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| Sampling optimization method for soil environmental quality monitoring based on feature representativeness |
| Received:February 11, 2023 |
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| KeyWord:spatial sampling;Latin hypercube sampling method;auxiliary data;characteristic representation;sample layout optimization |
| Author Name | Affiliation | E-mail | | CHU Yuting | School of Science, China University of Geosciences, Beijing 100083, China
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | | | LI Xiaolan | Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | lixl@nercita.org.cn | | LIAN Hairong | School of Science, China University of Geosciences, Beijing 100083, China | | | PAN Yuchun | Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China | |
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| Abstract: |
| For improving the representativeness of monitoring sites to accurately obtain soil information and effectively implement management measures, this paper proposed a sampling optimization method for soil environmental quality monitoring based on feature representativeness. In this method, the feature space based on soil auxiliary variables was first built, and a sample sequence was established with the stratified impact of the feature space, and then a highly representative sampling scheme was obtained by the point-by-point sampling pattern. An example which optimizes monitoring sites using soil type, soil texture, land use type, and slope as auxiliary variables was taken in this study, and the results of the proposed method, simple random sampling, spatial stratified sampling, and conditional Latin hypercube sampling methods(cLHS) was compared. It showed that the suggested method improves the feature space representativeness by approximately 15% on average and takes much less time than the cLHS but slightly higher than the simple random sampling method and spatial stratified sampling method. The sampling distribution features of heavy metal concentration are more in line with the total, and the sampling distribution has substantially less uncertainty than that with the other three methods. The proposed method can significantly improve the representativity of monitoring sites in the feature space, which can effectively reflect the overall distribution characteristics of the survey area soil attributes, and it provides a reference means for the effective investigation and monitoring of soil information. |
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