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By NASA
8 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Tom Bell, Woods Hole Oceanographic Institution
Dr. Kelsey Bisson, NASA Headquarters Science Mission Directorate
Graduate Mentor:
Kelby Kramer, Massachusetts Institute of Technology
Kelby Kramer, Graduate Mentor
Kelby Kramer, graduate mentor for the 2024 SARP Ocean Remote Sensing group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Lucas DiSilvestro
Shallow Water Benthic Cover Type Classification using Hyperspectral Imagery in Kaneohe Bay, Oahu, Hawaii
Lucas DiSilvestro
Quantifying the changing structure and extent of benthic coral communities is essential for informing restoration efforts and identifying stressed regions of coral. Accurate classification of shallow-water benthic coral communities requires high spectral and spatial resolution, currently not available on spaceborne sensors, to observe the seafloor through an optically complex seawater column. Here we create a shallow water benthic cover type map of Kaneohe Bay, Oahu, Hawaii using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) without requiring in-situ data as inputs. We first run the AVIRIS data through a semi-analytical inversion model to derive color dissolved organic matter, chlorophyll concentration, bottom albedo, suspended sediment, and depth parameters for each pixel, which are then matched to a Hydrolight simulated water column. Pure reflectance for coral, algae, and sand are then projected through each water column to create spectral endmembers for each pixel. Multiple Endmember Spectral Mixture Analysis (MESMA) provides fractional cover of each benthic class on a per-pixel basis. We demonstrate the efficacy of using simulated water columns to create surface reflectance spectral endmembers as Hydrolight-derived in-situ endmember spectra strongly match AVIRIS surface reflectance for corresponding locations (average R = 0.96). This study highlights the capabilities of using medium-fine resolution hyperspectral imagery to identify fractional cover type of localized coral communities and lays the groundwork for future spaceborne hyperspectral monitoring of global coral communities.
Atticus Cummings
Quantifying Uncertainty In Kelp Canopy Remote Sensing Using the Harmonized Landsat Sentinel-2 Dataset
Atticus Cummings
California’s giant kelp forests serve as a major foundation for the region’s rich marine biodiversity and provide recreational and economic value to the State of California. With the rising frequency of marine heatwaves and extreme weather onset by climate change, it has become increasingly important to study these vital ecosystems. Kelp forests are highly dynamic, changing across several timescales; seasonally due to nutrient concentrations, waves, and predator populations, weekly with typical growth and decay, and hourly with the tides and currents. Previous remote sensing of kelp canopies has relied on Landsat imagery taken with a eight-day interval, limiting the ability to quantify more rapid changes. This project aims to address uncertainty in kelp canopy detection using the Harmonized Landsat and Sentinel-2 (HLS) dataset’s zero to five-day revisit period. A random forest classifier was used to identify pixels that contain kelp, on which Multiple Endmember Spectral Mixture Analysis (MESMA) was then run to quantify intrapixel kelp density. Processed multispectral satellite images taken within 3 days of one another were paired for comparison. The relationship between fluctuations in kelp canopy density with tides and currents was assessed using in situ data from an acoustic doppler current profiler (ADCP) at the Santa Barbara Long Term Ecological Research site (LTER) and a NOAA tidal buoy. Preliminary results show that current and tidal trends cannot be accurately correlated with canopy detection due to other sources of error. We found that under cloud-free conditions, canopy detection between paired images varied on average by 42%. Standardized image processing suggests that this uncertainty is not created within the image processing step, but likely arises due to exterior factors such as sensor signal noise, atmospheric conditions, and sea state. Ultimately, these errors could lead to misinterpretation of remotely sensed kelp ecosystems, highlighting the need for further research to identify and account for uncertainties in remote sensing of kelp canopies.
Jasmine Sirvent
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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Last Updated Nov 22, 2024 Related Terms
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By NASA
5 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
A prototype of a robot designed to explore subsurface oceans of icy moons is reflected in the water’s surface during a pool test at Caltech in September. Conducted by NASA’s Jet Propulsion Laboratory, the testing showed the feasibility of a mission concept for a swarm of mini swimming robots.NASA/JPL-Caltech In a competition swimming pool, engineers tested prototypes for a futuristic mission concept: a swarm of underwater robots that could look for signs of life on ocean worlds.
When NASA’s Europa Clipper reaches its destination in 2030, the spacecraft will prepare to aim an array of powerful science instruments toward Jupiter’s moon Europa during 49 flybys, looking for signs that the ocean beneath the moon’s icy crust could sustain life. While the spacecraft, which launched Oct. 14, carries the most advanced science hardware NASA has ever sent to the outer solar system, teams are already developing the next generation of robotic concepts that could potentially plunge into the watery depths of Europa and other ocean worlds, taking the science even further.
