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NASA’s PACE, US-European SWOT Satellites Offer Combined Look at Ocean


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Preparations for Next Moonwalk Simulations Underway (and Underwater)

This animation shows data taken by NASA’s PACE and the international SWOT satellites over a region of the North Atlantic Ocean. PACE captured phytoplankton data on Aug. 8, 2024; layered on top is SWOT sea level data taken on Aug. 7 and 8, 2024. NASA’s Scientific Visualization Studio

One Earth satellite can see plankton that photosynthesize. The other measures water surface height. Together, their data reveals how sea life and the ocean are intertwined.

The ocean is an engine that drives Earth’s weather patterns and climate and sustains a substantial portion of life on the planet. A new animation based on data from two recently launched missions — NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the international Surface Water and Ocean Topography (SWOT) satellites — gives a peek into the heart of that engine.

Physical processes, including localized swirling water masses called eddies and the vertical movement of water, can drive nutrient availability in the ocean. In turn, those nutrients determine the location and concentration of tiny floating organisms known as phytoplankton that photosynthesize, converting sunlight into food. These organisms have not only contributed roughly half of Earth’s oxygen since the planet formed, but also support economically important fisheries and help draw carbon out of the atmosphere, locking it away in the deep sea.

“We see great opportunity to dramatically accelerate our scientific understanding of our oceans and the significant role they play in our Earth system,” said Karen St. Germain, director of the Earth Science Division at NASA Headquarters in Washington. “This visualization illustrates the potential we have when we begin to integrate measurements from our separate SWOT and PACE ocean missions. Each of those missions is significant on its own. But bringing their data together — the physics from SWOT and the biology from PACE — gives us an even better view of what’s happening in our oceans, how they are changing, and why.”

A collaboration between NASA and the French space agency CNES (Centre National d’Études Spatiales), the SWOT’ satellite launched in December 2022 to measure the height of nearly all water on Earth’s surface. It is providing one of the most detailed, comprehensive views yet of the planet’s ocean and its freshwater lakes, reservoirs, and rivers.

Launched in February 2024, NASA’s PACE satellite detects and measures the distribution of phytoplankton communities in the ocean. It also provides data on the size, amount, and type of tiny particles called aerosols in Earth’s atmosphere, as well as the height, thickness, and opacity of clouds.

“Integrating information across NASA’s Earth System Observatory and its pathfinder missions SWOT and PACE is an exciting new frontier in Earth science,” said Nadya Vinogradova Shiffer, program scientist for SWOT and the Integrated Earth System Observatory at NASA Headquarters.

Where Physics and Biology Meet

The animation above starts by depicting the orbits of SWOT (orange) and PACE (light blue), then zooms into the North Atlantic Ocean. The first data to appear was acquired by PACE on Aug. 8. It reveals concentrations of chlorophyll-a, a vital pigment for photosynthesis in plants and phytoplankton. Light green and yellow indicate higher concentrations of chlorophyll-a, while blue signals lower concentrations.

Next is sea surface height data from SWOT, taken during several passes over the same region between Aug. 7 and 8. Dark blue represents heights that are lower than the mean sea surface height, while dark orange and red represent heights higher than the mean. The contour lines that remain once the color fades from the SWOT data indicate areas of the ocean with the same height, much like the lines on a topographic map indicate areas with the same elevation.

The underlying PACE data then cycles through several groups of phytoplankton, starting with picoeukaryotes. Lighter green indicates greater concentrations of this group. The final two groups are cyanobacteria — some of the smallest and most abundant phytoplankton in the ocean — called Prochlorococcus and Synechococcus. For Prochlorococcus, lighter raspberry colors represent higher concentrations. Lighter teal colors for Synechococcus signal greater amounts of the cyanobacteria.

The animation shows that higher phytoplankton concentrations on Aug. 8 tended to coincide with areas of lower water height. Eddies that spin counterclockwise in the Northern Hemisphere tend to draw water away from their center. This results in relatively lower sea surface heights in the center that draw up cooler, nutrient-rich water from the deep ocean. These nutrients act like fertilizer, which can boost phytoplankton growth in sunlit waters at the surface.

