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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...
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Water exists in any one of the three classical states: solid (ice), liquid (water), and gas (steam or water vapor). The state of water depends on i) the intermolecular forces that draw molecules together and ii) the kinetic energy that leads to movements that pull them apart.
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RS-WaterQuality Mapper: an open-source water quality remote sensing toolbox in QGIS.

Haibin Su1, Hongxing Liu2, Lei Wang3

  • 1Department of Physics & Geosciences, Texas A&M University, Kingsville, Kingsville, TX 78363 USA.

Earth Science Informatics
|March 20, 2026
PubMed
Summary

A new open-source QGIS plugin, the RS-WaterQuality Mapper, simplifies satellite data analysis for water quality monitoring. This tool enhances accuracy in complex waters, aiding researchers and managers in effective ecosystem management.

Keywords:
ACOLITEEnsemble modelMachine learningQGISRemote sensingWater quality

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Geographic Information Systems (GIS)

Background:

  • Effective water management necessitates frequent monitoring, but traditional methods lack spatiotemporal scope.
  • Satellite remote sensing offers extensive data, yet accessible software for non-specialists is limited.
  • A gap exists in integrated, user-friendly tools for aquatic remote sensing data analysis.

Purpose of the Study:

  • To develop an open-source QGIS plugin, the RS-WaterQuality Mapper, for accessible aquatic remote sensing.
  • To provide a scientifically robust workflow integrating atmospheric correction and machine learning for water quality assessment.
  • To enhance the processing efficiency and predictive accuracy of satellite-derived water quality data, especially in optically complex inland waters.

Main Methods:

  • Development of the RS-WaterQuality Mapper, an open-source Python plugin for QGIS.
  • Implementation of an aqua-focused atmospheric correction workflow.
  • Application of a novel multi-predictor ensemble model using spectral-space partitioning.
  • Utilizing optimized Python libraries and a multi-processing architecture for computational efficiency.

Main Results:

  • The RS-WaterQuality Mapper provides a complete workflow for aquatic remote sensing within QGIS.
  • The multi-predictor ensemble model demonstrated enhanced predictive accuracy in optically complex inland waters.
  • Validation across diverse case studies (reservoir, saline lake, river system) showed strong performance (R² > 0.80).
  • The tool ensures computational efficiency for processing large satellite scenes.

Conclusions:

  • The RS-WaterQuality Mapper empowers researchers and water resource managers by integrating advanced science into a familiar GIS environment.
  • This open-source tool facilitates the use of satellite data for more effective aquatic ecosystem management.
  • The plugin addresses the need for accessible, robust software for non-specialist users in water quality monitoring.