Prediction of deep soil water content (0-5 m) with in-situ and remote sensing data
文献类型：期刊论文
| 作者 | Zhu, Zhaocen1,3; Zhao, Chunlei1,2; Jia, Xiaoxu1,2; Wang, Jiao1,2; Shao, Mingan1,2,3 |
| 刊名 | CATENA
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 222页码:10 |
| ISSN号 | 0341-8162 |
| 关键词 | Soil water content Pedo-transfer functions Remote sensing Chinese Loess Plateau |
| DOI | 10.1016/j.catena.2022.106852 |
| 通讯作者 | Zhao, Chunlei(zhaocl@igsnrr.ac.cn) |
| 英文摘要 | Accurate and timely evaluation of soil water content (SWC) in water-limited arid and semiarid regions will provide an essential reference for scientifically based water management strategies and vegetation restoration practices. Theoretical and empirical models based on remote sensing data have been successfully applied to predict large-scale SWC. However, most existing models are limited to the surface soil layer, while the prediction of SWC in the deep soil layer (1-5 m) has been overlooked because of the shortage of long-term and large-scale filed-observed data. In this study, a south-north transect (ca. 860 km) was selected across the Chinese Loess Plateau (CLP). Based on both long-term remote sensing and in-situ data (2013-2016), pedo-transfer functions (PTFs) for the SWC in the 0-5 m profile were developed using multiple regression, random forest, and artificial neural network. The results showed that evapotranspiration, potential evapotranspiration, soil surface temper-ature, total shortwave broadband albedo, and normalized difference vegetation index were important remote sensing parameters, whereas soil texture and bulk density were important in-situ parameters for SWC PTFs development. Among the three PTF development methods, machine learning (i.e. random forest and artificial neural network) obtained a higher accuracy than multiple regression. For different combinations of input pa-rameters, the introduction of in-situ factors significantly improved the accuracy of PTFs compared with PTFs based on remote sensing data only. The artificial neural network developed a PTF with only five input variables that predicted SWC with reasonable accuracy (root mean square error symbolscript 0.039, R2 symbolscript 0.697, mean absolute percentage symbolscript 24.8) and is thus useful for many applications on the Loess Plateau of China. In the future, more attention should be given to the role of in-situ parameters when developing PTFs for deep SWC prediction. |
| WOS关键词 | LAND-SURFACE TEMPERATURE ; LOESS PLATEAU ; RE-VEGETATION ; TIME-SERIES ; MOISTURE ; RETRIEVAL ; RADAR ; MODEL |
| 资助项目 | National Natural Science Founda-tion of China ; Opening Foundation of the State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau ; [41907009] ; [42022048] ; [41877016] ; [A314021402-2014] |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000904324200001 |
| 资助机构 | National Natural Science Founda-tion of China ; Opening Foundation of the State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/188500]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Chunlei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 3.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Zhaocen,Zhao, Chunlei,Jia, Xiaoxu,et al. Prediction of deep soil water content (0-5 m) with in-situ and remote sensing data[J]. CATENA,2023,222:10. |
| APA | Zhu, Zhaocen,Zhao, Chunlei,Jia, Xiaoxu,Wang, Jiao,&Shao, Mingan.(2023).Prediction of deep soil water content (0-5 m) with in-situ and remote sensing data.CATENA,222,10. |
| MLA | Zhu, Zhaocen,et al."Prediction of deep soil water content (0-5 m) with in-situ and remote sensing data".CATENA 222(2023):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
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