Remote Sensing and Machine Learning for Water Quality Monitoring
Summary
Recent advances in sensor technology and computational intelligence have transformed water quality monitoring from isolated field sampling to continuous, large-scale observation. Remote sensing platforms – including multispectral and hyperspectral satellites, unmanned aerial vehicles (UAVs) and airborne systems – capture spatially and temporally resolved data on surface reflectance, temperature and turbidity. Machine learning algorithms then mine these high-dimensional datasets to estimate key water quality indicators such as chlorophyll-a concentration, total suspended matter, coloured dissolved organic matter and algal bloom presence. Supervised approaches using regression trees, support vector machines and deep neural networks have been applied to map pollutant distribution, track temporal trends and predict risk hotspots. Unsupervised methods aid in anomaly detection and the integration of multi-sensor time series. Together, remote sensing and machine learning enable near-real-time assessments across inland lakes, rivers, coastal zones and reservoirs, supporting water resource management, pollution control and ecosystem health assessments on a global scale.
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Remote Sensing and Machine Learning for Water Quality Monitoring publication trend
The graph below shows the total number of articles in remote sensing and machine learning for water quality monitoring across all publications each year (not limited to Nature Index journals).
Technical terms
Remote sensing: Acquisition of information about water bodies from a distance, typically using satellite or aerial sensors, to measure surface properties without direct contact.
Hyperspectral imaging: Collection of data across dozens to hundreds of contiguous spectral bands, enabling detailed characterisation of water constituents based on their unique absorption and scattering features.
Machine learning: Computational techniques that learn patterns from data to make predictions or classifications without explicit programming of the relationships.
Chlorophyll-a: A pigment in algae and plants used as a proxy for phytoplankton biomass and primary productivity in aquatic systems.
Total suspended matter (TSM): The concentration of particulate materials suspended in water, including sediments and organic detritus, which influence water clarity and quality.
References
- A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sensing (2022).
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