Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau
文献类型：期刊论文
| 作者 | Xie, Baoni1,3; Jia, Xiaoxu1,2; Qin, Zhanfei3; Zhao, Chunlei1; Shao, Ming'an1,2 |
| 刊名 | VADOSE ZONE JOURNAL
 |
| 出版日期 | 2020 |
| 卷号 | 19期号:1页码:16 |
| DOI | 10.1002/vzj2.20025 |
| 通讯作者 | Jia, Xiaoxu(jiaxx@igsnrr.ac.cn) |
| 英文摘要 | Due to limited in situ observations, prediction of large-scale soil moisture content (SMC) for deep soil layers via interpolation is usually very challenging. This is especially true for regions with high spatial variations of terrain features. For precise prediction at a regional scale, SMC data for the 0- to 500-cm soil profile across China's Loess Plateau (CLP) region were collected and interpolated using four different methods. The methods included inverse distance weighting (IDW), ordinary kriging (OK), multiple linear regression with residual kriging (MLR-RK), and radial basis function neural network with residual kriging (RBFNN-RK). The objective of the study was to determine the optimal interpolation method for predicting regional SMC at various soil layers. The study showed that the performances of IDW, OK, and RBFNN-RK in predicting SMC were generally much better than that of MLR-RK. Specifically, IDW performed best for soil depths of 200300 and 400500 cm. This was attributed to the more uniform distribution (smoother change of spatial clusters) of SMC in these two layers. The OK method performed best for the 10- to 40- and 40- to 100-cm soil layers, which was due to the strong spatial dependence of the two layers. The RBFNN-RK performed best for the 0- to 10-, 100- to 200-, and 300- to 400-cm soil layers, because RBFNN-RK captures nonlinear relations of SMC with environmental factors. Ordinary kriging, IDW, and RBFNN-RK interpolation can therefore be used to predict regional SMC for different soil layers in CLP region. The RBFNN-RK method was recommended for predicting regional SMC in complex topographic hilly-gully regions where there is nonlinear relation between SMC and environmental variables. |
| 资助项目 | Scientific Research Starting Foundation for Doctors[BQ2017001] ; Youth Innovation PromotionAssociation of the ChineseAcademy of Sciences[2017076] ; NationalKey Project for Research and Development[2016YFC0501605] ; National Natural Science Foundation ofChina[41877016] ; National Natural Science Foundation ofChina[41530854] |
| WOS研究方向 | Environmental Sciences & Ecology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000618773300025 |
| 出版者 | WILEY |
| 资助机构 | Scientific Research Starting Foundation for Doctors ; Youth Innovation PromotionAssociation of the ChineseAcademy of Sciences ; NationalKey Project for Research and Development ; National Natural Science Foundation ofChina |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160633]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Jia, Xiaoxu |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 3.Hebei GEO Univ, Sch Land Resources & Urban Rural Planning, Shijiazhuang 050031, Hebei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Baoni,Jia, Xiaoxu,Qin, Zhanfei,et al. Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau[J]. VADOSE ZONE JOURNAL,2020,19(1):16. |
| APA | Xie, Baoni,Jia, Xiaoxu,Qin, Zhanfei,Zhao, Chunlei,&Shao, Ming'an.(2020).Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau.VADOSE ZONE JOURNAL,19(1),16. |
| MLA | Xie, Baoni,et al."Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau".VADOSE ZONE JOURNAL 19.1(2020):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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