Satellite-Based Observations of Sea Surface Salinity

Satellite-based observation of sea surface salinity (SSS) employs passive microwave radiometry at L-band frequencies to measure the natural thermal emission of the ocean surface, which varies with both salinity and temperature. Since 2009, missions such as ESA’s Soil Moisture and Ocean Salinity (SMOS), NASA’s Aquarius and Soil Moisture Active Passive (SMAP) have delivered global SSS fields with spatial resolutions of approximately 25–100 km and revisit times of days to weeks. Retrieval algorithms correct for sea-surface roughness, atmospheric absorption, radio-frequency interference and sensor drift, and are validated against in situ networks of Argo floats, moored buoys and research vessels. These continuous salinity maps reveal freshwater fluxes from precipitation, evaporation and river outflow, outline ocean circulation pathways, and underpin studies of the global water cycle, climate variability and marine biogeochemistry. Ongoing enhancements in sensor calibration, algorithmic refinement and assimilation into numerical models are steadily improving the accuracy and utility of satellite SSS for climate monitoring and operational oceanography.

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Satellite-Based Observations of Sea Surface Salinity publication trend

The graph below shows the total number of articles in satellite-based observations of sea surface salinity across all publications each year (not limited to Nature Index journals).

Technical terms

Sea Surface Salinity (SSS): Salt concentration in the uppermost layer of the ocean, expressed in practical salinity units (psu).
Passive Microwave Radiometry: Technique measuring natural thermal emissions from the sea surface at L-band (~1.4 GHz) to infer SSS.
Retrieval Algorithm: Computational method converting raw radiometric brightness temperatures into salinity estimates by correcting for environmental and instrumental effects.
Root Mean Square Error (RMSE): Statistical measure of the average magnitude of differences between satellite-derived salinity and reference in situ observations.

References

1. Simulated Sea Surface Salinity Data from a 1/48° Ocean Model. Scientific Data (2024).
2. Remote sensing of sea surface salinity: challenges and research directions. GIScience & Remote Sensing (2023).
3. Salinity Inversion of Flat Sea Surface Based on Deep Neural Network. Space Science & Technology (2024).
4. Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010–2019). Remote Sensing of Environment (2020).
5. The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases. Remote Sensing (2018).
6. Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote Sensing (2019).