Yitao,Wu, Hua,Chen, Hong,&Zhu, Xinming.(2023).A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,4404514. |
| MLA | Li, Yitao,et al."A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):4404514. |
入库方式： OAI收割
来源：地理科学与资源研究所
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