A point-by-point response to the comments by the editor

In regions with distinct seasons, soil salinity usually varies greatly between seasons. Thus, the seasonal dynamics of soil salinization must be monitored for the prevention and control of soil salt hazard and the reduction of ecological risk. This article took the Kenli district in the Yellow River Delta (YRD) of China as the experimental area. Based on Landsat data from spring and 15 autumn, improved vegetation indices (IVIs) were created and then applied to inversion modeling of the soil salinity content (SSC) by employing the stepwise multiple linear regression, back propagation neural network and support vector machine methods. Finally, the optimal SSC model in each season was extracted, and the spatial distributions and seasonal dynamics of SSC within a 20 year were analyzed. The results indicated that the SSC varied obviously between seasons in the YRD, and the support vector machine method resulted in the best inversion models for the precision of the calibration set (R 2 >0.72, RMSE <6.34 g/kg) and the validation set (R 2 >0.71, RMSE<6.00 g/kg, and RPD>1.66). The best SSC inversion model for spring could be applied to the SSC inversion in 25 winter (R 2 of 0.66); similarly, the best model for autumn could also be applied to SSC inversion in summer (R 2 of 0.65). The SSC exhibited a gradually increasing trend from 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 in the end of winter. This 30 work provides data support for the control of soil salt hazards and utilization of saline-alkali soil in the YRD.