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Preparations for Next Moonwalk Simulations Underway (and Underwater)

Faculty Advisors:

Dr. Lisa Haber, Virginia Commonwealth University

Dr. Brandon Alveshere, Virginia Commonwealth University

Dr. Chris Gough, Virginia Commonwealth University

Graduate Mentor:

Mindy Priddy, Virginia Commonwealth University

Mindy Priddy, Graduate Mentor

Mindy Priddy, graduate mentor for the 2024 SARP Terrestrial Fluxes group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.

Angelina De La Torre

Using NDVI as a Proxy for GPP to Predict Carbon Dioxide Fluxes

Angelina De La Torre

Climate change, driven primarily by greenhouse gases, poses a threat to the future of our planet. Among these gases is carbon dioxide (CO₂), which has a much longer atmospheric residence time compared to other greenhouse gases. One potential factor in reducing atmospheric CO₂ enrichment is plant productivity. Gross Primary Productivity (GPP) estimates the amount of CO₂ fixed during photosynthesis. The Normalized Difference Vegetation Index (NDVI) provides insight into the health of an ecosystem by measuring the density and greenness of vegetation. Therefore, it can be inferred that there is a relationship between NDVI and GPP, as greener plants are likely more productive. In this study, we used NDVI as a proxy for GPP and analyzed the effect NDVI had on CO₂ fluxes during California’s wet season between January and March 2023 in a restored tidal freshwater wetland. GPP and CO₂ flux data were obtained from the Dutch Slough AmeriFlux tower in Oakley, California. Landsat data were used to calculate the average NDVI. The influence of NDVI on GPP was assessed using linear regression. A second linear regression was then performed using NDVI and CO₂ flux, of which GPP is one component. We anticipate that wetlands with greater vegetation density will have lower CO₂ emissions.

Because Landsat data scans in 16-day intervals, daily variation in NDVI could not be observed. This translates to a frequency discrepancy between the Landsat and AmeriFlux data, as AmeriFlux towers measure in half-hour intervals. Additionally, the wet season represented was limited by data availability, as the data before 2023 were unavailable. Despite data limitations in this study, the outlined process could be repeated in various wetland and climate classifications for further analysis of a larger sample size. This study could assist in developing strategies to increase CO₂ sequestration in an attempt to slow the effects of climate change.

Samarth Jayadev

Using Machine Learning to Assess Relationships between NDVI and Net Carbon Exchange During the COVID-19 Pandemic

Samarth Jayadev

Understanding the movement of carbon between Earth’s land surface and atmosphere is essential for ecosystem monitoring, creating climate change mitigation strategies, and assessing the carbon budget on national to global scales. Measures of greenness serve as indicators of processes such as photosynthesis that control carbon exchange and are vital in modeling of carbon fluxes. NASA’s Orbiting Carbon Observatory (OCO-2) provides high quality measurements of column-averaged CO₂ concentrations that can be used to derive net carbon exchange (NCE), a measure of CO₂ flux between terrestrial ecosystems and the atmosphere.

From OCO-2, NCE data collected at the land nadir, land glint satellite position combined with in situ sampling can provide accurate measurements on a 1°x1° scale suitable for carbon flux characterization across the contiguous United States (CONUS). Normalized difference vegetation index (NDVI), which ranges from -1 to +1, measures the greenness of vegetation, serving as an indicator of plant density and health. This can help to understand ecosystem to carbon-cycle interactions and be leveraged for determining patterns with NCE. We examined the relationship between NDVI and NCE across CONUS during 2020 using Gradient Boosting Decision Trees (GBDT) which specialize in classifying and predicting non-linear relationships. This algorithm takes multiple weak learners (decision trees) and combines their predictions in an iterative ensemble method to improve prediction accuracy. Feature and permutation importance tests found that January and August (trough and peak NDVI, respectively) were the highest weighted predictor variables related to NCE. The dataset was split in a 90% training 10% test ratio across latitude/longitude grid cells to assess and verify model performance. Using the mean squared error loss function and hyperparameters with optimal estimators, tree depth, sample split, and learning rate the algorithm was able to converge the test predictions to match the deviance of the training data. The gradient boosting model can be applied to different months and years of NDVI/NCE to further explore these relationships or a multitude of research questions. Further studies should consider integrating land use and land cover change variables such as bare land and urbanization to improve predictions of NCE.

