Salinization information extraction model based on VI–SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image

Abstract

The interference of soil salt content, vegetation, and other factors greatly constrain soil salinization monitoring via remote sensing techniques. However, traditional monitoring methods often ignore the vegetation information. 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 three categories of soil salinization monitoring index were established; then, the inversion accuracies among all the indices were compared. The experiment results showed that remote sensing monitoring index based on MSAVI–SI1 with SDI2 had the highest inversion accuracy (R2 = 0.876), while that based on the ENDVI–SI4 feature space with SDI1 had the lowest (R2 = 0.719). The reason lied in the fact that MSAVI fully considers the bare soil line and thus effectively eliminates the background influence of soil and vegetation canopy. Therefore, the remote sensing monitoring index derived from MSAVI–SI1 can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.