基于特征筛选算法的数字土壤制图研究
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华中农业大学资源与环境学院

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基金项目:

国家自然科学基金项目(42171056,41877001)资助


Research on Digital Soil Mapping Based on Feature Selection Algorithm
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College of Resource and Environment, Huazhong Agricultural University

Fund Project:

National Natural Science Foundation of China (Nos. 42171056,41877001)

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    摘要:

    平缓地带数字土壤制图中,环境协变量的选择是提高制图精度的关键。已有研究证明遥感影像可作为推理制图的辅助因子,而如何确定环境因子推理制图时各自的权重已成为现阶段研究的重点。选取湖北省麻城市乘马岗镇为研究区,采用3种特征筛选方法进行有效环境变量筛选,探索参与平原-丘陵混合区域制图的因子并确定其重要性,依据选择的相对稳定的指标,进一步探索提高土壤类型制图准确性的途径。根据141个野外独立样点的检验结果表明:在推理制图中,遥感因子在平原区域的重要性程度高于丘陵区域,且遥感因子中归一化植被指数(NDVI)和均值(Mean)较为稳定;基于递归特征算法的按地形推理制图精度最高为75.89%,分别高于ReliefF算法和基于Tree的特征筛选算法13.48%和4.97%;此外3种特征筛选算法制图结果中,按地形因子分区制图的精度均高于整体区域制图。因此,遥感因子作为辅助手段参与推理过程可有效提高制图精度。本研究采用的特征挖掘与机器学习算法对提升土壤制图精度具有一定的理论意义。

    Abstract:

    【Objective】Traditional digital soil mapping methods are unable to produce detailed soil maps within a reasonable cost and time. Digital soil mapping is a powerful technique, which is popular and widely used by scholars coupled with environmental covariates to map soil types or properties. The selection of environmental covariates is the key to ensuring the accuracy of mapping. Previous studies have proven that remote-sensing images can be used as auxiliary factors for reasoning mapping. Remote sensing data can provide rich soil landscape information, which is consistent with the core idea of using grids to express spatial changes of soil features in digital soil mapping. Moreover, remote sensing technology can obtain real-time information quickly. However, there are few relevant studies on how principal components and texture information of remote sensing factors contribute to the reasoning process. Thus, determining the weight of remote sensing factors in the reasoning process is the key content of this study, which is tested by the reliability of testing mapping results.【Method】Chengmagang Town, Macheng City, Hubei Province was selected as the study area. Using Chinese soil classification and soil type map with a spacing of 10 meters, which were extracted from the contour data and remote sensing image using a variety of feature selection algorithms to effective screening of variables, this study conducted the soil digital mapping by reasoning machine learning algorithms. Specifically, the recursive feature elimination screening algorithm, ReliefF algorithm and tree-based feature screening algorithm were used to rank all environmental factors in the whole area, plain and hilly areas of the study area, respectively. Then, it screened the effective environmental variables of environmental factors and analyzed the weight of remote sensing factors in the reasoning process. The factors involved in plant-hill region mapping were explored and their importance was determined. According to the selected relatively stable indicators, the gradient boosting decision tree model after parameter tuning of the Bayesian optimization algorithm based on TPE was used for modeling. Also, the mapping accuracy results after different feature screening algorithms were compared between the whole region and the terrain region to further explore ways to improve the accuracy of soil type mapping.【Result】The soil type inference map was verified by 141 independent field sampling sites. The results showed that the importance of remote sensing factors in the plain area was higher than that in the hilly area and the NDVI and Mean values of the remote sensing factors were relatively stable. The highest accuracy of topographical inference mapping based on the recursive feature algorithm was 75.89%, which was higher than the 13.48% and 4.97% of the ReliefF algorithm and tree-based feature screening algorithm, respectively. In addition, among the mapping results of the three feature screening algorithms, the accuracy of the mapping based on terrain factors was higher than that of the overall region mapping. It suggests that remote sensing factors as an auxiliary means to participate in the reasoning process can effectively improve mapping accuracy. 【Conclusion】This study uses a feature selection algorithm to select features with a strong correlation with soil types as auxiliary variables in the machine learning model. The method is efficient and cost-effective for soil type prediction. Compared to other methods, the soil type mapping method based on machine learning is advantageous and the feature mining and machine learning algorithms have theoretical significance and practical value.

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张晓婷,黄 魏,傅佩红,孟 可,王苏放.基于特征筛选算法的数字土壤制图研究[J].土壤学报,2024,61(3). DOI:10.11766/trxb202208090441 ZHANG Xiaoting, HUANG Wei, FU Peihong, MENG Ke, WANG Sufang. Research on Digital Soil Mapping Based on Feature Selection Algorithm[J]. Acta Pedologica Sinica,2024,61(3).

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  • 收稿日期:2022-08-09
  • 最后修改日期:2023-10-11
  • 录用日期:2023-10-20
  • 在线发布日期: 2023-10-23
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