Satellites are capable of measuring an incredible variety of phenomena about the ocean from space: sea surface temperature (SST), sea surface salinity (SSS), chlorophyll, sea level, and winds, to name a few. Satellite remote sensing can be “passive”—sensing electromagnetic radiation at the top of the atmosphere and using algorithms to remove signals from the intervening atmosphere—or “active”—using radars of various descriptions to probe the sea surface and determine ocean properties from the reflected radar signal.
But there are limitations to satellite remote sensing. Cloud cover, time of day and how often the satellite passes over a particular area (revisit intervals may be one day or greater), and spatial resolution all impact the quantity and quality of data retrieved from Earth orbit. Saildrone is working with the National Aeronautics and Space Administration (NASA) and associated researchers to increase the value of remote sensing data by providing in situ data for satellite calibration and validation studies and algorithm development.
A pixel of earth science data from Earth orbit is just like a pixel in a photograph; there are high-resolution photographs and low-resolution photographs. Satellite infrared imaging has a very high spatial resolution—one pixel equals approximately one square kilometer—but it can’t see through the clouds. Microwave imaging can see through clouds, but the resolution is much lower, about 25 square kilometers per pixel; the result is something akin to looking at a map with your glasses off. Sub-pixel variability—variations within the average pixel—is the result of unresolved small-scale phenomena, the atmosphere intervening to disguise the ocean signals or simply the limitations of the remote sensing technique (antennas, sensitivity, signal processing, etc.).
“Saildrone provides a lot of information about sub-pixel variability and where it’s high and where it’s low because it measures at such a fine spatial resolution,” said Dr. Chelle Gentemann, a senior scientist at The Farallon Institute. “When you’re trying to understand how fronts affect weather and fluxes between the ocean and the atmosphere, resolving those fronts with the highest resolution is really important.”
Saildrone unmanned surface vehicles (USVs) collect data with a temporal frequency of one-second or better, resolving phenomena at the ocean surface that are part of the unresolved sub-pixel variability in satellite images.
The standard Saildrone sensor suite includes a conductivity, temperature, and depth (CTD) sensor mounted on the hull about .5 meters (1.6 feet) below the sea surface to measure “bulk” temperature, which is what most drifting buoys measure and what’s used to develop most satellite algorithms. Satellites measure the skin temperature—the top one millimeter (.039 inches) of the sea surface—and there are very few in situ observations of the skin temperature.
Satellite algorithms for temperature are developed in a variety of ways, but most use data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which compiles all collected surface marine data in one place. In situ observations are required for algorithm development; algorithms are naturally biased to where the calibrating data is sampled, therefore, it is important to collect the widest possible range of data to reduce regional bias and uncertainty.
There are many different platforms that provide data for algorithm development. Moored buoys provide temporal data, but not spatial. Research vessels provide temporal and spatial data, but are extremely expensive to operate, and cannot operate in all seasons in all areas of the ocean. Surface and subsurface gliders, floats, and drifters provide several key oceanographic and atmospheric measurements, but payloads are typically limited, and movement is largely restricted to that of ocean currents. Only Saildrone’s global fleet of USVs has proved to be capable of providing temporal and spatial data above and below the sea surface, in all areas of the ocean in all conditions.
“The Saildrone measurements provide significant value to certain types of scientific studies through their design as a solar-powered, movable, steerable platform that samples a wide variety of air-sea-interface and upper-ocean parameters, especially in regions where it is difficult to deploy and maintain other types of assets … The Saildrone vehicle’s advantage is for science applications needing rapid spatial sampling (it can travel at up to 4 m s-1), with additional atmospheric and oceanographic measurements needed to advance research into upwelling dynamics, sub-mesoscale variability, and air-sea fluxes in the vicinity of ocean fronts, diurnal warming modeling, carbon cycling, and biophysical interactions and coupled atmosphere-ocean modeling and data assimilation.” (Gentemann, Scott, et al. 2020)
The two animations below comparing Saildrone and satellite sea surface temperature (SST) and sea surface salinity (SSS) data, created by scientists at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC), provide critical information for the validation of satellite-derived data sets in highly dynamic areas of the ocean.
Animation showing Saildrone and satellite SST and SSS data collected during the 2017 SPURS-2 mission.
