Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables
文献类型：期刊论文
| 作者 | Yang, Chenconghai2; Yang, Lin2,3; Zhang, Lei2; Zhou, Chenghu1,2 |
| 刊名 | GEODERMA
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 437页码:14 |
| 关键词 | Soil organic matter Digital soil mapping INLA-SPDE Soil moisture indices NSDSI Uncertainty assessment |
| ISSN号 | 0016-7061 |
| DOI | 10.1016/j.geoderma.2023.116571 |
| 通讯作者 | Yang, Lin(yanglin@nju.edu.cn) |
| 英文摘要 | Generating accurate spatial information on soil organic matter (SOM) is increasingly important in the context of global environmental change. Both prediction models and environmental covariates influence the mapping results and accuracy, making them important factors in SOM mapping. The Bayesian spatial model INLA-SPDE is an emerging model, that has shown potential in digital soil mapping (DSM), but its application is still limited. Soil moisture, which affects soil water status and the decomposition of SOM, can be a potential predictor for mapping SOM. However, the difficulty of obtaining soil moisture measurements over a large area using groundbased methods hinders its application. Recently, high spatial resolution remote sensing (RS) has provided a possible way to generate soil moisture indices over a large area. However, the effectiveness of RS-based soil moisture indices on SOM mapping is unknown. Fourier transforms decomposed (FTD) variables based on vegetation indices have been proven effective in detecting time-series patterns of crop growth, thereby improving the mapping accuracy of farmland. Yet, the effectiveness of FTD variables has not been verified in other vegetation-covered areas. This paper examines the use of INLA-SPDE with three RS-based soil moisture indices (NSDSIs) and six FTD variables for SOM mapping compared to Random Forest (RF), in a study area with diverse vegetation cover in Anhui Province, China. The finding indicates that with the optimal combination of environmental covariates, INLA-SPDE yields a higher prediction accuracy than RF, with an increase of 18% in R2. Either the RS-based soil moisture indices covariates or the FTD variables are effective in mapping SOM. When compared to using only natural environmental covariates, the best combination including RS-based soil moisture indices and FTD variables improved the mapping accuracy by 25% in terms of R2, 21% of LCCC, and 11% of RMSE. Furthermore, quantitative prediction uncertainty maps are derived based on the INLA-SPDE. This study demonstrates the effectiveness of INLA-SPDE model with the RS-based soil moisture indices and Fourier transforms decomposed variables for SOM mapping. |
| WOS关键词 | UNCERTAINTY ; CARBON ; MODEL ; FOREST ; REFLECTANCE ; PREDICTION ; FRACTIONS ; INFERENCE ; CROPLANDS ; RETRIEVAL |
| 资助项目 | National Key Research and Develop-ment Program Plan[2022YFC3800802] ; National Natural Science Foundation of China[41971054] ; Funda-mental Research Funds for the Central Universities[0209-14380115] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001032555600001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Develop-ment Program Plan ; National Natural Science Foundation of China ; Funda-mental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/195778]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang, 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.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 3.Nanjing Univ, Frontiers Sci Ctr Crit Earth Mat Cycling, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Chenconghai,Yang, Lin,Zhang, Lei,et al. Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables[J]. GEODERMA,2023,437:14. |
| APA | Yang, Chenconghai,Yang, Lin,Zhang, Lei,&Zhou, Chenghu.(2023).Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables.GEODERMA,437,14. |
| MLA | Yang, Chenconghai,et al."Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables".GEODERMA 437(2023):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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