Makai Ogoshi

Deep-learning Derived Spaceborne Canopy Structural Metrics Predict Forest Carbon Fluxes

Makai Ogoshi

Terrestrial and airborne lidar data products describing canopy structure are potent predictors of forest carbon fluxes, but whether satellite data products produce similarly robust indicators of canopy structure is not known. The assessment of contemporary spaceborne lidar and other remote sensing data products as predictors of carbon fluxes is crucial to next generation instrument and data product design and large-spatial scale modeling. We investigated relationships between deciduous broadleaf forest canopy structure, derived from deep-learning models created with lidar data from GEDI and optical imagery from Sentinel-2, and forest carbon exchange. These included comparisons to in-situ continuous net ecosystem exchange (NEE), gross primary production (GPP), and net primary production (NPP). We find that the mean  canopy height from the gridded spaceborne product has a strong correlation with forest NPP, similar to prior analysis with ground-based lidar (portable canopy lidar; PCL). For comparison to NPP, heights taken from the gridded spaceborne product were compared by overlapping the product with nine terrestrial forest sites from the National Ecological Observatory Network (NEON). We used standard deviation of canopy height as a measure of canopy structural complexity. Complexity derived from the gridded spaceborne product does not show the same strong correlation with NPP as found when using PCL. Mean annual GPP and NEE across five years were compared to the gridded spaceborne product at six Fluxnet2015-tower sites with continuous, gap-filled carbon flux data. When compared to in-situ flux tower data, neither mean canopy height nor structural complexity strongly correlate to annual NEE or GPP. Primarily, the finding that derived spaceborne products exhibit a strong correlation between forest canopy height and NPP will advance global-scale application of forest-carbon flux predictions. Secondarily, a variety of limitations highlight shortcomings in the current terrestrial flux data network. A small number of available study sites, both spatially and temporally, and lack of resolution in vertical complexity of canopy structure both contribute to uncertainty in assessing the relationships to NEE and GPP.

Sebastian Reed

Porewater Methane Concentrations Vary Significantly Across A Freshwater Tidal Wetland

Sebastian Reed

Methane is a potent greenhouse gas that is over 80 times more powerful than CO₂ at trapping heat and accounts for an estimated 30% of global temperature rise associated with climate change. The largest natural source of methane worldwide is wetlands. Despite the role of methane in driving climate change, the magnitude of global annual wetland methane flux remains highly uncertain. This study analyzes the effects of greenness (assessed using Normalized Difference Vegetation Index; NDVI), plant species composition, rooting depth, atmospheric methane concentration, and plant longevity on porewater methane concentration at the Kimages Rice Rivers Center tidal freshwater wetland. Samples for atmospheric and porewater concentrations were conducted in situ in June 2024. For each sampling location (n = 23) we collected whole air samples (WAS) 2m above the marsh surface and porewater samples 5cm below the marsh surface. We visually assessed species composition at each sample location, with 12 species of wetland plants present overall. We used the TRY plant database to find the rooting depth, leaf nitrogen content, and lifespan of each species. Drone multispectral data from 2023 was used to estimate NDVI values. These variables were compared to the pore water methane concentration via stepwise linear regression. Leaf N content, NDVI, plant species, and WAS sampling did not show statistically significant correlation to porewater methane concentration. Rooting depth showed a slight positive correlation with porewater methane (alpha = 0.1, p = 0.08, R^2 = 0.1). Samples with only perennial plants (as opposed to annual plants) had a higher mean value of porewater methane (p = 0.1). Analyzing porewater methane provides insight as to what wetland components affect methanogenesis and methane release, which aids in assessing which plant functional traits are most responsible for driving or mitigating climate change. Results from this study and future research in this area has the potential to more accurately assess how methane cycles through wetlands to the atmosphere.

Nohemi Rodarte

Understanding the vertical profile of CO₂ concentration: How carbon dioxide levels change with altitude

