Abstract: Using Landsat-8 remote sensing data, the correlation of band reflectance, 2D and 3D indices with soil conductivity was analyzed based on three treatments： Raw spectra, first-order derivatives and second-order derivatives. The optimal spectral index was selected as the input parameter of the neural network algorithm, and the soil salinization prediction model was constructed based on MATLAB. The results showed that the 2D and 3D spectral indices had a higher correlation with the conductivity than the original spectra, and the overall correlation between the 2D and 3D indices constructed after the second-order derivative treatment and soil conductivity was better than that of the first-order derivative treatment and the original spectra. The accuracy of the model constructed by choosing B1 to B7 as the input parameters of the neural network algorithm under the original spectra was optimal, the correlation coefficients of the training set, validation set, test set and the whole were 0.732 4, 0.716 4, 0.444 5, 0.691 9, respectively, and the constructed model had high prediction accuracy when the soil conductivity was around 1 000 μS/cm.

Key words: soil salinization, Landsat-8, remote sensing derivative processing, optimal spectral index, neural network algorithm