The 2017 Salinity Processes in the Upper Ocean-Regional Study (SPURS)-2 study was Saildrone’s first mission with NASA. The goal of NASA’s SPURS field campaigns is to understand key mechanisms responsible for near-surface salinity variations in the ocean.
Two saildrones performed data calibration measurements with the Scripps Institution of Oceanography ship Roger Revelle and Woods Hole Oceanographic Institution (WHOI) SPURS-2 buoy in the SPURS-2 field study area, a low-salinity, high-precipitation region of the Eastern Tropical Pacific. Observations of salinity, temperature, and other variables provided detailed characterization of oceanic structure over a continuum of spatial-temporal scales when combined with remote sensing data and showed the Saildrone platform to be useful for remote sensing validation. The animation above demonstrates good agreement between satellite and saildrone data; variations between SSS and SST are likely the result of a difference in spatial-temporal resolutions (the Saildrone data being the higher resolution of the two).
Animation comparing Saildrone and satellite SST and SSS data collected during the 2018 Baja California mission.
The 2018 Baja California mission included a three-way comparison of data collected by satellite, saildrone, and moored buoys and research vessels to calibrate satellite SST and SSS in a region of the Coastal Pacific known for complex air-land-sea dynamics.
The Suomi NPP satellite is equipped with a Visible Infrared Imaging Radiometer Suite (VIIRS) and provides operation data for use in global weather and climate models. During the 60-day cruise, two saildrones surveyed the highly variable California Current System along the US/Mexico coast to assess the utility of the Saildrone platform for satellite SST validation and model assimilation. The mission also looked at air-sea heat fluxes along dynamic frontal regions using Saildrone’s standard sensor suite plus an Acoustic Doppler Current Profiler (ADCP).
“The capability of Saildrone to measure ocean parameters such as SST and SSS in areas where they can change over very small temporal and spatial scales provides a unique validation tool. The animations clearly show differences do exist between the satellite products and Saildrone, a clear indication the sampling of Saildrone is resolving scales of the ocean not visible in the satellite products,” said Dr. Jorge Vazquez of NASA’s PO.DAAC.
Data from both the Baja Campaign and the SPURS-2 study is available at NASA’s PO.DAAC, hosted by the Jet Propulsion Laboratory (JPL) at the California Institute of Technology.
The 2019 NASA Multi-Sensor Improved Sea Surface Temperature Project (MISST) mission in the Bering and Chukchi Seas was the first of five years of temperature calibration and validation at high latitudes. This mission, part of a larger survey in partnership with the National Oceanographic and Atmospheric Administration (NOAA), marked Saildrone’s fifth consecutive year of successful surveys in the US Arctic. In addition to the standard sensor suite, two of six saildrones were equipped with an IR pyrometer mounted on the bow, known colloquially as “the unicorn” because of its horn-like shape (pictured above). This unique sensor measures both the skin temperature and reflected sky radiation—knowing if measurements are taken under a clear vs a cloudy sky is important for skin temperature algorithms.
“[The “unicorn”] is not proven technology at this point, but if it’s determined to work, it will be revolutionary. Currently, we only have these observations from a couple of research vessels. If we can deploy this technology globally on a saildrone, it would completely change how we approach collecting in situ data for sea surface temperature algorithm development,” said Gentemann.
Ocean processes drive large-scale planetary systems directly related to weather and climate, food supply, shipping, and socio-economic issues. Past missions in the Tropical Pacific and Arctic have included one to four saildrones collecting high-resolution observations; it is expected that upcoming NASA missions will make use of saildrone arrays for dynamic in situ observation in key regions around the planet.
“Coastal upwelling and western boundary currents are two critical areas of the world’s oceans associated with small scale mesoscale (<100km) and submesoscale (<10km) features. Saildrone will provide a powerful tool for understanding how best to apply satellite-derived products in these critical areas of the world’s oceans,” said Vazquez.
Chelle L. Gentemann, Joel P. Scott, et al., “Saildrone: adaptively sampling the marine environment,” Bulletin of the American Meteorological Society, January 24, 2020 (published online)
Jorge Vazquez-Cuervo, Jose Gomez-Valdes, et al., “Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast,” Remote Sensing, 2019.
Dongxiao Zhang, Meghan F. Cronin, et al., “Comparing Air-Sea Flux Measurements from a New Unmanned Surface Vehicle and Proven Platforms During the Spurs-2 Field Campaign,” Oceanography, Vol. 32, No. 2.