Suchergebnisse

Abstract. In regions with distinct seasons, soil salinity usually
varies greatly by season. Thus, the seasonal dynamics of soil salinization
must be monitored to prevent and control soil salinity hazards and to reduce
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 autumn, improved vegetation indices (IVIs) were created and then
applied to inversion modeling of the soil salinity content (SSC) by
employing 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 year were analyzed. The results indicated that the
SSC varied by season in the YRD, and the support vector machine method
offered the best SSC inversion models for the precision of the calibration
set (R2>0.72, RMSE < 6.34 g kg−1) and the validation
set (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 SSC
inversion 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.