Models For The Remote Sensing Of Salinization In Yinchuan Plain

Soil salinization is one of the main types of land desertification and degradation, and it leads to the land desertification in arid and semi-arid area, so it is particularly essential to investigate and control desertification. To establish the remote sensing monitoring model for soil salinization, typical soil salinization area in Pingluo County of Nningxia Province in China was taken as the study area, and the spectral data measured in the field, the value of pH and salinity measured in the laboratory as the basic data. Hyperspectral data processing method was used to analyze the spectral characteristics of different levels of soil salinization. Spectral data were transformed with 11 different approaches, including reciprocal, logarithm, root mean square and their first order differentials, etc. After the transformation, the correlation analysis was carried out between the obtained soil spectra and soil salinity. The most sensitive band was selected, and the field spectral sensitive band and soil salinity were used and the multiple linear regression was employed to establish the spectral quantitative models for evaluating the soil salinization degrees. Through the study, results show that:(1) The measured spectral data data combined with soil salinity, salinization soil can be divided into the salinization of soil, mild salinization of soil, moderate, severe soil salinization soil four types. Different levels of salinization of soil spectral characteristic curve basic converge on the form; In the visible light wave band, the reflectivity of different soil salinization is not showed regular changes.(2) The measured spectral data of soil and vegetation were transformed in 22 different approaches, including bottom, logarithmic, logarithmic reciprocal, RMS four forms of transformation, and first order differentiation. The reflectivity data in the form of 22 kinds of transformation and correlation analysis of the soil salinity data, various forms of transformation results show that the spectral correlation after the differential transform is best.(3) the reciprocal first order differential of measured soil spectral is the most sensitive to soil salinization degrees. The spectral quantitative models based on the wavelengths of 940 nm and 1094 nm are the best.(4) After the transformation, the obtained soil and vegetation characteristics spectra that correlate well with soil salt content, built soil index and many vegetation index. By comparing various spectral transformations, the first order differential of soil spectral was the most sensitive to soil salinization degrees. The model was based on the spectral index, including SI and MSAVI, and it could monitor soil salinization accurately. The soil salinization could be achieved rapidly in the area.