Study On Surface Soil Moisture Jnversion Of Salinization Of Farmland Based On Multi-temporal Remote Sensing Images

Soil moisture plays an important role in agricultural production, so its monitoring is always a focus. Remote sensing technology will become the main means of area soil moisture monitoring, with its advantage of frequent and lasting provide surface area information. This paper selectes salinization irrigation district-Hetao Irrigation District as study area and selectes different periods of landsat8images in2013as data source. The land useful types, the vegetation structure and degree of salinity of the main crop and forest were classified appling of decision tree classification method and combination of field sampling and lab experiments. This paper studies the applicability of surface moisture inversion method under different vegetation cover during the year in the study area and determines the temporal and spatial distribution of surface soil moisture. The research result has theory and application value of temporal and spatial variation of surface soil moisture monitoring by remote sensing in Hetao Irrigation District.The paper analyzes surface soil salinization types of farmland and salt wasteland combination of field sampling and lab experiments. The results show that farmland salinization types is mostly sulfate-chloride and wasteland salinization types is mostly chloride and the main ingredient of wasteland salinity is sodium chloride. The paper analyzes soil texture, soil moisture, soil salinity on soil reflectance effects, the results show that the main factors influencing the soil reflectance is soil water and soil salt. Results showed that salinity is the main factor of affecting soil reflectance in the salt peak of spring.The paper selectes different periods of landsat8images as data source and extracts vegetation coverage region of remote sensing images at different crop growth stages appling of decision tree classification method. The paper overlay analyzes vegetation coverage region and obtains full vegetation coverage area. The land useful types, the vegetation structure and degree of salinity of the main crop and forest were classified appling of decision tree classification method. Classification results are similar with the statistical results. The results showed that the decision tree method has higher classification accuracy.When surface is exposed or low vegetation coverage, soil humidity component of tasseled cap and SMMRS method is applied to inverse surface soil moisture. The results show that the two methods are applicable to non salinization or mild salinization soil and the soil of vegetation coverage less than about25%, and precision of humidity component method was significantly higher than that of SMMRS method.When surface is e vegetation coverage, the paper selects the thermal infrared data of Landsat8images as data source and inverses the land surface temperature appling atmospheric correction method. The paper constructs temperature-vegetation feature space using respectively the normalized difference vegetation index NDVI and enhanced vegetation index EVI and Ts. The EVI-Ts feature space plot is more concentration than The NDVI-Ts feature space plot in the same image, because that EVI value of high plant coverage pixel less than NDVI value of that. The paper extracts feature space dry and wet edge appling Matlab software and dry and wet edges equation is fitted by Excel. The TVDI value is calculated to invers surface soil moisture. The results show that the two TVDI value of NDVI and EVI calculation respectively has high inversion accuracy. The inversion accuracy of TVDI value calculated by EVI is slightly higher than that of TVDI computed by NDVI.