Progress on spatial prediction methods for soil particle-size fractions
文献类型：期刊论文
| 作者 | Shi Wenjiao1,2,3; Zhang Mo1,2,3 |
| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 33期号:7页码:1553-1566 |
| 关键词 | soil interpolation spatial accuracy geostatistics machine learning high accuracy surface modeling |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-023-2142-6 |
| 通讯作者 | Shi Wenjiao(shiwj@lreis.ac.cn) |
| 英文摘要 | Soil particle-size fractions (PSFs), including three components of sand, silt, and clay, are very improtant for the simulation of land-surface process and the evaluation of ecosystem services. Accurate spatial prediction of soil PSFs can help better understand the simulation processes of these models. Because soil PSFs are compositional data, there are some special demands such as the constant sum (1 or 100%) in the interpolation process. In addition, the performance of spatial prediction methods can mostly affect the accuracy of the spatial distributions. Here, we proposed a framework for the spatial prediction of soil PSFs. It included log-ratio transformation methods of soil PSFs (additive log-ratio, centered log-ratio, symmetry log-ratio, and isometric log-ratio methods), interpolation methods (geostatistical methods, regression models, and machine learning models), validation methods (probability sampling, data splitting, and cross-validation) and indices of accuracy assessments in soil PSF interpolation and soil texture classification (rank correlation coefficient, mean error, root mean square error, mean absolute error, coefficient of determination, Aitchison distance, standardized residual sum of squares, overall accuracy, Kappa coefficient, and Precision-Recall curve) and uncertainty analysis indices (prediction and confidence intervals, standard deviation, and confusion index). Moreover, we summarized several paths on improving the accuracy of soil PSF interpolation, such as improving data distribution through effective data transformation, choosing appropriate prediction methods according to the data distribution, combining auxiliary variables to improve mapping accuracy and distribution rationality, improving interpolation accuracy using hybrid models, and developing multi-component joint models. In the future, we should pay more attention to the principles and mechanisms of data transformation, joint simulation models and high accuracy surface modeling methods for multi-components, as well as the combination of soil particle size curves with stochastic simulations. We proposed a clear framework for improving the performance of the prediction methods for soil PSFs, which can be referenced by other researchers in digital soil sciences. |
| WOS关键词 | LOG-RATIO TRANSFORMATION ; COMPOSITIONAL DATA ; QUANTILE REGRESSION ; ROBUST ESTIMATORS ; ORGANIC-CARBON ; VARIOGRAM ; FOREST ; INTERPOLATION ; TEXTURE ; CURVES |
| 资助项目 | National Natural Science Foundation of China[41930647] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100202] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20040301] ; State Key Laboratory of Resources and Environmental Information System |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001076750300010 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198633]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Shi Wenjiao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat CAS, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi Wenjiao,Zhang Mo. Progress on spatial prediction methods for soil particle-size fractions[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2023,33(7):1553-1566. |
| APA | Shi Wenjiao,&Zhang Mo.(2023).Progress on spatial prediction methods for soil particle-size fractions.JOURNAL OF GEOGRAPHICAL SCIENCES,33(7),1553-1566. |
| MLA | Shi Wenjiao,et al."Progress on spatial prediction methods for soil particle-size fractions".JOURNAL OF GEOGRAPHICAL SCIENCES 33.7(2023):1553-1566. |
入库方式： OAI收割
来源：地理科学与资源研究所
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