Electrical conductivity (EC) is not only an important index to evaluate
the degree of soil salinization, but also an essential basis for judging
whether saline soil can be improved and assess the effect of improvement
efforts. Satellite remote sensing provides much information for large
scale EC inversion of saline soil, which enables the possibility for
evaluating the degree and distribution of soil salinization. Taking the
salinized region of western Jilin Province as the study area, 328
salinized soil samples were collected, and the EC was measured in June
2019. The construction of the optimal spectral parameters was based on
the correlation between the conductivity and the spectral reflectivity
of Sentinel-2 MSI data; after satisfying the normal distribution for the
Box-Cox transformation of EC, the inversion model of EC was established
by using linear regression model, support vector machine (SVM),
regression tree (RT), Gaussian process regression (GPR), and ensemble
tree (ET). The verification results of the model on the validation set
showed that the performance of GPR was optimal (R2 = 0.66, RMSE = 0.48
mS/cm, MAE=0.52 mS/cm), which increased R2 by 29.04% compared with the
traditional linear regression model. Finally, according to the GPR
model, the EC results of pixel-level resolution (10 m × 10 m) of saline
soil in western Jilin Province were inversed, which provided a
scientific basis for the study of the distribution characteristics and
improvement scheme of saline soil.
The dynamic programming management model of groundwater with covariates: a case study for the Songyuan area, Jilin Province, China
