干旱区生态环境知识资源中心

Soil salinity is one of the common factors leading to land degradation problems on earth, especially in arid and semiarid regions. There is an urgent need for rapid, accurate and cost-effective monitoring and assessment of soil salinization. Remote Sensing (RS) and Geographical Information Systems (GIS) are employed as viable technologies for detecting, monitoring, and predicting spatial-temporal patterns of soil salinization. The purpose of this study is to establish partial least squares regression (PLSR) models that are based on remotely sensed data and field measured electrical conductivity (ECa) and to retrieve soil salinity estimates by constructing an optimal model. First, the soil adjusted vegetation index (SAVI) was calculated based on WorldView-2 images. Second, a statistical regression method was applied to analyze the correlation between ECa and SAVI under different parameters. The SAVI that was measured as the most stable parameter was an optimum index. Finally, a PLSR prediction model of soil salinity was established based on the sensitivity bands, the optimum index and ECa. The results of this study are the following: ( a) According to the adjusted parameter (L = 100), the SAVI index illustrated the best correlation with ECa, and ECa was also significantly related to the bands (( Red Edge) Band6, ( Near-IR1) Band7 and ( Near-IR2) Band8) derived from a World-view-2 image. (b) The results of the PLSR predictive model calibration showed that the model-D performed best through the sensitivity bands and optimal index, with the highest coefficient of determination (R-2 = 0.67) and the smallest root mean square error (RMSE) of 1.19 dS center dot m(-1). The results indicated that the model-D that is constructed and applied in this paper could provide quantitative information for detecting and monitoring soil salinization in the Keriya Oasis and could also supply examples for the study of soil salinization in arid and semiarid regions with similar environmental conditions.