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| A physical method for downscaling land surface temperatures using surface energy balance theory | |
Hu, Yongxin1,2,3; Tang, Ronglin1,3; Jiang, Xiaoguang1,4; Li, Zhao-Liang1,2,3; Jiang, Yazhen1,3; Liu, Meng5; Gao, Caixia4; Zhou, Xiaoming6
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| 2023-03-01 | |
| Source Publication | REMOTE SENSING OF ENVIRONMENT
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| ISSN | 0034-4257 |
| Volume | 286 |
| Abstract | Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. The DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for inter comparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy. |
| Keyword | Land surface temperature Thermal infrared remote sensing Surface energy balance Downscaling DTsEB |
| DOI | 10.1016/j.rse.2022.113421 |
| Indexed By | SCIE |
| Language | 英语 |
| WOS Research Area | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000913792900001 |
| Publisher | ELSEVIER SCIENCE INC |
| Source library | WOS |
| Document Type | 期刊论文 |
| Identifier | https://ir.lut.edu.cn/handle/2XXMBERH/161033 |
| Collection | 土木工程学院 |
| Affiliation | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 2.Univ Strasbourg, ICube Lab, UMR 7357, CNRS, 300 Bd Sebastien Brant,CS 10413, F-67412 Illkirch Graffenstaden, France; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; 4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Quantitat Remote Sensing Informat Technol, Beijing, Peoples R China; 5.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr, Beijing, Peoples R China; 6.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou, Gansu, Peoples R China |
| Recommended Citation GB/T 7714 | Hu, Yongxin,Tang, Ronglin,Jiang, Xiaoguang,et al. A physical method for downscaling land surface temperatures using surface energy balance theory[J]. REMOTE SENSING OF ENVIRONMENT,2023,286. |
| APA | Hu, Yongxin.,Tang, Ronglin.,Jiang, Xiaoguang.,Li, Zhao-Liang.,Jiang, Yazhen.,...&Zhou, Xiaoming.(2023).A physical method for downscaling land surface temperatures using surface energy balance theory.REMOTE SENSING OF ENVIRONMENT,286. |
| MLA | Hu, Yongxin,et al."A physical method for downscaling land surface temperatures using surface energy balance theory".REMOTE SENSING OF ENVIRONMENT 286(2023). |
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