| In arid and semi-arid agricultural regions, soil salinization is not only one of the most important problems of land degradation, but also closely related to the ecological environment. The soil salt content is one for the description of salt in the soil parameters, also the most key indicators to determine the degree of soil salinity. The analysis of accurate and reliable on the soil salt content is the foundation of all relevant saline work. Remote sensing as an effective technical means of monitoring regional soil salinity, it can rapidly, real-time access to saline alkali land information, such as the nature, scope, degree of salinization and so on. This research based on ETM image and the soil sample salinity data to establish the soil salinity remote sensing inversion model to monitor the soil salinization degree of arable land in Sanggan River Valley, it is of great significance to governance saline soils, preventing further degradation, reasonable development and utilization of saline soil resources, and maintain ecological sustainable development.This study use the remote sensing images on the Sanggan River Valley to analyse spatial distribution characteristics of salt content in the soil. The pretreatment was carried on the ETM remote sensing images, which including radiometric correction, geometric correction, mosaic and so on. Extract arable land and the real surface reflectance of soil sampling points. The basin is divided into two regions according to the soil type in the study area, then analyse salt content in the soil of the sampling points, spectral values of the corresponding remote sensing image and various transformation indicators of two regions in Sanggan River Valley by using SPSS19.0software, remote sensing image respectively to two regional soil sampling points and the corresponding salt content by using the software of spss19.0value and various transformations of multiple stepwise regression analysis, the inversion model of the salt content in the soil of the respective. The soil salinity content inversion in the study area using the ETM model in the image, to obtain spatial distribution characteristics of soil salinization information. On the basis of saline alkali degree classification standard to classify the soil salt content, and make the salt content in the soil class map, in order to offered basis for saline alkali soil improvement, dynamic simulation and prediction in the Valley. The main results are as follows:(1) Use6S model to atmospheric correction for the remote sensing image, compared with other methods, can be more accurately obtain the features of the true surface reflectance. Use the iterative approximation method to figure out the aerosol optical thickness are respectively0.903and1.194.(2) By supervised classification and unsupervised classification method for extraction of arable land, can better handle synonyms spectrum phenomenon. Accuracy test conducted by the overall classification accuracy and Kappa coefficient, the results were83.6%and0.7103, which explains the classification meet the accuracy requirements.(3) The soil salinity inversion model of two sub-study area in Sanggan River Valley are as follows1) Chestnut soil type in the study area salinity inversion model (Ri indicates that the I band of the real surface reflectance of the surface) for: Y=26.751R5+40.619(logR5)’-1.374/R7+7.924(R2=0.613)2) Soil type in the study area soil salinity inversion model: Y=41.911ogR3-56.126(logR5)’-12.481/R7+25.014(R2=0.565)Compared with the saline content estimation based on geostatistics method, results showed that the accuracy of salt content by use of ETM images is more consistent with the actual situation.(4) Arable soil salinity layers have different spatial correlation,5-10cm and10-20cm had medium spatial correlation, and has strong spatial correlation of0-5cm and20-40cm. Yingxian County land area in light saline alkali land, the area close to417.04square kilometers, moderate saline alkali land area of about38.38square kilometers. |
PDF Full Text Request