Spatio-Temporal Variation in Soil Salinity and Its Influencing Factors in Desert Natural Protected Forest Areas

Abstract

Soil salinity is a crucial parameter affecting soil health. Excessive surface salt accumulation degrades soil structure, inhibits vegetation growth, and diminishes plant diversity. Such increases in salinity can accelerate desertification, leading to soil resource loss, hampering agricultural progress, and compromising ecological security. However, the vastness of arid regions and data acquisition challenges often hinder efficient SSC monitoring and modeling. In this study, we leveraged remote sensing data coupled with machine learning techniques to investigate the spatio-temporal dynamics of SSC in a representative desert natural forest area, the Alxa National Public Welfare Forest. Utilizing the geodetector model, we also delved into the factors influencing SSC. Our results underscored the effectiveness of the Convolutional Neural Networks (CNN) model in predicting SSC, achieving an accuracy of 0.745. Based on this model, we mapped the spatial distribution of SSC, revealing hydrothermal conditions as pivotal determinants of salt accumulation. From 2016 to 2021, soils impacted by salinity in the research area exhibited a rising trend, attributed to the prevailing dry climate and low precipitation. Such intensified salinity accumulation poses threats to the healthy growth of protective forest vegetation. This study can provide a theoretical reference for salinization management and ecological protection in desert natural forest areas.

Keywords

• soil salinization
• remote sensing inversion
• spatio-temporal dynamic
• influencing factors