Development of a 250-m Downscaled Land Surface Temperature Data Set and Its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China
文献类型：期刊论文
| 作者 | Liu, Kai1; Su, Hongbo2; Li, Xueke3; Chen, Shaohui1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:12 |
| 关键词 | Downscaling evapotranspiration (ET) geographically weighted regression (GWR) land surface temperature (LST) random forest (RF) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2020.3037168 |
| 通讯作者 | Su, Hongbo(suh@fau.edu) |
| 英文摘要 | Satellite-derived land surface temperature (LST) is critical for retrieving terrestrial evapotranspiration (ET); however, its availability is limited by low spatial resolution and inclement weather conditions. This study develops a spatio-temporal regression strategy that can downscale 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to 250-m resolution and simultaneously gap-fill the missing values. The proposed methodology synergistically uses random forest (RF) model and geographically weighted regression, which are, respectively, available for demonstrating the nonlinear correlation between LST and explanatory variables and for calibrating the RF-derived residuals. The study is conducted across a region of similar to 1.49 million square kilometers in northern China. The coupled model creates a 250-m spatial resolution LST product with the root-mean-square error (RMSE) of 2.32 and 1.87 K when compared with field observations and reference Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST, respectively. Meanwhile, it minimizes the constraint of LST availability due to inclement weather conditions with RMSE of 2.69 and 2.31 K relative to field observations and reference images, respectively. The results further reveal that remote-sensing-derived ET using the 250-m downscaled LST data is fairly accurate with the relative errors of 6%-9% as evaluated with flux measurements. The 250-m modeled ET retrievals exhibit a more intense hydrological response to the water use conditions compared with the 1-km remotely sensed ETs and Noah land surface model ETs. This study may benefit land surface hydrology research and water resource management. |
| WOS关键词 | AGRICULTURAL AREA ; VEGETATION INDEX ; RANDOM FOREST ; TIME-SERIES ; MODIS ; DISAGGREGATION ; VALIDATION ; SCALE ; INTERPOLATION ; RECONSTRUCTION |
| 资助项目 | Natural Science Fund of China[41971315] ; Natural Science Fund of China[41571356] ; Natural Science Fund of China[41371348] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000726094900009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Natural Science Fund of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/168500]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Su, Hongbo |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources, Beijing 100101, Peoples R China 2.Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Boca Raton, FL 33431 USA 3.Brown Univ, Inst Brown Environm & Soc, Providence, RI 02912 USA |
| 推荐引用方式 GB/T 7714 | Liu, Kai,Su, Hongbo,Li, Xueke,et al. Development of a 250-m Downscaled Land Surface Temperature Data Set and Its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:12. |
| APA | Liu, Kai,Su, Hongbo,Li, Xueke,&Chen, Shaohui.(2022).Development of a 250-m Downscaled Land Surface Temperature Data Set and Its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,12. |
| MLA | Liu, Kai,et al."Development of a 250-m Downscaled Land Surface Temperature Data Set and Its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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