使用遥感产品和机器学习预测帕罗佩巴河流域的水质参数
Rafael Luís Silva Dias1, Ricardo Santos Silva Amorim1, Demetrius David da Silva1
1Department of Agricultural Engineering, Universidade Federal de Viçosa, Viçosa 36570-900, MG, Brazil.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种新的遥感和机器学习方法,用于预测地表水质. 规范化的PlanetScope数据与决策树算法为环境监测提供了卓越的准确性.
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:
- 传统的水质监测受到成本,频率和空间覆盖范围的限制.
- 极端事件需要加强时间和空间的水质评估.
- 卫星图像提供了一个补充的解决方案,但面临着分辨率挑战.
研究的目的:
- 开发和评估一种利用遥感数据和机器学习预测水质参数的方法.
- 为了比较不同的卫星数据源 (Sentinel-2,原始PlanetScope,正常化的PlanetScope) 和机器学习算法.
- 评估拟议的方法论是否适合于洛特式和晶体式环境.
主要方法:
- 使用了Sentinel-2和PlanetScope (PS) 图像,包括正常化的PS数据.
- 从24个监测站 (2016-2023) 收集的水质数据.
- 应用随机森林,k-最近邻居,支持向量机器和立体算法用于预测.
主要成果:
- 使用规范化PS数据的模型在估计水质参数方面表现最好.
- 基于决策树的算法 (Random Forest和Cubist) 显示出优越的概括能力.
- 使用RMSE,MAE,CCC和R平方指标评估模型性能.
结论:
- 拟议的方法对于使用遥感和机器学习预测水质参数是有效的.
- 规范化的PlanetScope数据和决策树算法强烈推用于水质监测.
- 该方法为各种环境监测应用提供了宝贵的见解.
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