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Related Concept Videos

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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Multi-algorithm satellite remote sensing for water quality in China's two largest rivers: Framework and policy

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
Summary
This summary is machine-generated.

Satellite remote sensing offers a cost-effective solution for river water quality monitoring. This framework accurately retrieves parameters, revealing significant pollution differences between the Changjiang and Yellow Rivers.

Keywords:
Changjiang RiverMachine learningRemote sensingSpatiotemporal analysisWater quality monitoringYellow River

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Water Resource Management

Background:

  • Traditional water quality monitoring methods for large rivers are limited by spatiotemporal coverage and high costs.
  • Satellite remote sensing presents a viable alternative for comprehensive water quality assessment.

Purpose of the Study:

  • To develop and validate a satellite remote sensing framework for retrieving water quality parameters in the Changjiang and Yellow Rivers.
  • To assess the effectiveness of the framework in identifying pollution sources and evaluating water protection policies.

Main Methods:

  • Utilized harmonized Sentinel-2 imagery and in-situ water quality data (2019-2024).
  • Constructed a multi-dimensional feature space including spectral, derivative, and spatiotemporal variables.
  • Evaluated 11 machine learning algorithms, focusing on LightGBM and Random Forest for parameter retrieval.

Main Results:

  • Achieved an average adjusted R² of 0.76 across nine water quality parameters, with high accuracy for electrical conductivity (0.95) and moderate for ammonia nitrogen (0.58).
  • Classified water quality parameters based on retrieval mechanisms: spectral-dominated, spatiotemporal-dominated, and spatial-dominated spectral-regulated.
  • Identified significant water quality disparities between rivers, with the Changjiang showing Class IV-V water and the Yellow River exhibiting more severe pollution.

Conclusions:

  • The developed framework provides a robust foundation for operational satellite-based water quality monitoring in large river systems.
  • Demonstrated the framework's capability to identify pollution sources and assess the impact of water protection laws.
  • Highlighted the need for evidence-based watershed management informed by satellite remote sensing data.