[A Quantitative Analysis of the Reflectance of the Saline Soil under Different Disturbance Extent].

The reflectance of saline soil in the downstream of No.500 reservoir in Fukang, Xinjiang province was investigated. Through filed sampling and spectral test, using the method of spectral transform, correlation analysis and a quantitative analysis were conducted on the salt and water content of the soil under different disturbance degree. A multiple linear regression model was established between the soil reflectance and soil salinity content. The results show that: first, the human disturbance has a significantly positive correlation with the soil content while it has an extremely negative correlation with the water content. The correlation coefficients are 0.961 and -0.929 respectively. Secondly, it shows that those most heavily disturbed soil reflectance is about 10%higher than the slightly disturbed, while the slightly disturbed soil reflectance is about 17% higher than the undisturbed soil. The reason is that the soil surface of barren land with a small amount of vegetation, the biological creature and soil surface crust have been destroyed. The more the disturbance is, the greater chance the surface layer would be destroyed. Meanwhile, the surface layer of soil will be lack of the crust protective; the soil salinity of the bottom rises to the surface associated with the soil moisture will quickly evaporate. The salt is concentrated to the surface layer due to both little precipitation and a lack of protection of soil crust. Thirdly, the peak wavelength location of the spectrum is increased (999, 876~979, 1 182~1 370, 1 900 nm) while the soil is taken from undisturbed to heavily disturbed conditions, which means that with the increase of disturbance, the soil becomes more sensitive in the near infrared region. What’s more, the three different prediction models are established though the reflectance R, the reflectivity of the first derivative R′, the reflectance R+water. According to the R(2) and the RMSE to comprehensive judge the accuracy of the model. It is found that among those established prediction models of the same soil salinity in the different levels of disturbance, the smaller the degree of human disturbance is, the higher the accuracy of model is. It is found that among all of those established prediction models, the one based on the derivative of R works the best, of which R(2) is larger than 0.983, model accuracy is improved by 5%~10% ,which means that through a derivative transformation, the linear noises in the original spectrum can be removed.