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.