Soil salinization poses a significant challenge for achieving sustainable utilization of land resources, especially in coastal, arid, and semi-arid areas. Timely monitoring of soil salt content and its spatial distribution is conducive to secure efficient agricultural development in these regions. In this study, to address the persistent problem of soil salinization in the Yellow River Delta in China, the feature space method was used to construct multiple feature spaces of surface albedo (Albedo)–modified soil-adjusted vegetation index (MSAVI), salinity index (SI)–Albedo, and SI–normalized difference vegetation index (NDVI), and an optimal inversion model of soil salinity was developed. Based on Landsat 8 Operational Land Imager (OLI) image data and simultaneous field-measured sampling data, an optimal model from 2015 to 2019 was used to obtain the soil salt content in the region at a 30 m resolution. The results show that the proportion of soil salinization in 2015 and 2019 was approximately 76% and 70%, respectively, and overall soil salinization showed a downward trend. The salinization-mitigated areas are primarily distributed in the southwest of the Yellow River Delta, and the aggravated areas are distributed in the northeast and southeast. In general, the spatial variation characteristics show an increasing trend from the southwest to the eastern coastal areas, corresponding to the formation mechanism of salt accumulation in the region. Further, corresponding sustainable development countermeasures and suggestions were proposed for different salinity levels. Meanwhile, this study revealed that the SI–Albedo feature space model is the most suitable for inversion of salinization in coastal areas.
An attempt to monitor seasonal dynamics of soil salinization in the Yellow River Delta region of China using Landsat data