Recognition of potential outliers in soil datasets from the perspective of geographical context for improving farm-level soil mapping accuracies
文献类型：期刊论文
| 作者 | Wang, Yongji; Qi, Qingwen4,5; Zhou, Lin3; Wang, Meizi3; Wang, Qinqin3; Wang, Jun2 |
| 刊名 | GEODERMA
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 431页码:116374 |
| 关键词 | Digital soil mapping (DSM) Soil samples Outlier recognition Geographical context Local spatial autocorrelation and heterogeneity |
| DOI | 10.1016/j.geoderma.2023.116374 |
| 文献子类 | Article |
| 英文摘要 | Soil datasets with outliers lead to inaccurate farm-level digital soil mapping (DSM) results. Existing methods identify potential outliers in soil datasets based on expert experience or simple statistics that neglect the geographical characteristics of soil. In this paper, a novel potential outlier recognition method was developed from the perspective of geographical context. First, spatial search distance was automatically determined by the spatial distance among soil samples. Second, similarities of adjacent soil samples and the local spatial variation level were comprehensively considered to calculate outlier scores. Finally, a frequency histogram of outlier scores was generated to determine a suitable threshold for recognizing potential abnormal samples. To validate the proposed method, it was compared to Lambda and Box-Plot methods, and the ordinary kriging method was used to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus and available potassium, in an agricultural region. Then, a synthetic study using artificially contaminated DEM data was also conducted. The comparative experiment shows that the proposed method is better able to recognize potential outliers by mining the local spatial structure, as indicated by lower mean absolute error (MAE) and root mean square error (RMSE) values. It can be concluded that consideration of local spatial autocorrelation and heterogeneity is helpful in recognizing potential outliers. |
| WOS关键词 | SAMPLES |
| WOS研究方向 | Agriculture |
| WOS记录号 | WOS:000936689400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200752]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China 2.Henan Agr Univ, Coll Plant Protect, Zhengzhou 450002, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Zhengzhou Univ, Sch Geosci & Technol, Zhengzhou 450001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yongji,Qi, Qingwen,Zhou, Lin,et al. Recognition of potential outliers in soil datasets from the perspective of geographical context for improving farm-level soil mapping accuracies[J]. GEODERMA,2023,431:116374. |
| APA | Wang, Yongji,Qi, Qingwen,Zhou, Lin,Wang, Meizi,Wang, Qinqin,&Wang, Jun.(2023).Recognition of potential outliers in soil datasets from the perspective of geographical context for improving farm-level soil mapping accuracies.GEODERMA,431,116374. |
| MLA | Wang, Yongji,et al."Recognition of potential outliers in soil datasets from the perspective of geographical context for improving farm-level soil mapping accuracies".GEODERMA 431(2023):116374. |
入库方式： OAI收割
来源：地理科学与资源研究所
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