干旱区生态环境知识资源中心

The present paper selects the Kuqa Oasis as the study area, studies spectrum characteristics of soil salinity, and establishes soil spectrum library. Through transforming and analyzing varying degrees of soil salinization reflectance spectra data in the typical study area, and selecting the most sensitive spectral bands in response to salinization, we established the measured hyperspectral soil salinity monitoring model, and by correcting the soil salinity monitoring model established by HIS image through scale effect conversion improved the model accuracy under the conditions of a regional-scale monitoring of soil salinization. The results show that both measured hyperspectral soil salinity monitoring model and HSI image soil salinity inversion model have good accuracy, model determination coefficient (R-2) is higher than 0. 57 and the model stability is better. Compared with the corrected HSI image soil salinity inversion model and uncorrected HSI image soil salinity inversion model, the coefficient of determination has been greatly improved, which increased from 0. 571 to 0. 681, and through the 0. 01 significance level, the root mean square error (RMSE) value is 0. 277. The correction HIS image soil salinization monitoring model can better improve the model accuracy under the condition of regional scale soil salinization monitoring, and using this method to carry out the soil salinization quantitative remote sensing monitoring is feasible, and also can provide scientific reference for future research.