Abstract: Understanding the spatial variability and agglomeration of soil salinity is of great significance for the sustainable development of estuarine wetland. Landsat 8 OLI remote sensing image, digital elevation mode and soil surface samples of Minjiang estuary wetland of Fuzhou were used as the data sources. The correlation analysis and principal component analysis were combined to select significant environmental variables and to reduce their dimensions. We analyzed the spatial variability of soil salinity with support vector regression ordinary kriging model (SVROK) and regression kri-ging model (RK), and quantified spatial agglomeration of soil salinity by the spatial autocorrelation analysis. The results showed that three principal components (PCs) extracted by the principal component analysis could explain at least 85% of the total variance in the original dataset and reflected the comprehensive information of vegetation cover, soil properties and topography. Both soil salinity and its residuals were affected by structural factors and random factors. The SVROK model based on principal component (PCs) as input variables can more accurately reflect the spatial variability of soil salinity, with a trend of “higher in the north and lower in the south”. The Moran’s I of soil salinity was more than 0.5, with significant positive spatial autocorrelation and a higher spatial aggregation degree, displaying the spatial agglomeration characteristics of “high value agglomeration, high value widespread, high value surrounded by low value”.