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Abstract Soil salinization is one of the important factors that affect the soil health in the arid area, so it is very important to obtain the information of soil salinity and monitor the change of soil salinity for the rational use of land resources and soil restoration in the arid area. Based on 52 soil samples collected in the field and Landsat 8 OLI remote sensing images obtained at the same time, the correlation and curve regression analysis were used to quantitatively analyze the correlation and fitting degree between the soil salinization evaluation index based on multispectral remote sensing data and the measured soil Electrical Conductivity (EC). The results are as follows: ① The soil salinity in the study area is relatively light, and the total proportion of non-salinized and slightly salinized soil samples is 82.68%; ② The correlation between salinity index and soil EC is higher than that of vegetation index. The correlation between salinity index S3 (S3), salinity index S5 (S5), salinity index S6 (salinity index, S6) and salinity index Si (salinity index, SI) is above 0.50; ③ Salinity indexes S2 (S2), S3, S5 and Si have the highest fitting degree with soil EC in the whole sample, among which S5 has the best performance (R2 = 0.41). The fitting degree of index and soil EC increases significantly with the increase of soil salinity under different salinity levels. The highest fitting degree of salinity index and soil EC is S1 (R2 = 0.73) and S2 (R2 = 0.72); ④ In the fitting model, the evaluation index and soil EC calculated based on cubic model, quadratic model and S model has a high fitting degree. This study has analyzed the applicability of various soil salinization evaluation indexes in soil salinity monitoring of Yinbei irrigation area, and the preliminary conclusions can provide reference for remote sensing monitoring of soil salinity in Yinbei irrigation area of Ningxia.
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Keywords
soil salinization
salinity index
vegetation index
curve fitting
Landsat
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Issue Date: 21 July 2021
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