Abstract
ABSTRACTTraditional monitoring methods often ignore the vegetation information, which has significantly indirect influence on the process of soil salinization. In this study, the vegetation indices-salinity indices (VI-SI) feature space was utilized to improve the inversion accuracy of soil salinity, while considering the bare soil and vegetation information. By fully considering the surface vegetation landscape in the Yellow River Delta, twelve VI-SI feature spaces were constructed, and two categories of soil salinization monitoring index were established. The experiment results showed that remote sensing monitoring index based on MSAVI-SI1 had the highest inversion accuracy (coefficient of determination (R2) = 0.912), while that based on the ENDVI-SI4 feature space had the lowest (R2 = 0.664). Therefore, the remote sensing monitoring index derived from MSAVI-SI can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.
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