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. In regions with distinct seasons, soil salinity usuallyvaries greatly by season. Thus, the seasonal dynamics of soil salinizationmust be monitored to prevent and control soil salinity hazards and to reduceecological risk. This article took the Kenli District in the Yellow Riverdelta (YRD) of China as the experimental area. Based on Landsat data fromspring and autumn, improved vegetation indices (IVIs) were created and thenapplied to inversion modeling of the soil salinity content (SSC) byemploying stepwise multiple linear regression, back propagation neuralnetwork and support vector machine methods. Finally, the optimal SSC modelin each season was extracted, and the spatial distributions and seasonaldynamics of SSC within a year were analyzed. The results indicated that theSSC varied by season in the YRD, and the support vector machine methodoffered the best SSC inversion models for the precision of the calibrationset ( R2>0.72 , RMSE   6.34 g kg −1 ) and the validationset ( R2>0.71 , RMSE   6.00 g kg −1 and RPD  >  1.66). The best SSC inversion model for spring could be applied to the SSCinversion in winter ( R2 of 0.66), and the best model for autumn could be applied to the SSC inversion in summer ( R2 of 0.65). The SSC exhibited a gradual increasing trend from the southwest to northeast in the Kenli District. The SSC also underwent the following seasonal dynamics: soil salinity accumulated in spring, decreased in summer, increased in autumn and reached its peak at the end of winter. This work provides data support for the control of soil salinity hazards and utilization of saline–alkali soil in the YRD.

Monitoring the seasonal dynamics of soil salinization in the Yellow River delta of China using Landsat data