丁玮祺, 吾木提·艾山江, 阿不都艾尼·阿不里, 尼加提·卡斯木. 基于机器学习的干旱区土壤盐渍化定量估算[J]. 土壤通报, 2022, 53(5): 1038 − 1048. DOI: 10.19336/j.cnki.trtb.2021102103
引用本文: 丁玮祺, 吾木提·艾山江, 阿不都艾尼·阿不里, 尼加提·卡斯木. 基于机器学习的干旱区土壤盐渍化定量估算[J]. 土壤通报, 2022, 53(5): 1038 − 1048. DOI: 10.19336/j.cnki.trtb.2021102103
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DING Wei-qi, UMUT Hasan, ABDUGHENI Abliz, NIJAT Kasim. Quantitative Estimation Soil Salinization in Arid Areas Based on Machine Learning[J]. Chinese Journal of Soil Science, 2022, 53(5): 1038 − 1048. DOI: 10.19336/j.cnki.trtb.2021102103
Citation: DING Wei-qi, UMUT Hasan, ABDUGHENI Abliz, NIJAT Kasim. Quantitative Estimation Soil Salinization in Arid Areas Based on Machine Learning[J]. Chinese Journal of Soil Science, 2022, 53(5): 1038 − 1048. DOI: 10.19336/j.cnki.trtb.2021102103
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基于机器学习的干旱区土壤盐渍化定量估算

Quantitative Estimation Soil Salinization in Arid Areas Based on Machine Learning

    摘要
  • 摘要:
      目的  探讨对土壤盐渍化进行快速、准确监测技术与方法。
      方法  利用353个地面表观电导率数据，以及从Worldview-2影像获取对应采样点的波段反射率值，结合两波段组合植被指数和三波段组合植被指数，筛选最佳二维、三维波段组合方式，引入人工神经网络、K近邻和支持向量回归来构建区域土壤盐渍化定量反演模型。
      结果  ① WV-2影像的红边和近红外波段与ECa呈现显著相关（P < 0.01）。② 二维植被指数（RVI(B5-B2)、NDVI(B6-B2)、DVI(B2-B6)）和三维植被指数（3DVI(B2-B6-B6)、3DVI(B3-B5-B6)、3DVI(B5-B2-B1)、3DVI(B2-B1-B6)、3DVI(B2-B1-B6)、3DVI(B6-B1-B2)、3DVI(B5-B3-B7)）的波段组合计算提高了其对土壤盐渍化的敏感性。③ 基于不同维度数据的机器学习估算模型中，3DVI和KNN算法结合对土壤盐渍化估算效果最为突出，且模型精度为R2 = 0.773，RMSE = 1.659 dS m−1，RPD = 2.216。
      结论  所构建的多维植被指数可应用于类似环境条件下盐渍土地监测和评价研究。

     

    Abstract:
      Objective  Soil salinization is one of the main factors causing land degradation and desertification, especially affecting the agricultural activities and land use management in arid and semi-arid regions. There was an urgent need for rapid, accurate and economical monitoring techniquces for soil salt.
      Method  The 353 data of surface apparent conductivity (Electronic Conductivity, ECa) were used, as well as the band reflectivity values of corresponding sampling points obtained from Worldview-2 (WV-2) images, combined with the combination of two-band combined vegetation index (Two- Dimensional vegetation index, 2DVI) and Three-Dimensional vegetation index (Three-Dimensional vegetation index, 3DVI). The best combination of two-dimensional and three-dimensional bands, artificial neural networks (Artificial Neural Network, ANN), K-nearest neighbors (K- Nearest Neighbors (KNN) and Support Vector Regression (SVR) are used to construct a quantitative inversion models of regional soil salinization.
      Result  ① The red edge and near-infrared band of WV-2 images were significantly correlated with ECa (ρ < 0.01). ② Two-dimensional vegetation index (RVI(B5-B2), NDVI(B6-B2), DVI (B2-B6)) and three-dimensional vegetation index (3DVI(B2-B6-B6), 3DVI(B3-B5-B6), 3DVI(B5-B2-B1), 3DVI(B2-B1-B6), 3DVI(B2 -B1-B6), 3DVI(B6-B1-B2), 3DVI(B5-B3-B7)) combined calculation of the bands to improve its sensitivity to soil salinization. ③ Machine learning estimation based on data of different dimensions in the model, the combination of 3DVI and KNN algorithm had the most prominent effect on soil salinization estimation, and the model accuracy was R2 = 0.773, RMSE = 1.659 ds m−1, RPD = 2.216.
      Conclusion  Vegetation index stablished in this study would be helpful to monitor and evaluate the saline land under similar environmental conditions.

     

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