Objective  The paper aims to provide method for estimating the soil conductivity of lakeside oasis soil by hyperspectral, so as to realize the rapid estimation of regional soil salinity.

  Method  Combined analysis of soil conductivity values and soil hyperspectral data, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and genetic algorithm (GA) were used to screen the characteristic bands of soil conductivity. Based on the full band and characteristic band, three machine learning algorithm models, inlcuding BP neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM), were constructed, and the partial least squares model (PLSR) was introduced for comparing their accuracy.

  Result  The soil conductivity ranged from 0.20 to 17.22 mS cm⁻¹ in the study area, with an average value of 2.61 mS cm⁻¹ and a coefficient of variation of 134.87%, showing strong variability; The characteristic bands screened by the CARS, SPA, and GA algorithms compress the modeling input to 0.87%, 1.68%, and 0.70% of the total number of bands, respectively, which reduced the amount of modeling input and increased the modeling speed. The choice of variable method CARS > SPA > GA; The three machine learning algorithm models were all better than PLSR model. The coefficient of determination (R²) increased by 20.57% and the relative percent deviation (RPD) increased by 17.84% on average. The CARS-SVM was the best model for soil conductivity hyperspectral estimation, with R² of 0.76 and 0.75 for training set and validation set, respectively, RMSE of 1.79 mS cm⁻¹ and 1.68 mS cm⁻¹, and RPD of 2.04 and 2.00, respectively; The soil conductivity hyperspectral estimation model with a soil depth of 20 ~ 30 cm has the highest accuracy, with R² of 0.83 and 0.84 for training set and validation set, respectively, RMSE of 1.37 mS cm⁻¹ and 1.77 mS cm⁻¹, and RPD of 2.41 and 2.50, respectively.

  Conclusion  The soil conductivity hyperspectral estimation model based on CARS-SVM has high accuracy and optimal estimation ability, which can provide a scientific reference for the estimation of soil conductivity in lakeside oasis.