A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands
文献类型：期刊论文
| 作者 | Wang, SuiZi1,2; Fan, JiangWen1 ; Zhong, HuaPing1; Li, YuZhe1; Zhu, HuaZhong1; Qiao, YuXin1; Zhang, HaiYan1,2
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| 刊名 | CATENA
 |
| 出版日期 | 2019-03-01 |
| 卷号 | 174页码:248-258 |
| ISSN号 | 0341-8162 |
| 关键词 | Soil organic carbon Spatial distribution Multi-factor weighted regression model Soil layer Grasslands |
| DOI | 10.1016/j.catena.2018.10.050 |
| 通讯作者 | Zhu, HuaZhong(zhuhz@igsnrr.ac.cn) |
| 英文摘要 | Soil organic carbon (SOC) plays an important role in global carbon cycling and is increasingly important to the ecosystem. An accurate SOC content map would significantly contribute to the proper application of ecological modeling. Therefore, there is a need to accurately estimate and map SOC content in grasslands. We evaluated various methods for estimating the SOC content of grasslands using field soil sampling data and auxiliary data in the pastoral area. The results showed that (1) SOC is affected by various factors, including geographic location, soil, topography, and climate. Single-variable SOC models account for 2%-72% of the variations in the grassland SOC. (2) Based on the correlation of environmental variables of SOC, normalized difference vegetation index, annual precipitation, annual average temperature, elevation, and moisture index were explored as critical auxiliary data to predict SOC content. We established multi-factor weighted regression model (MWRM). (3) We compared three spatial estimation methods, including inverse distance weighting, regression kriging, and MWRM, to determine a suitable method for SOC mapping. Our results indicate that among the three spatial estimation methods, MWRM provides the lowest prediction error (RMSE = 4.85 g/kg; MAE = 3.47 g/kg; MRE = 24.04%) and highest R-2 (0.89) and Lin's concordance (0.94) values in the spatial estimation at a 0-10 cm soil layer. (4) Therefore, we applied MWRM to predict SOC content at various layers, and its SOC content prediction in the topsoil (0-20 cm) is better than that in the subsurface (20-30 cm) and subsoil (30-40 cm). The SOC content spatial distribution demonstrated a similar pattern for each soil layer and the SOC content gradually decreased with increasing soil depth. |
| WOS关键词 | TERRESTRIAL ECOSYSTEMS ; CLIMATE-CHANGE ; MATTER CONTENT ; LOESS PLATEAU ; STORAGE ; STOCKS ; SCALE ; PREDICTION ; VARIABILITY ; DYNAMICS |
| 资助项目 | National Science and Technology Foundation[2012FY111900-2] ; National Key R & D Program of China[2017YFA0604804] ; National Natural Science Foundation of China[J1103520] ; National Science and Technology Infrastructure Platform - Earth System Scientific Data Sharing Platform[2005DKA32300] |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE BV |
| WOS记录号 | WOS:000456754600023 |
| 资助机构 | National Science and Technology Foundation ; National Key R & D Program of China ; National Natural Science Foundation of China ; National Science and Technology Infrastructure Platform - Earth System Scientific Data Sharing Platform |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68577]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhu, HuaZhong |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, 11 Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, SuiZi,Fan, JiangWen,Zhong, HuaPing,et al. A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands[J]. CATENA,2019,174:248-258. |
| APA | Wang, SuiZi.,Fan, JiangWen.,Zhong, HuaPing.,Li, YuZhe.,Zhu, HuaZhong.,...&Zhang, HaiYan.(2019).A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands.CATENA,174,248-258. |
| MLA | Wang, SuiZi,et al."A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands".CATENA 174(2019):248-258. |
入库方式： OAI收割
来源：地理科学与资源研究所
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