基于Sentinel-2和可解释的机器学习的Hetao平原农田土壤盐度和pH值的反转
Hua-Yu Huang1, Qi-Dong Ding1, Jun-Hua Zhang1
1College of Ecology and Environmental Science, Ningxia University, Yinchuan 750021, China.
Ying yong sheng tai xue bao = The journal of applied ecology
|August 31, 2025
概括
通过机器学习,遥感有效地监测土壤的盐化和化. 盐度指数是指导受影响地区农业管理的关键预测指标.
科学领域:
- 农业科学
- 环境科学
- 遥感技术
背景情况:
- 土壤盐化和化威胁着农业的可持续性和环境健康.
- 准确评估土壤盐度 (SSC) 和 pH 是监测这些问题的关键.
- 遥感为大规模监测土壤状况提供了可行的解决方案.
研究的目的:
- 使用遥感数据和机器学习开发和验证SSC和pH的反向模型.
- 确定影响土壤盐化和化的关键变量.
- 在Hetao平原绘制土壤盐度和度的空间分布.
主要方法:
- 综合的Sentinel-2图像,环境变量,土壤物理化学属性和SAR数据.
- 使用梯度提升机 (GBM) 进行特征选.
- 应用了六种机器学习算法 (XGBoost,LightGBM,AdaBoost,CatBoost,RF,ERT) 进行SSC和pH逆转.
- 使用Shapley添加式解释 (SHAP) 进行可变贡献分析.
主要成果:
- 总体而言,土壤盐化和化程度为轻度至中度,空间异质性显著.
- 通过过特征,GBM有效地降低了模型的复杂性.
- 在SSC (R2=0.925) 和pH (R2=0.818) 逆转方面,XGBoost和ERT模型显示出最佳的性能.
- 盐度指数是最重要的变量 (SSC为34.9%,pH为34.2%).
结论:
- 遥感与机器学习相结合,为监测土壤盐化和化提供了有效的方法.
- 盐度指数是评估土壤盐度和 pH 的关键指标.
- 这些发现为类似地区的农业管理和决策提供了科学基础.
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