Overlapping SWOT and PACE data enables a better understanding of the connections between ocean dynamics and aquatic ecosystems, which can help improve the management of resources such as fisheries, since phytoplankton form the base of most food chains in the sea. Integrating these kinds of datasets also helps to improve calculations of how much carbon is exchanged between the atmosphere and the ocean. This, in turn, can indicate whether regions of the ocean that absorb excess atmospheric carbon are changing.

More About SWOT

The SWOT satellite was jointly developed by NASA and CNES, with contributions from the Canadian Space Agency (CSA) and the UK Space Agency. NASA’s Jet Propulsion Laboratory, managed for the agency by Caltech in Pasadena, California, leads the U.S. component of the project. For the flight system payload, NASA provided the Ka-band radar interferometer (KaRIn) instrument, a GPS science receiver, a laser retroreflector, a two-beam microwave radiometer, and NASA instrument operations.  The Doppler Orbitography and Radioposition Integrated by Satellite system, the dual frequency Poseidon altimeter (developed by Thales Alenia Space), the KaRIn radio-frequency subsystem (together with Thales Alenia Space and with support from the UK Space Agency), the satellite platform, and ground operations were provided by CNES. The KaRIn high-power transmitter assembly was provided by CSA.

To learn more about SWOT, visit:

https://swot.jpl.nasa.gov

More About PACE

The PACE mission is managed by NASA Goddard Space Flight Center, which also built and tested the spacecraft and the Ocean Color Instrument, which collected the data shown in the visualization. The satellite’s Hyper-Angular Rainbow Polarimeter #2  was designed and built by the University of Maryland, Baltimore County, and the Spectro-polarimeter for Planetary Exploration  was developed and built by a Dutch consortium led by Netherlands Institute for Space Research, Airbus Defence, and Space Netherlands.

To learn more about PACE, visit:

https://pace.gsfc.nasa.gov

News Media Contacts

Jacob Richmond (for PACE)
NASA’s Goddard Space Flight Center, Greenbelt, Md.
jacob.a.richmond@nasa.gov

Jane J. Lee / Andrew Wang (for SWOT)
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov

2024-169

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      Kelp Us!: A Methods Analysis for Predicting Kelp Pigment Concentrations from Hyperspectral Reflectance
      Jasmine Sirvent
      Ocean color remote sensing enables researchers to assess the quantity and physiology of life in the ocean, which is imperative to understanding ecosystem health and formulating accurate predictions. However, without proper methods to analyze hyperspectral data, correlations between spectral reflectance and physiological traits cannot be accurately derived. In this study, I explored different methods—single variable regression, partial least squares regressions (PLSR), and derivatives—in analyzing in situ Macrocystis pyrifera (giant kelp) off the coast of Santa Barbara, California in order to predict pigment concentrations from AVIRIS hyperspectral reflectance. With derivatives as a spectral diagnostic tool, there is evidence suggesting high versus low pigment concentrations could be diagnosed; however, the fluctuations were within 10 nm of resolution, thus AVIRIS would be unable to reliably detect them. Exploring a different method, I plotted in situ pigment measurements — chlorophyll a, fucoxanthin, and the ratio of fucoxanthin to chlorophyll a—against hyperspectral reflectance that was resampled to AVIRIS bands. PLSR proved to be a more successful model because of its hyperdimensional analysis capabilities in accounting for multiple wavelength bands, reaching R2 values of 0.67. Using this information, I constructed a model that predicts kelp pigments from simulated AVIRIS reflectance using a spatial time series of laboratory spectral measurements and photosynthetic pigment concentrations. These results have implications, not only for kelp, but many other photosynthetic organisms detectable by hyperspectral airborne or satellite sensors. With these findings, airborne optical data could possibly predict a plethora of other biogeochemical traits. Potentially, this research would permit scientists to acquire data analogous to in situ measurements about floating matters that cannot financially and pragmatically be accessed by anything other than a remote sensor.

