整合谷歌地球引擎,Sentinel-2图像和机器学习,以进行河流系统中总溶解固体的时间映射
Eric Ariel L Salas1, Sakthi S Kumaran2, Robert Bennett2
1Agricultural Research Development Program (ARDP), Central State University, Wilberforce, OH, 45384, USA. esalas@centralstate.edu.
Scientific reports
|July 29, 2025
概括
在内陆水域绘制总溶解固体 (TDS) 是一个挑战. 这项研究使用谷歌地球引擎,Sentinel-2图像和机器学习来创建详细的TDS地图,显示射频模型对水质监测有效.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 水质监测 水质监测
背景情况:
- 总溶解固体 (TDS) 是内陆系统中的关键水质指标.
- 对于TDS的现场数据收集是昂贵和劳动密集型的,限制了空间和时间分辨率.
- 先进的遥感和机器学习为高效的TDS映射提供了潜力.
研究的目的:
- 开发和验证使用Google Earth Engine (GEE) 和Sentinel-2图像绘制TDS的空间显式方法.
- 为了比较随机森林 (RF) 和支持矢量机器 (SVM) 对于TDS分类的有效性.
- 分析时间TDS变化及其与小迈阿密河 (LMR) 土地覆盖面和降雨量的相关性.
主要方法:
- 整合了GEE,Sentinel-2卫星图像和机器学习算法 (RF和SVM).
- 从Sentinel-2数据中提取光谱指数,用于列车分类模型.
- 在三年 (2020-2023) 的8月至11月期间,对LMR的TDS水平进行分析.
主要成果:
- 射频模型的性能优于SVM,达到高精度 (总精度高达0.88,卡帕系数为0.85).
- 发现了TDS水平的升高,特别是在中游的LMR部分.
- TDS波动与时间降雨模式相关,与自然覆盖面 (森林,湿地) 相反.
结论:
- 基于GEE的方法与Sentinel-2和RF提供了一个有效的工具,用于绘制TDS和监测水质.
- 自然土地覆盖增强了对TDS波动的弹性,突出了保护对水质管理的重要性.
- 结果为管理LMR中环保问题提供了有价值的空间见解.
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