Other meanings of Remote sensing
Oceanography
Remote sensing in oceanography uses satellite and airborne sensors to observe the ocean surface and subsurface properties over large spatial and temporal scales. Key measurements include sea surface temperature, ocean color (for chlorophyll concentration), sea surface height (via altimetry), and surface wind vectors (via scatterometry). These data are essential for climate monitoring, fisheries management, weather forecasting, and understanding ocean circulation. The field began with the 1978 Seasat mission and has since expanded into a constellation of operational satellites, such as the Jason series and Copernicus Sentinel-3.
Satellite altimetry measures sea surface height with centimeter accuracy by timing radar pulses reflected from the ocean surface. This data reveals ocean currents, eddies, and global sea-level rise. Sea surface temperature (SST) is derived from thermal infrared and microwave radiometers, providing critical inputs for climate models and El Niño forecasts. Ocean color sensors detect phytoplankton chlorophyll concentrations by measuring reflected sunlight in visible bands, enabling estimates of primary productivity and harmful algal blooms.1 Scatterometers use radar backscatter to infer surface wind speed and direction, improving storm tracking and marine weather predictions.2
Oceanographic remote sensing supports operational services such as fisheries management, where ocean-color maps guide fishing fleets to productive waters.3 Altimetry data are assimilated into ocean circulation models for hurricane intensity forecasting and ship routing. Climate research relies on long-term SST and sea-level records to monitor global warming and polar ice melt. In coastal zones, high-resolution imagery helps map bathymetry, monitor oil spills, and track sediment transport.1 The data are also used in maritime security, such as detecting illegal fishing through vessel identification from synthetic aperture radar.
GNSS reflectometry (GNSS-R) uses reflected GPS signals from the ocean surface to infer sea state, wind speed, and soil moisture, a technique pioneered by the 2014 CyGNSS mission.4 Early ocean remote sensing dates to the 1960s with TIROS satellites, whose infrared images first revealed ocean eddies. The 1978 Seasat carried the first spaceborne synthetic aperture radar, altimeter, and scatterometer, but failed after 106 days. Underwater features such as internal waves and submarine groundwater discharge can be detected via sea-surface roughness patterns in SAR imagery. Archaeological remote sensing in the ocean has identified submerged ancient settlements using multispectral and lidar data from aircraft.
Atmospheric correction remains a major challenge for ocean-color sensors, as aerosols and clouds obscure the signal, requiring complex algorithms.1 Calibration and validation depend on in situ measurements from buoys, ships, and autonomous floats, which are sparse in remote regions. Future missions, such as NASA's PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) and ESA's Copernicus Sentinel-6, will provide higher spectral resolution and improved altimetry continuity. Machine learning is increasingly used to fuse multi-sensor data and fill gaps, while small satellite constellations (e.g., Planet's Dove) offer daily revisit times for coastal monitoring.5
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