      Isabelle Cobb
      Correlations Between SSHa and Chl-a Concentrations in the Northern South China Sea
      Isabelle Cobb
      Sea surface height anomalies (SSHa)–variations in sea surface height from climatological averages–occur on seasonal timescales due to coastal upwelling and El Niño-Southern Oscillation (ENSO) cycles. These anomalies are heightened when upwelling plumes bring cold, nutrient-rich water to the surface, and are particularly strong along continental shelves in the Northern South China Sea (NSCS). This linkage between SSHa and nutrient availability has interesting implications for changing chlorophyll-a (chl-a) concentrations, a prominent indicator of phytoplankton biomass that is essential to the health of marine ecosystems. Here, we evaluate the long-term (15 years) relationship between SSHa and chl-a, in both satellite remote sensing data and in situ measurements. Level 3 SSHa data from Jason 1/2/3 satellites and chl-a data from MODIS Aqua were acquired and binned to monthly resolution. We found a significant inverse correlation between SSHa and chl-a during upwelling months in both the remote sensing (Spearman’s R=-0.57) and in situ data, with higher resolution in situ data from ORAS4 (an assimilation of buoy observations from 2003-2017) showing stronger correlations (Spearman’s R=-0.75). In addition, the data reveal that the magnitude of SSH increases with time during instances of high correlation, possibly indicating a trend of increased SSH associated with reduced seasonal chl-a concentrations. Thus, this relationship may inform future work predicting nutrient availability and threats to marine ecosystems as climate change continues to affect coastal sea surface heights.

      Alyssa Tou
      Exploring Coastal Sea Surface Temperature Anomalies and their effect on Coastal Fog through analyzing Plant Phenology
      Alyssa Tou
      Marine heat waves (MHW) have been increasing in frequency, duration and intensity, giving them substantial potential to influence ecosystems. Do these MHWs sufficiently enhance coastal precipitation such that plant growth is impacted? Recently, the Northeast Pacific experienced a long, intense MHW in 2014/2015, and another short, less intense MHW in 2019/2020. Here we investigate how the intensity and duration of MHWs influence the intensity and seasonal cycle of three different land cover types (‘grass’, ‘trees’, and a combination of both ‘combined’’) to analyze plant phenology trends in Big Sur, California. We hypothesize that longer intense MHWs decrease the ocean’s evaporative capacity, decreasing fog, thus lowering plant productivity, as measured by Normalized Difference Vegetation Index (NDVI). Sea surface temperature (SST) and NDVI data were collected from the NOAA Coral Reef Watch, and NASA MODIS/Terra Vegetation Indices 16-Day L3 Global 250m products respectively. Preliminary results show no correlation (R2=0.02) between SSTa and combined NDVI values and no correlation (R2=0.01) between SST and NDVI. This suggests that years with anomalously high SST do not significantly impact plant phenology. During the intense and long 2014/2015 MHW, peak NDVI values for ‘grass’ and ‘combined’ pixels were 2.0 and 1.7 standard deviations above the climatological average, while the shorter 2019/2020 MHW saw higher peaks of 3.2 and 2.4 standard deviations. However, the ‘grass’, ‘tree’ and ‘combined’ NDVI anomalies were statistically insignificant during both MHWs, showing that although NDVI appeared to increase during the shorter and less intense MHW, these values may be attributed to other factors. The data qualitatively suggest that MHW’s don’t impact the peak NDVI date, but more data at higher temporal resolution are necessary. Further research will involve analyzing fog indices and exploring confounding variables impacting NDVI, such as plant physiology, anthropogenic disturbance, and wildfires. In addition, it’s important to understand to what extent changes in NDVI are attributed to the driving factors of MHWs or the MHWs themselves. Ultimately, mechanistically understanding the impacts MHW intensity and duration have on terrestrial ecosystems will better inform coastal community resilience.


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