干旱区研究2026，Vol.43Issue(3)：539-551,13.DOI:10.13866/j.azr.2026.03.08
融合机器学习与地理探测器的绿洲盐渍化垂直特征解析
Vertical gradient characteristics of soil salinization in arid oasis areas:A study based on machine learning and optimal parameter geographic detector
摘要
Abstract
Soil salinization is a critical barrier constraining the sustainable development of oasis agriculture in ar-id regions.This study aims to reveal the vertical variation characteristics of soil salinity in the Aksu oasis and its principal controlling mechanisms.The research integrated machine learning algorithms with optimal parameter geospatial detectors and constructed soil total salt models at four depths(0-10 cm,10-20 cm,20-30 cm,and 30-50 cm)using Sentinel-2 imagery.Results showed that the XGBoost model achieved the highest prediction accu-racy(R2≥0.6,RMSE≤5.97 g·kg-1),with overall robust performance but slightly higher uncertainty in severely sa-linized areas.Spatially,salt concentration decreased with increasing depth and exhibited significant low values near rivers due to leaching effects during wet periods.Attribution analysis demonstrated that the driving mecha-nisms exhibited vertical stratification:surface layer(0-10 cm)salinity was dominated by bivariate synergistic en-hancement of human activities,soil,and climate as local factors;while the deeper layer(30-50 cm)was deter-mined by the coupled mechanism of"groundwater-climatic evaporation."This study elucidated the vertical differ-entiation mechanisms of salinization processes and provided scientific evidence for three-dimensional monitoring and prevention of oasis salinization.关键词
土壤盐渍化/遥感/机器学习/最优参数地理探测器/土壤可溶性盐含量Key words
soil salinization/remote sensing/machine learning/optimal parameters-based geographical detec-tor/soil soluble salt content引用本文复制引用
熊海斌,郝兴明,邸彦峰,慈梦涛,梁其祥,张静静,范雪..融合机器学习与地理探测器的绿洲盐渍化垂直特征解析[J].干旱区研究,2026,43(3):539-551,13.基金项目
新疆天山英才计划(2023TSYCJU0005) （2023TSYCJU0005）
