Including soil depth as a predictor variable increases prediction accuracy of SOC stocks
文献类型：期刊论文
| 作者 | Li, Jiaying7,8,9; Liu, Feng6; Shi, Wenjiao1,5,9; Du, Zhengping4; Deng, Xiangzheng5,9; Ma, Yuxin3; Shi, Xiaoli7,8; Zhang, Mo4,5; Li, Qiquan2 |
| 刊名 | SOIL & TILLAGE RESEARCH
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 238页码:17 |
| ISSN号 | 0167-1987 |
| 关键词 | High accuracy surface modelling Interpolation Linear model Machine learning model Soil organic carbon stocks |
| DOI | 10.1016/j.still.2024.106007 |
| 通讯作者 | Shi, Wenjiao(shiwj@lreis.ac.cn) |
| 英文摘要 | Accurate estimates of soil organic carbon (SOC) stocks are important in understanding terrestrial carbon cycling. Based on the fundamental theorem of surfaces, an alternative method, high accuracy surface modelling (HASM) combined with soil depth information was applied to predict the spatial pattern of SOC stocks in Hebei Province, China. In this study, we collected 434 soil samples and key environmental covariates related to soil-forming factors (soil, climate, organisms, topography, and soil depth information) in the study area, and compared the accuracy of 16 spatial prediction models (including single models, hybrid models, and HASM combined with single or hybrid models) on the spatial distribution of SOC stocks. The results confirmed that the method of HASM combined with the generalized additive model (GAM) with soil depth covariate (HASM_GAMD) achieved a better performance than other methods at soil depths of 0-30, 0-100 and 0-200 cm. The root-mean-square error and coefficient of determination values of predicting the spatial pattern of SOC stocks by the HASM_GAMD model demonstrated a 43% and 49% improvement, respectively, compared with models without depth information. The prediction uncertainty of the HASM_GAMD model based on 90% prediction interval was lower than that of other models. The HASM_GAMD model excels in addressing not only the nonlinear relationship between covariates and SOC stocks, but also in incorporating point observation data that varies with soil depth. Furthermore, the model conducts modelling by integrating surface and optimal control theories. Results obtained from the HASM_GAMD demonstrated that the SOC stocks in Hebei Province amounted to 1449.08 Tg C. Our study introduces an alternative model for modelling of SOC stocks and our findings are a valuable reference for assessing carbon stocks in Hebei Province to support sustainable land management and climate change mitigation. |
| WOS关键词 | ORGANIC-CARBON STOCKS ; HEBEI PROVINCE ; REGIONAL-SCALE ; LANDSCAPE ; MODELS ; CHINA ; UNCERTAINTY ; PATTERNS ; DENSITY ; TOPSOIL |
| 资助项目 | National Natural Science Foundation of China[41930647] ; National Natural Science Foundation of China[72221002] ; National Natural Science Foundation of China[42071072] ; Major Research Project of Humanities and Social Sciences Research of Hebei Education Department[ZD202412] ; Scientific Research Foundation for the Returned Overseas Chinese Scholars of Hebei Province[C20230347] ; State Key Laboratory of Resources and Environmental Information System |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001174772600001 |
| 资助机构 | National Natural Science Foundation of China ; Major Research Project of Humanities and Social Sciences Research of Hebei Education Department ; Scientific Research Foundation for the Returned Overseas Chinese Scholars of Hebei Province ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203159]  |
| 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, 11,Datun Rd, Beijing 100101, Peoples R China 2.Sichuan Agr Univ, Coll Resources, Chengdu 611130, Peoples R China 3.Manawatu Mail Ctr, Landcare Res, Private Bag 11052, Palmerston North 4442, New Zealand 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 6.Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Peoples R China 7.Hebei Normal Univ, Hebei Technol Innovat Ctr Remote Sensing Identific, Geocomputat & Planning Ctr, Shijiazhuang 050024, Peoples R China 8.Hebei Normal Univ, Sch Geog Sci, Hebei Key Lab Environm Change & Ecol Construct, Shijiazhuang 050024, Peoples R China 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jiaying,Liu, Feng,Shi, Wenjiao,et al. Including soil depth as a predictor variable increases prediction accuracy of SOC stocks[J]. SOIL & TILLAGE RESEARCH,2024,238:17. |
| APA | Li, Jiaying.,Liu, Feng.,Shi, Wenjiao.,Du, Zhengping.,Deng, Xiangzheng.,...&Li, Qiquan.(2024).Including soil depth as a predictor variable increases prediction accuracy of SOC stocks.SOIL & TILLAGE RESEARCH,238,17. |
| MLA | Li, Jiaying,et al."Including soil depth as a predictor variable increases prediction accuracy of SOC stocks".SOIL & TILLAGE RESEARCH 238(2024):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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