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.

Keywords:

• Soil salinization
• feature space
• Landsat8 OLI
• monitoring model
• Yellow River Delta

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Natural Science Foundation of Shandong Province (Grant Nos. ZR2018BD001); Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J18KA181); Key Research Program of Frontier Science of Chinese Academy of Sciences (Grant No. QYZDY-SSW-DQC007); Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University (Grant No. KLGIS2017A02); Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (Grant No. 17I04); Open fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (Grant No. 2016NGCM02); Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University) (No. 2017(B)003), and The National Key R&D Program of China (Grant No. 2017YFA0604804).