Nohemi Rodarte

Carbon dioxide (CO₂) is one of the main greenhouse gasses that contribute to global warming.While the relationship between CO₂ concentrations and land cover types, such as forests and urban areas, is well documented, there is limited knowledge of how CO₂ concentrations vary with altitude at fine spatial scales. Guided by our hypothesis that CO₂ levels vary with altitude and increase with elevation, we used airborne data collected from the B200 aircraft, which flew at different altitudes (400 to 1200 feet) above the urban area of Hopewell, Virginia, between 9:40 AM and 10:40 AM. We analyzed the CO₂ concentrations recorded by the flight to obtain the median and range for each 100 feet of altitude. Our results reveal that carbon dioxide concentrations varied significantly across the range of altitudes investigated. Within the area studied, CO₂ concentrations were found to range between 410 and 470 ppm. The distribution of these concentrations along the altitude gradient shows a bimodal pattern, with notable peaks at altitudes of 700 to 800 feet and 1100 to 1200 feet. Although CO₂ levels were present at all measured altitudes, there was a noticeable drop in the mean concentration at 800 feet,which then stabilized until reaching 1,000 feet before rising again. This pattern indicates that the concentrations of this greenhouse gas are not uniformly distributed with altitude, but rather vary significantly, showing higher concentrations at certain elevations and lower concentrations at others. The CO₂ distribution fluctuates with altitude, showing higher or lower levels at specific heights rather than a smooth gradient, indicating that altitude impacts CO₂ concentrations. While we did not identify the drivers of this change, future studies could evaluate how factors such as surface emissions, atmospheric mixing, and local conditions may contribute to vertical CO₂ profiles, since the altitudes we considered in this research are within the troposphere.

Camille Shaw

Linking NDVI with CO₂ and CH₄ Fluxes: Insights into Vegetation and Urban Source-Sink Dynamics in the Great Dismal Swamp

Camille Shaw

In recent years, carbon dioxide, methane, and other greenhouse gases have gained attention because of their contribution to the rise in Earth’s global mean temperature. Methane and carbon dioxide have various sources and sinks, but an expanding array of sources have created a need to assess ongoing change in carbon balance. This study aims to quantify the relationship between Normalized Difference Vegetation Index, or NDVI, and methane and carbon dioxide fluxes. We measured carbon dioxide and methane concentrations within the boundary layer using the PICARRO instrument, focusing on the Great Dismal Swamp, a forested wetland, and surrounding areas in the Eastern Mid-Atlantic Region. Data collection occurred at various times of day and along different flight paths in 2016, 2017, and 2024, with each year representing data from a single season, either spring or fall, for temporal analysis. We calculated methane and carbon dioxide fluxes along the flight paths using airborne eddy covariance, a method for capturing accurate flux measurements while accounting for the mixing of gases in the boundary layer caused by heat. Additionally, we calculated NDVI for this area using NASA’s Landsat 8 and 9 satellite imagery. Analysis of the afternoon flight data revealed a negative linear correlation between NDVI and carbon dioxide flux. Urban areas, characterized by low NDVI, exhibit a positive carbon dioxide flux as a consequence of emissions from vehicles, while forested areas, with high NDVI, show a negative carbon dioxide flux because of photosynthesis. In contrast, methane flux shows minimal correlation with NDVI. The lack of correlation arises because forested wetlands, with high NDVI, emit substantial amounts of methane, while urban areas, despite having low NDVI, still produce significant methane emissions from landfills and industrial activities. Future research could further investigate how seasonal and diurnal variations influence the correlations between NDVI and greenhouse gases by collecting comprehensive data across all seasons within a given year and at various times of the day.

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Last Updated
Nov 22, 2024

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      October 2014 Astrogram September 2010 Astrogram I have served as the editor of the Astrogram since February 1998. Over the past quarter century, it has been an interesting, and sometimes quite challenging, task for me to capture the breadth and depth of Ames’s story and ensure that we always published the newsletter on time. I still remember trekking over to the center’s imaging office to review the physical negatives and images that the Ames photographers had taken of events onsite and select the most compelling photos. I used a very early version of visual design software to craft the layout. When the paper was completed, I’d file it onto a CD and then hand it to the courier who would drive from the San Francisco printshop to pick it up from me. Once and awhile, someone would request to have an additional feature added, requiring multiple trips up the 101 and back. Sometimes I’d come in on the weekends to work on the paper, due to late submissions, much to the chagrin of my kids.
      July 2007 Astrogram It has been a pleasure serving as the editor over the past quarter century, almost as many years as my kids are old. A person once asked me if I had changed my name to Astrid since it’s so like the word Astrogram. Any relationship between the newsletter and my name is simply serendipity. I have enjoyed being behind the scenes, mostly working diligently at my computer. Many at Ames know my name because of the newsletter but may have never met me in person. It’s been amusing sometimes when I encounter someone who can’t put a finger as to why they knew my name but didn’t recognize me standing in front of them. Their usual response when they realized why they know me was, “Ah, Astrid of the Astrogram.”
      March 20, 1998 Astrogram Just as NASA innovates, the content of the Astrogram has to innovate as well. Many of the stories that you used to read in the Astrogram, you can now find on our NASA Ames web page here. If you would like to access past, archived issues of the Astrogram, going back to 1958, please consult the Ames Research Center Archives. I will continue to help tell Ames’s story, just using new platforms.
      Whether this is your first issue or you have been an Astrogram supporter for decades, thank you for reading!
      – Astrid of the Astrogram officially signing off


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      Researchers present techniques for measuring thermal diffusion of molecules in a mixture. Thermal diffusion is measured using the Soret coefficient – the ratio of movement caused by temperature differences to overall movement within the system. This has applications in mineralogy and geophysics such as predicting the location of natural resources beneath Earth’s surface.

