Abstract
Soil salinization is one of the most common land degradation progresses in Yellow River Delta (YRD) of China. Remote sensing is widely viewed as a time- and cost-efficient way for detection of soil salinity. In this study, we collected 55 soil samples and corresponding field spectra from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. A partial least square regression (PLSR) model was created between field measured spectra (The spectra have been resampled to Advanced Land Imaging (ALI) spectral resolution) and soil salinity. Significant correlation was observed between predicted values and measured values with determination coefficient (R2) of 0.837. The PLSR mode was then applied to ALI reflectance image on a pixel-to-pixel basis. The result indicated that it is an efficient method that produces, fast, wide-coverage, and reliable distribution map of soil salinity using field-derived spectra and ALI multispectral imagery with PLSR method.
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