This is where an ocean-exploration mission concept called SWIM comes in. Short for Sensing With Independent Micro-swimmers, the project envisions a swarm of dozens of self-propelled, cellphone-size swimming robots that, once delivered to a subsurface ocean by an ice-melting cryobot, would zoom off, looking for chemical and temperature signals that could indicate life.
Dive into underwater robotics testing with NASA’s futuristic SWIM (Sensing With Independent Micro-swimmers) concept for a swarm of miniature robots to explore subsurface oceans on icy worlds, and see a JPL team testing a prototype at a pool at Caltech in Pasadena, California, in September 2024. NASA/JPL-Caltech “People might ask, why is NASA developing an underwater robot for space exploration? It’s because there are places we want to go in the solar system to look for life, and we think life needs water. So we need robots that can explore those environments — autonomously, hundreds of millions of miles from home,” said Ethan Schaler, principal investigator for SWIM at NASA’s Jet Propulsion Laboratory in Southern California.
Under development at JPL, a series of prototypes for the SWIM concept recently braved the waters of a 25-yard (23-meter) competition swimming pool at Caltech in Pasadena for testing. The results were encouraging.
SWIM Practice
The SWIM team’s latest iteration is a 3D-printed plastic prototype that relies on low-cost, commercially made motors and electronics. Pushed along by two propellers, with four flaps for steering, the prototype demonstrated controlled maneuvering, the ability to stay on and correct its course, and a back-and-forth “lawnmower” exploration pattern. It managed all of this autonomously, without the team’s direct intervention. The robot even spelled out “J-P-L.”
Just in case the robot needed rescuing, it was attached to a fishing line, and an engineer toting a fishing rod trotted alongside the pool during each test. Nearby, a colleague reviewed the robot’s actions and sensor data on a laptop. The team completed more than 20 rounds of testing various prototypes at the pool and in a pair of tanks at JPL.
“It’s awesome to build a robot from scratch and see it successfully operate in a relevant environment,” Schaler said. “Underwater robots in general are very hard, and this is just the first in a series of designs we’d have to work through to prepare for a trip to an ocean world. But it’s proof that we can build these robots with the necessary capabilities and begin to understand what challenges they would face on a subsurface mission.”
Swarm Science
A model of the final envisioned SWIM robot, right, sits beside a capsule holding an ocean-composition sensor. The sensor was tested on an Alaskan glacier in July 2023 through a JPL-led project called ORCAA (Ocean Worlds Reconnaissance and Characterization of Astrobiological Analogs). The wedge-shaped prototype used in most of the pool tests was about 16.5 inches (42 centimeters) long, weighing 5 pounds (2.3 kilograms). As conceived for spaceflight, the robots would have dimensions about three times smaller — tiny compared to existing remotely operated and autonomous underwater scientific vehicles. The palm-size swimmers would feature miniaturized, purpose-built parts and employ a novel wireless underwater acoustic communication system for transmitting data and triangulating their positions.
Digital versions of these little robots got their own test, not in a pool but in a computer simulation. In an environment with the same pressure and gravity they would likely encounter on Europa, a virtual swarm of 5-inch-long (12-centimeter-long) robots repeatedly went looking for potential signs of life. The computer simulations helped determine the limits of the robots’ abilities to collect science data in an unknown environment, and they led to the development of algorithms that would enable the swarm to explore more efficiently.
The simulations also helped the team better understand how to maximize science return while accounting for tradeoffs between battery life (up to two hours), the volume of water the swimmers could explore (about 3 million cubic feet, or 86,000 cubic meters), and the number of robots in a single swarm (a dozen, sent in four to five waves).
In addition, a team of collaborators at Georgia Tech in Atlanta fabricated and tested an ocean composition sensor that would enable each robot to simultaneously measure temperature, pressure, acidity or alkalinity, conductivity, and chemical makeup. Just a few millimeters square, the chip is the first to combine all those sensors in one tiny package.
Of course, such an advanced concept would require several more years of work, among other things, to be ready for a possible future flight mission to an icy moon. In the meantime, Schaler imagines SWIM robots potentially being further developed to do science work right here at home: supporting oceanographic research or taking critical measurements underneath polar ice.
More About SWIM
Caltech manages JPL for NASA. JPL’s SWIM project was supported by Phase I and II funding from NASA’s Innovative Advanced Concepts (NIAC) program under the agency’s Space Technology Mission Directorate. The program nurtures visionary ideas for space exploration and aerospace by funding early-stage studies to evaluate technologies that could transform future NASA missions. Researchers across U.S. government, industry, and academia can submit proposals.