      A series of ESA investigations studied diffusion, or how heat and particles move through liquids, in microgravity. Selectable Optical Diagnostics Instrument-Influence of VIbrations on DIffusion of Liquids examined how vibrations affect diffusion in mixtures with two components and SODI-DCMIX measured more-complex diffusion in mixtures of three or more components. Understanding and predicting the effects of thermal diffusion has applications in various industries such as modeling of underground oil reservoirs.
      NASA astronaut Kate Rubins works on Selectable Optical Diagnostics Instrument Experiment Diffusion Coefficient Mixture-3 (SODI) DCMix-3 installation inside the station’s Microgravity Science Glovebox.JAXA (Japan Aerospace Exploration Agency)/Takuya Onishi Research validates ferrofluid technology

      Researchers validated the concept of using ferrofluid technology to operate a thermal control switch in a spacecraft. This outcome could support development of more reliable and long-lasting spacecraft thermal management systems, increasing mission lifespan and improving crew safety.

      Überflieger 2: Ferrofluid Application Research Goes Orbital analyzed the performance of ferrofluids, a technology that manipulates components such as rotors and switches using magnetized liquids and a magnetic field rather than mechanical systems, which are prone to wear and tear. This technology could lower the cost of materials for thermal management systems, reduce the need for maintenance and repair, and help avoid equipment failure. The paper discusses possible improvements to the thermal switch, including optimizing the geometry to better manage heat flow.
      A view of the Ferrofluid Application Research Goes Orbital investigation hardware aboard the International Space Station. UAE (United Arab Emirates)/Sultan AlneyadiView the full article
    • By NASA
      4 min read
      Preparations for Next Moonwalk Simulations Underway (and Underwater)
      NASA/Quincy Eggert NASA’s Armstrong Flight Research Center in Edwards, California, is preparing today for tomorrow’s mission. Supersonic flight, next generation aircraft, advanced air mobility, climate changes, human exploration of space, and the next innovation are just some of the topics our researchers, engineers, and mission support teams focused on in 2024.
      NASA Armstrong began 2024 with the public debut of the X-59 quiet supersonic research aircraft. Through the unique design of the X-59, NASA aims to reduce the sonic boom to make it much quieter, potentially opening the future to commercial supersonic flight over land. Throughout the first part of the year, NASA and international researchers studied air quality across Asia as part of a global effort to better understand the air we breathe. Later in the year, for the first time, a NASA-funded researcher conducted an experiment aboard a commercial suborbital rocket, studying how changes in gravity during spaceflight affect plant biology.
      Here’s a look at more NASA Armstrong accomplishments throughout 2024:
      Our simulation team began work on NASA’s X-66 simulator, which will use an MD-90 cockpit and allow pilots and engineers to run real-life scenarios in a safe environment. NASA Armstrong engineers completed and tested a model of a truss-braced wing design, laying the groundwork for improved commercial aircraft aerodynamics. NASA’s Advanced Air Mobility mission and supporting projects worked with industry partners who are building innovative new aircraft like electric air taxis. We explored how these new designs may help passengers and cargo move between and inside cities efficiently. The team began testing with a custom virtual reality flight simulator to explore the air taxi ride experience. This will help designers create new aircraft with passenger comfort in mind. Researchers also tested a new technology that will help self-flying aircraft avoid hazards. A NASA-developed computer software tool called OVERFLOW helped several air taxi companies predict aircraft noise and aerodynamic performance. This tool allows manufacturers to see how new design elements would perform, saving the aerospace industry time and money. Our engineers designed a camera pod with sensors at NASA Armstrong to help advance computer vision for autonomous aviation and flew this pod at NASA’s Kennedy Space Center in Florida. NASA’s Quesst mission marked a major milestone with the start of tests on the engine that will power the quiet supersonic X-59 experimental aircraft. In February and March, NASA joined international researchers in Asia to investigate pollution sources. The now retired DC-8 and NASA Langley Gulfstream III aircraft collected air measurements over the Philippines, South Korea, Malaysia, Thailand, and Taiwan. Combined with ground and satellite observations, these measurements continue to enrich global discussions about pollution origins and solutions. The Gulfstream IV joined NASA Armstrong’s fleet of airborne science platforms. Our teams modified the aircraft to accommodate a