The Study Soil Salinization Hyperspectral Technology

Soil salinization is the most common process of land degradation, natural factors or human improper use can cause soil salinization, pose risks to the enviro nment. This phenomenon is mainly due to high temperatures, strong soil evaporation, high water table, scarce rainfall. In arid and semi-arid regions, the evaporation of water to the surface with salt water into steam into the air, and a lot of salt accumulation in the soil surface, the formation of soil salinization. Salinization of soil is not only low yield, low fat, low crop survival and sustainable use of poor, therefore, the implementation of soil salinization monitoring is necessary. Traditional monitoring methods not only requires a lot of manpower, material and financial results obtained both surface and not representative, with the birth and development of hyperspectral technology gradually make up for these deficiencies. In this paper, the typical Xinjiang salinization Irrigation District for the study, using a portable spectrometer to obtain the spectral curve of soil water and soil salt content in eight different textures texture ion content, using the first derivative, second derivative, in addition to the continuous processing of statistical data methods of soil transform the raw spectra studied using hyperspectral technology content synchronization acquisition salinization of soil moisture, salinity and eight ions. The main conclusions are as follows:(1) From the different moisture content gradient, both the hyperspectral reflectance of soil in the land of germplasm with the increase of water content showed a trend of decline and is bigger than the variations in the reflectivity of sandy loam; From the perspective of different salt contents, with the increase of salt content in two species of hyperspectral reflectance generally showed increasing trend; From the point of water and salt content of different gradient, the original water hyperspectral reflectance salt and salt water content shows no obvious correlation.(2) The raw spectral data processing and feature analysis water and salt through the raw spectral data first derivative, second derivative and statistics in addition to the continuous reprocessing correlation coefficients were significantly increased, but the rate of increase for the three data after the original spectrum of different water and salt content of processed correlation coefficients were significantly different. In the loam, the water content of 0% and 10% moisture content and salt in addition to the spectral curve through continuous statistics, 15% moisture content and salt spectral curves take the first derivative, and salt water content of 19% after the second order differential spectral curve after help to improve the correlation coefficient. In sandy soil, water and salt spectral curve 0% water content by continuous addition statistics, 10% moisture and 15% moisture and 19% moisture content and salt spectral curve of the correlation coefficient after the second order differential treatment more a significant increase.(3) Establish a multiple linear regression model select the first derivative, second derivative and continuous statistics except after significant band established a multiple linear regression model, when its salt content of loam <6.38 m S/cm moisture prediction models were 0.85,0.91,0.89 R2, 0.93,0.71, sandy soil salinity <7.45 m S/cm when its moisture prediction model of R2 respectively 0.83,0.95,0.87,0.80,0.74,0.78 achieve significant correlation; in the salt water of different prediction models, loam soil moisture content <12% predicted their salt content R2 respectively 0.89,0.92,0.82,0.73, sandy soil water content <16% predicted when its salt content R2 respectively 0.82,0.90,0.78,0.86,0.68 achieve significant correlation; sync prediction model in water and salt, water content <10%, salt content of <5.83 m S/cm loam and salt content synchronization forecast R2 cm reaches 0.84, sandy soil water content <13% salt content <7.45 m S/cm, the water and salt content sync prediction R2 of 0.90, a better prediction results.(4) Saline soil in the anion content in the soil with the highest is the Cl-, total anion percentage of 51.21%, followed by SO42-, HCO3-, respectively 45.86% and 2.93% of the total anion; In the cation, is the highest levels of Na+, accounting for 60.51% of the total cation, followed by K+, Ca2+ and magnesium, accounted for 24.87%, 13.74% and 0.88% of the total cation. Show that the regional soil types of sulfate, chloride salt. Of raw salt ions and the spectral reflectance of correlation analysis showed that the original seven ion original correlation coefficient and spectral reflectance didn’t show good correlation, and so on the first order and second order differential and continuous statistics in addition to processing, the results showed that K+, Na+ and Cl-, HCO3-, Ca2+, SO42- after the first order differential treatment is more advantageous to sensitive wave band.