Remote Sensing of Aquatic Ecosystems

Remote sensing of aquatic ecosystems employs satellite, airborne and vessel-based sensors to monitor water bodies at spatial and temporal scales unattainable by in situ surveys alone. Passive optical imagery in visible and near-infrared bands is widely used to assess water quality variables such as turbidity, chlorophyll concentration and harmful algal blooms. Hyperspectral sensors extend this capability by capturing fine spectral signatures that discriminate submerged aquatic vegetation (SAV), benthic substrates and phytoplankton functional groups. Active systems, including lidar and synthetic aperture radar, complement optical methods by penetrating surface layers and providing structural information on submerged habitats. Advanced processing approaches—water column correction, spectral unmixing and machine-learning classification—enable quantitative mapping of vegetation cover, sediment transport and biogeochemical parameters across diverse environments from tropical seagrass meadows to temperate lakes. These observations inform ecosystem management, guide restoration of degraded habitats and support global assessments of carbon cycling, biodiversity and climate-driven change in freshwater and coastal systems.

Research from Nature Portfolio

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Remote Sensing of Aquatic Ecosystems publication trend

The graph below shows the total number of articles in remote sensing of aquatic ecosystems across all publications each year (not limited to Nature Index journals).

Technical terms

Hyperspectral imaging: Acquisition of continuous spectral data across many narrow wavelength bands, enabling detailed discrimination of aquatic substrates and vegetation.

Water column correction: Computational methods to remove the influence of water and its constituents on remotely sensed signals, isolating target reflectance.

Spectral end-member: A representative reflectance spectrum of a pure material or community used in spectral unmixing models.

Submerged aquatic vegetation (SAV): Plant communities growing beneath the water surface, whose distribution and health can be mapped by remote sensing.

Geostationary Ocean Colour Imager (GOCI): A geostationary satellite sensor providing high-frequency optical observations of aquatic environments, particularly for monitoring temporal dynamics.

Cyanobacterial bloom: A dense aggregation of cyanobacteria at or near the water surface, detectable via characteristic reflectance signatures.

References

1. Hyperspectral library of submerged aquatic vegetation and benthic substrates in the Baltic Sea. Earth System Science Data (2025).
2. Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnology and Oceanography (2018).
3. A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists. Remote Sensing (2021).