Mapping high resolution National Soil Information Grids of China
文献类型：期刊论文
| 作者 | Liu, Feng2,3; Wu, Huayong2; Zhao, Yuguo2,3; Li, Decheng2; Yang, Jin-Ling2,3; Song, Xiaodong2; Shi, Zhou4; Zhu, A-Xing1,5; Zhang, Gan-Lin2,3,6 |
| 刊名 | SCIENCE BULLETIN
 |
| 出版日期 | 2022-02-15 |
| 卷号 | 67期号:3页码:328-340 |
| 关键词 | Predictive soil mapping Soil-landscape model Machine learning Depth function Large and complex areas Soil spatial variation |
| ISSN号 | 2095-9273 |
| DOI | 10.1016/j.scib.2021.10.013 |
| 通讯作者 | Zhang, Gan-Lin(glzhang@issas.ac.cn) |
| 英文摘要 | Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0-5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development. (c) 2021 Science China Press. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| WOS关键词 | DEPTH FUNCTIONS ; UNCERTAINTY ; GLOBALSOILMAP ; PREDICTION ; PROPERTY ; SCALE ; MAP |
| 资助项目 | National Key Basic Research Special Foundation of China[2008FY110600] ; National Key Basic Research Special Foundation of China[2014FY110200] ; National Natural Science Foundation of China[41930754] ; National Natural Science Foundation of China[42071072] ; 2nd Comprehensive Scientific Survey of the Qinghai-Tibet Plateau[2019QZKK0306] ; Project of One-Three-Five Strategic Planning & Frontier Sciences of the Institute of Soil Science, Chinese Academy of Sciences[ISSASIP1622] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000750754200015 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Basic Research Special Foundation of China ; National Natural Science Foundation of China ; 2nd Comprehensive Scientific Survey of the Qinghai-Tibet Plateau ; Project of One-Three-Five Strategic Planning & Frontier Sciences of the Institute of Soil Science, Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/170197]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Gan-Lin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Zhejiang Univ, Coll Environm & Resource Sci, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Peoples R China 5.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China 6.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Feng,Wu, Huayong,Zhao, Yuguo,et al. Mapping high resolution National Soil Information Grids of China[J]. SCIENCE BULLETIN,2022,67(3):328-340. |
| APA | Liu, Feng.,Wu, Huayong.,Zhao, Yuguo.,Li, Decheng.,Yang, Jin-Ling.,...&Zhang, Gan-Lin.(2022).Mapping high resolution National Soil Information Grids of China.SCIENCE BULLETIN,67(3),328-340. |
| MLA | Liu, Feng,et al."Mapping high resolution National Soil Information Grids of China".SCIENCE BULLETIN 67.3(2022):328-340. |
入库方式： OAI收割
来源：地理科学与资源研究所
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