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Research Progress and Trends in Remote-Sensing Retrieval of Water-Quality Parameters: A Knowledge Graph Analysis
Hongbo Li1, Xiuxiu Chen1,2, Shixuan Liu3,4
1School of Spatial Planning and Design, Hangzhou City University, Hangzhou 310015, China.
Sensors (Basel, Switzerland)
|May 4, 2026
Summary
Remote-sensing inversion of water quality uses advanced technology for environmental monitoring. Research shows rapid growth, with China leading publications, focusing on data fusion and AI for better water resource management.
Area of Science:
- Environmental Science
- Remote Sensing Technology
- Water Resources Management
Background:
- Remote-sensing inversion is crucial for water quality monitoring and ecological governance.
- A bibliometric review of 2812 studies (1980-2026) was conducted to map the field's knowledge landscape.
- Global research has evolved through three stages: exploration, slow growth, and rapid expansion.
Purpose of the Study:
- To provide a comprehensive systematic review of remote-sensing inversion of water-quality parameters.
- To analyze the development context, research characteristics, and future trends in this interdisciplinary field.
- To offer valuable references for academic exploration and practical applications in water resources management.
Main Methods:
- Bibliometric-based narrative review.
- Knowledge mapping analysis using CiteSpace software.
- Selection of 2812 English studies from the Web of Science Core Collection (WOSCC).
Main Results:
- Global research experienced initial exploration (1980-2000), slow growth (2001-2015), and rapid explosion (2016-2026).
- China leads in publication volume, with key collaborators including the Chinese Academy of Sciences, Wuhan University, and NASA.
- Core research hotspots include multi-source data fusion, AI-driven inversion model optimization, and a shift towards inland water bodies.
Conclusions:
- Current challenges involve data fusion adaptability, integrating geospatial factors, and systematic inland water research.
- Future research should advance remote-sensing data-fusion methods and optimize water-quality inversion models.
- Strengthening inland water body studies is essential for effective water resources management and ecological governance.
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