Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China
文献类型：期刊论文
| 作者 | Qu, Yuquan1,2; Zhu, Zhongli1; Montzka, Carsten2; Chai, Linna1; Liu, Shaomin1; Ge, Yong3,4 ; Liu, Jin1; Lu, Zheng1; He, Xinlei1; Zheng, Jie1
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| 刊名 | JOURNAL OF HYDROLOGY
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| 出版日期 | 2021 |
| 卷号 | 592页码:21 |
| ISSN号 | 0022-1694 |
| 关键词 | Soil moisture Downscaling Methods comparison Qinghai-Tibet plateau |
| DOI | 10.1016/j.jhydrol.2020.125616 |
| 通讯作者 | Zhu, Zhongli(zhuzl@bnu.edu.cn) |
| 英文摘要 | Microwave remote sensing is able to retrieve soil moisture (SM) at an adequate level of accuracy. However, these microwave remotely sensed SM products usually have a spatial resolution of tens of kilometers which cannot satisfy the requirements of fine to medium scale applications such as agricultural irrigation and local water resource management. Several SM downscaling methods have been proposed to solve this mismatch by downscaling the coarse-scale SM to fine-scale (several kilometers or hundreds of meters). Although studies have been conducted over different climatic zones and from different data sets with good results, there is still a lack of a comprehensive comparison and evaluation between them to guide the production of high-resolution and high-accuracy SM data. Therefore, in this study we compared several SM downscaling methods (from 0.25 degrees to 0.01 degrees) based on polynormal fitting, physical model, machine learning and geostatistics over the Qinghai-Tibet plateau where there is a wide range of climate conditions from four aspects, that is, comparison with the original microwave product, comparison with in situ measurements, inter-comparison based on three-cornered hat (TCH) method, and a spatial feasibility analysis. The comparison results show that the method based on a physical model, in this case the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) method, has the highest ability on preserving the coarse-scale feature of original microwave SM product, while to some extent, this ability could be a disadvantage for improving the accuracy of the downscaling results. In addition, soil evaporation efficiency (SEE) alone is not sufficient to represent SM spatial patterns over complex land surface. Geostatistics based area-to-area regression Kriging (ATARK) introduces the highest uncertainty caused by the overcorrection during the residual interpolation process while this process can also improve correlation (R) and correct the bias as well as provide more feasible spatial patterns and details. Two machine learning methods, the random forest (RF) and Gaussian process regression (GPR) show high stability on all comparison results but provide smoother spatial patterns. The multivariate statistical regression (MSR) method performs worst due to the fact that its simple linear regression model could not meet the requirement of SM fitting on complicated land surface. Moreover, all five downscaling methods show a declining accuracy after downscaling. This phenomenon may be caused by the spatial mismatch on fine-scale. In addition, this could also be caused by the tendency that downscaled results will usually provide more spatial details from downscaling predictors, while they cannot capture the temporal changes of the microwave SM product well. In general, this phenomenon tends to be more significant over heterogeneous land surface. All in all, five widely used soil moisture downscaling methods were compared based on a comprehensive comparison scheme to add to the body of knowledge in applicability of downcaling methods under different weather conditions. |
| WOS关键词 | REGRESSION NEURAL-NETWORKS ; LAND-SURFACE TEMPERATURE ; L-BAND RADIOMETER ; HIGH-RESOLUTION ; AMSR-E ; RANDOM FOREST ; SATELLITE ; ALGORITHM ; SMOS ; RADAR |
| 资助项目 | National Natural Science Foundation of China[41671336] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20100101] ; State Key Laboratory of Earth Surface Processes and Resource Ecology[2017-FX04] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000639844900018 |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; State Key Laboratory of Earth Surface Processes and Resource Ecology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/162673]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhu, Zhongli |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 2.Forschungszentrum Julich, Inst Bio & Geosci Agrosphere IBG 3, D-52428 Julich, Germany 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lab Remote Sensing & Geospatial Sci, Lanzhou 730000, Gansu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qu, Yuquan,Zhu, Zhongli,Montzka, Carsten,et al. Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China[J]. JOURNAL OF HYDROLOGY,2021,592:21. |
| APA | Qu, Yuquan.,Zhu, Zhongli.,Montzka, Carsten.,Chai, Linna.,Liu, Shaomin.,...&Han, Tian.(2021).Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China.JOURNAL OF HYDROLOGY,592,21. |
| MLA | Qu, Yuquan,et al."Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China".JOURNAL OF HYDROLOGY 592(2021):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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