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相关概念视频

Quality of Water01:19

Quality of Water

504
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
504

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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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多算法卫星遥感中国两大河流水质:框架和政策实施效应

Yanfei Zhang1, Tiejun Liu2

  • 1Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China; Institute of Water Resources for Pastoral Area, Ministry of Water Resources of the People's Republic of China, Hohhot, 010020, China.

The Science of the total environment
|September 13, 2025
PubMed
概括

卫星遥感为河流水质监测提供了具有成本效益的解决方案. 这个框架准确地检索了参数,揭示了长江和黄河之间的污染差异.

关键词:
长江河 - 长江河机器学习 机器学习遥感是一种远程传感.时间空间分析.水质监测 监测水质 监测水质黄河 - 黄河 - 黄河 - 黄河

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科学领域:

  • 环境科学 环境科学
  • 遥感 遥感 遥感 遥感
  • 水资源管理 水资源管理

背景情况:

  • 对于大型河流,传统的水质监测方法受到时间空间覆盖和高成本的限制.
  • 卫星遥感为全面的水质评估提供了一个可行的替代方案.

研究的目的:

  • 开发和验证卫星遥感框架,用于检索长江和黄河的水质参数.
  • 评估框架在确定污染源和评估水保护政策方面的有效性.

主要方法:

  • 利用了协调的Sentinel-2图像和现场水质数据 (2019-2024年).
  • 构建了一个多维特征空间,包括光谱,衍生和时空变量.
  • 评估了11个机器学习算法,专注于LightGBM和随机森林进行参数检索.

主要成果:

  • 在九个水质参数中获得了0.76的平均调整R2,电导率的精度高 (0.95),氨的精度中等 (0.58).
  • 基于检索机制的水质参数分类:光谱主导,时空主导和空间主导的光谱调节.
  • 确定了河流之间的水质差异很大,江的水分分别为IV-V级,黄河的污染更严重.

结论:

  • 开发的框架为大型河流系统的卫星水质监测提供了坚实的基础.
  • 证明了该框架能够识别污染源并评估水保护法律的影响.
  • 强调需要基于证据的流域管理,以卫星遥感数据为基础.