next-generation science instrument that will collect terrain information of the Earth in a more capable, versatile, and maintainable way. The ER-2 and the King Air supported the development of spaceborne instruments by testing them in suborbital settings. On the Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment mission (PACE-PAX), the ER-2 validated data collected by the PACE satellite about the ocean, atmosphere, and surfaces. Operating over several countries, researchers onboard NASA’s C-20A collected data and images of Earth’s surface to understand global ecosystems, natural hazards, and land surface changes. Following Hurricane Milton, the C-20A flew over affected areas to collect data that could help inform disaster response in the future. We also tested nighttime precision landing technologies that safely deliver spacecraft to hazardous locations with limited visibility. With the goal to improve firefighter safety, NASA, the U.S. Forest Service, and industry tested a cell tower in the sky. The system successfully provided persistent cell coverage, enabling real-time communication between firefighters and command posts. Using a 1960s concept wingless, powered aircraft design, we built and tested an atmospheric probe to better and more economically explore giant planets. NASA Armstrong hosted its first Ideas to Flight workshop, where subject matter experts shared how to accelerate research ideas and technology development through flight. These are just some of NASA Armstrong’s many innovative research efforts that support NASA’s mission to explore the secrets of the universe for the benefit of all.
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      Last Updated Dec 20, 2024 EditorDede DiniusContactSarah Mannsarah.mann@nasa.govLocationArmstrong Flight Research Center Related Terms
      Armstrong Flight Research Center Advanced Air Mobility Aeronautics C-20A DC-8 Earth Science ER-2 Flight Opportunities Program Quesst (X-59) Sustainable Flight Demonstrator Explore More
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    • By NASA
      At Goddard Space Flight Center, the GSFC Data Science Group has completed the testing for their SatVision Top-of-Atmosphere (TOA) Foundation Model, a geospatial foundation model for coarse-resolution all-sky remote sensing imagery. The team, comprised of Mark Carroll, Caleb Spradlin, Jordan Caraballo-Vega, Jian Li, Jie Gong, and Paul Montesano, has now released their model for wide application in science investigations.
      Foundation models can transform the landscape of remote sensing (RS) data analysis by enabling the pre-training of large computer-vision models on vast amounts of remote sensing data. These models can be fine-tuned with small amounts of labeled training and applied to various mapping and monitoring applications. Because most existing foundation models are trained solely on cloud-free satellite imagery, they are limited to applications of land surface or require atmospheric corrections. SatVision-TOA is trained on all-sky conditions which enables applications involving atmospheric variables (e.g., cloud or aerosol).
      SatVision TOA is a 3 billion parameter model trained on 100 million images from Moderate Resolution Imaging Spectroradiometer (MODIS). This is, to our knowledge, the largest foundation model trained solely on satellite remote sensing imagery. By including “all-sky” conditions during pre-training, the team incorporated a range of cloud conditions often excluded in traditional modeling. This enables 3D cloud reconstruction and cloud modeling in support of Earth and climate science, offering significant enhancement for large-scale earth observation workflows.
      With an adaptable and scalable model design, SatVision-TOA can unify diverse Earth observation datasets and reduce dependency on task-specific models. SatVision-TOA leverages one of the largest public datasets to capture global contexts and robust features. The model could have broad applications for investigating spectrometer data, including MODIS, VIIRS, and GOES-ABI. The team believes this will enable transformative advancements in atmospheric science, cloud structure analysis, and Earth system modeling.
      The model architecture and model weights are available on GitHub and Hugging Face, respectively. For more information, including a detailed user guide, see the associated white paper: SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery. 
      Examples of image reconstruction by SatVision-TOA. Left: MOD021KM v6.1 cropped image chip using MODIS bands [1, 3, 2]. Middle: The same images with randomly applied 8×8 mask patches, masking 60% of the original image. Right: The reconstructed images produced by the model, along with their respective Structural Similarity Index Measure (SSIM) scores. These examples illustrate the model’s ability to preserve structural detail and reconstruct heterogeneous features, such as cloud textures and land-cover transitions, with high fidelity.NASAView the full article
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