Research On Saline-alkali Land Characteristics In Western Jilin Province Of China Based On Remote Sensing Data

The objects of saline-alkali land monitoring mainly includes the distribution, area and degrees of salinization of saline-alkali land. Traditional classification methods of land salinization only manually extract the sample data from the study area, which cannot obtain the classification results for large area. With the development of remote sensing technology, remote sensing data is often used to extract and analyze the information of saline-alkali soil. At present, most classification methods of land salinization utilize the spectral information of remote sensing data based on supervised classification or unsupervised classification, and the classification results of saline-alkali land still exist certain errors. In addition, researches on characteristics of soil moisture and salinity are also significant parts in saline-alkali land observation, but, at present, the studies on retrieval of soil salinity is relatively few. Passive microwave remote sensing data can reflect the dielectric properties of different land surfaces. Meanwhile, soil dielectric properties have high sensitivity to changes of soil moisture and salinity. As a result, passive microwave remote sensing data can be used for retrieval of surface soil moisture and salinity. In this paper, the spectral remote sensing data and passive microwave remote sensing data were utilized to detect the characteristics of saline-alkali land in Western Jilin Province. The specific details and the innovative results of the research are as follows:(1) Combining the sample data with Landsat TM images, the Western Jilin Province of China was selected as the study area in this paper. Through analyzing the relationship between the spectral characteristics and the content of soil salinity of the sample data extracted from different types of saline-alkali land, a land salinization classification method using the decision tree was proposed. The experimental results demonstrated that the proposed method can supply more accurate classification information of land salinization, and further effectively monitor soil salinization changes for the study area.(2) Western Jilin Province of China was selected as the study area in this paper. By the iteration between the rough surface reflectivity calculated based on satellite data and the rough surface reflectivity calculated based on land surface model, three parameters, including surface roughness parameter, soil moisture and salinity can be retrieved simultaneously. After comparing the retrieval results to the field measurement of salinity and FY satellite soil moisture product, the experimental results show that the surface roughness parameters of the study area were concentrated in the vicinity of 0.31. The error between the average of retrieved salinity and the average of the field measurement of salinity was about 10.52g/kg. The error between the average of retrieved soil moisture and the average of FY satellite soil moisture product was approximately 0.005 cm3/cm3.Compared with the traditional supervised and unsupervised classification method, a method using spectral remote sensing data combining ground experimental data was proposed to achieve the classification, distribution and area information of saline-alkali land. The real spectral characteristics of land features were mostly taken into consideration in this method, and the results could be more similar to the physical characteristics of actual land features. Compared with the ground radar and active microwave measurement, the method based on the passive microwave remote sensing data can realize the quantitative retrieval of soil moisture and salinity in a short period and a large measurement range. In some extent, this method also provided the actual research thought and practice experience for the actual thought of research train and practical experience for the follow-up studies on the statistics of saline-alkali land information.