How the SWIM concept was developed Learn about underwater robots for Antarctic climate science See NASA’s network of ready-to-roll mini-Moon rovers News Media Contact
Melissa Pamer
Jet Propulsion Laboratory, Pasadena, Calif.
626-314-4928
melissa.pamer@jpl.nasa.gov
2024-162
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Last Updated Nov 20, 2024 Related Terms
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By NASA
MuSat2 at Vandenberg Air Force Base, prior to launch. MuSat2 leverages a dual-frequency science antenna developed with support from NASA to measure phenomena such as ocean wind speed. Muon Space A science antenna developed with support from NASA’s Earth Science Technology Office (ESTO) is now in low-Earth orbit aboard MuSat2, a commercial remote-sensing satellite flown by the aerospace company Muon Space. The dual-frequency science antenna was originally developed as part of the Next Generation GNSS Bistatic Radar Instrument (NGRx). Aboard MuSat2, it will help measure ocean surface wind speed—an essential data point for scientists trying to forecast how severe a burgeoning hurricane will become.
“We’re very interested in adopting this technology and pushing it forward, both from a technology perspective and a product perspective,” said Jonathan Dyer, CEO of Muon.
Using this antenna, MuSat2 will gather signals transmitted by navigation satellites as they scatter off Earth’s surface and back into space. By recording how those scattered navigation signals change as they interact with Earth’s surface, MuSat2 will provide meteorologists with data points they can use to study severe weather.
“We use the standard GPS signals you know—the navigation signals that work for your car and your cell phone,” explained Chris Ruf, director of the University of Michigan Space Institute and principal investigator for NGRx.
Ruf designed the entire NGRx system to be an updated version of the sensors on NASA’s Cyclone Global Navigation Satellite System (CYGNSS), another technology he developed with support from ESTO. Since 2016, data from CYGNSS has been a critical resource for people dedicated to forecasting hurricanes.
The science antenna aboard MuSat2 enables two key improvements to the original CYGNSS design. First, the antenna allows MuSat2 to gather measurements from satellites outside the U.S.-based GPS system, such as the European Space Agency’s Galileo satellites. This capability enables MuSat2 to collect more data as it orbits Earth, improving its assessments of conditions on the planet’s surface.
Second, whereas CYGNSS only collected cross-polar radar signals, the updated science antenna also collects co-polar radar signals. This additional information could provide improved information about soil moisture, sea ice, and vegetation. “There’s a whole lot of science value in looking at both polarization components scattering from the Earth’s surface. You can separate apart the effects of vegetation from the effects of surface, itself,” explained Ruf.
Hurricane Ida, as seen from the International Space Station. NASA-developed technology onboard MuSat2 will help supply the U.S. Air Force with critical data for producing reliable weather forecasts. NASA For Muon Space, this technology infusion has been helpful to the company’s business and science missions. Dallas Masters, Vice President of Muon’s Signals of Opportunity Program, explains that NASA’s investments in NGRx technology made it much easier to produce a viable commercial remote sensing satellite. According to Masters, “NGRx-derived technology allowed us to start planning a flight mission early in our company’s existence, based around a payload we knew had flight heritage.”
Dyer agrees. “The fact that ESTO proves out these measurement approaches – the technology and the instrument, the science that you can actually derive, the products from that instrument – is a huge enabler for companies like ours, because we can adopt it knowing that much of the physics risk has been retired,” he said.
Ultimately, this advanced antenna technology for measuring ocean surface wind speed will make it easier for researchers to turn raw data into actionable science products and to develop more accurate forecasts.
“Information is absolutely precious. When it comes to forecast models and trying to understand what’s about to happen, you have to have as good an idea as you can of what’s already happening in the real world,” said oceanographer Lew Gramer, an Associate Scientist with the Cooperative Institute For Marine And Atmospheric Studies and NOAA’s Hurricane Research Division.
Project Lead: Chris Ruf, University of Michigan
Sponsoring Organizations: NASA’s Earth Science Technology Office and Muon Space
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Last Updated Nov 12, 2024 Related Terms
CYGNSS (Cyclone Global Navigation Satellite System) Earth Science Earth Science Division Earth Science Technology Office Oceans Science-enabling Technology Technology Highlights Explore More
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By European Space Agency
New research, partially funded by ESA, reveals that the cool ‘ocean skin’ allows oceans to absorb more atmospheric carbon dioxide than previously thought. These findings could enhance global carbon assessments, shaping more effective emission-reduction policies.
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