Representative revision of soil samples based on estimation of kernel density

LI Kun; CHEN Yuhao; LI Wenyue; WANG Ziying; FU Peihong; HUANG Wei

doi:10.13300/j.cnki.hnlkxb.2025.01.010

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Representative revision of soil samples based on estimation of kernel density

Resources and environment | 更新时间：2026-11-05

- Representative revision of soil samples based on estimation of kernel density
- Journal of Huazhong Agricultural University Vol. 44, Issue 1, Pages: 94-104(2025)
- 作者机构：
  
  华中农业大学资源与环境学院，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.01.010
  CLC： S159.9
- Received：05 December 2023，
  
  Published：15 January 2025
- 稿件说明：
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李坤，陈宇昊，李文岳，等.基于核密度估计的土壤样本代表性修正研究［J］.华中农业大学学报，2025，44（1）：94‐104.

LI Kun,CHEN Yuhao,LI Wenyue,et al.Representative revision of soil samples based on estimation of kernel density[J].Journal of Huazhong Agricultural University,2025,44(01):94-104.
李坤，陈宇昊，李文岳，等.基于核密度估计的土壤样本代表性修正研究［J］.华中农业大学学报，2025，44（1）：94‐104. DOI： 10.13300/j.cnki.hnlkxb.2025.01.010.

LI Kun,CHEN Yuhao,LI Wenyue,et al.Representative revision of soil samples based on estimation of kernel density[J].Journal of Huazhong Agricultural University,2025,44(01):94-104. DOI： 10.13300/j.cnki.hnlkxb.2025.01.010.

摘要

为充分利用历史样点数据获取更可靠的土壤-环境知识，进而获取更高精度的土壤预测推理图，采用样本代表性修正方法获取更高的知识精度，利用样本空间与总体空间环境协变量的空间相似度关系，以核密度估计为基础，采用3种不同算法对每个土壤采样点探寻最优权重，并以土壤表层有机质含量预测制图为例验证方法的科学性和有效性。结果显示，该修正方法最高可将多元线性回归制图的RMSE和MAE分别降低10.30%和12.74%，证实了该方法的可行性与有效性。

Abstract

How to obtain more reliable soil-environment knowledge from existing historical samples has become an important scientific issue in digital soil mapping. This article used the method of revising the representativeness of samples to obtain higher accuracy of knowledge. Three different algorithms and the spatial similarity relationship between the covariates of the sample space and the overall spatial environment were used to identify the optimal weights for each sampling point of soil based on the estimation of kernel density. The prediction mapping of the content of organic matter on the surface of soil was used as an example to verify the scientific and validity of the method. The results showed that the revised method reduced RMSE and MAE of multiple linear regression mapping by 10.30% and 12.74%， confirming the feasibility and validity of this method. It will provide technical support for processing the data from sampling points of soil to make full use of historical data and improve the accuracy of mapping soil.

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references

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