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By NASA
Explore This Section Science Goddard Space Flight Center Linking Satellite Data and… Overview Learning Resources Science Activation Teams SME Map Opportunities More Science Activation Stories Citizen Science 4 min read
Linking Satellite Data and Community Knowledge to Advance Alaskan Snow Science
Seasonal snow plays a significant role in global water and energy cycles, and billions of people worldwide rely on snowmelt for water resources needs, including water supply, hydropower, agriculture, and more. Monitoring snow water equivalent (SWE) is critical for supporting these applications and for mitigating damages caused by snowmelt flooding, avalanches, and other snow-related disasters. However, our ability to measure SWE remains a challenge, particularly in northern latitudes where in situ SWE observations are sparse and satellite observations are impacted by the boreal forest and environmental conditions. Despite limited in situ SWE measurements, local residents in Arctic and sub-Arctic regions provide a vast and valuable body of place-based knowledge and observations that are essential for understanding snowpack behavior in northern regions.
As part of a joint NASA SnowEx, NASA’s Minority University Research and Education Project (MUREP) for American Indian and Alaska Native STEM (Science, Technology, Engineering, & Mathematics) Engagement (MAIANSE), and Global Learning & Observations to Benefit the Environment (GLOBE) Program partnership, a team of scientists including NASA intern Julia White (NASA Goddard Space Flight Center, University of Alaska Fairbanks), Carrie Vuyovich (NASA Goddard Space Flight Center), Alicia Joseph (NASA Goddard Space Flight Center), and Christi Buffington (University of Alaska Fairbanks, GLOBE Implementation Office) is studying snow water equivalent (SWE) across Interior Alaska. This project combines satellite-based interferometric synthetic aperture radar (InSAR) data, primarily from the Sentinel-1 satellite, with ground-based observations from the Snow Telemetry (SNOTEL) network and GLOBE (Global Learning Observations to Benefit the Environment). Together, these data sources help the team investigate how SWE varies across the landscape and how it affects local ecosystems and communities. The team is also preparing for future integration of data from NASA’s upcoming NISAR (NASA ISRO Synthetic Aperture Radar) mission, which is expected to enhance SWE retrieval capabilities.
After a collaborative visit to the classroom of Tammie Kovalenko in November 2024, Delta Junction junior and senior high school students in vocational agriculture (Vo Ag) classes, including members of Future Farmers of America (FFA), began collecting GLOBE data on a snowdrift located just outside their classroom. As the project progressed, students developed their own research questions. One student, Fianna Rooney, took the project even further — presenting research posters at both the GLOBE International Virtual Science Symposium (IVSS) and both the FFA Regional and National Conventions. Her work highlights the growing role of Alaskan youth in science, and how student-led inquiry can enrich both education and research outcomes. (This trip was funded by the NASA Science Activation Program’s Arctic and Earth SIGNs – STEM Integrating GLOBE & NASA – project at the University of Alaska Fairbanks.)
In February 2025, the team collaborated with Delta Junction Junior High and High School students, along with the Delta Junction Trails Association, to conduct a GLOBE Intensive Observation Period (IOP), “Delta Junction Snowdrifts,” to collect Landcover photos, snow depth, and snow water equivalent data. Thanks to aligned interests and research goals at the Alaska Satellite Facility (ASF), the project was further expanded into Spring 2025. Collaborators from ASF and the Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) collected high resolution airborne data over the snowdrift at the Delta Junction Junior and Senior High School. This complementary dataset helped strengthen connections between satellite observations and ground-based student measurements.
This effort, led by a NASA intern, scientists, students, and Alaskan community members, highlights the power of collaboration in advancing science and education. Next steps will include collaboration with Native Alaskan communities near Delta Junction, including the Healy Lake Tribe, whose vast, generational knowledge will be of great value to deepening our understanding of Alaskan snow dynamics.
Learn more about how NASA’s Science Activation program connects NASA science experts, real content, and experiences with community leaders to do science in ways that activate minds and promote deeper understanding of our world and beyond: https://science.nasa.gov/learn/about-science-activation/
Julia White and Delta Junction student following GLOBE protocols for snow depth. Tori Brannan Share
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Last Updated Jul 14, 2025 Editor NASA Science Editorial Team Location Goddard Space Flight Center Related Terms
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By Amazing Space
Massive Solar Prominence "The Beast" Threatens Eruption? Space Weather Update July 14 2025 NASA SDO
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By NASA
The crew of NASA’s SpaceX Crew-11 mission sit inside a Dragon training spacecraft at SpaceX in Hawthorne, California. Pictured from left: Roscosmos cosmonaut Oleg Platonov, NASA astronauts Mike Fincke and Zena Cardman, and JAXA (Japan Aerospace Exploration Agency) astronaut Kimiya Yui (Credit: SpaceX). NASA’s SpaceX Crew-11 mission is set to launch a four-person crew to the International Space Station later this summer. Some of the crew have volunteered to participate in a series of experiments to address health challenges astronauts may face on deep space missions during NASA’s Artemis campaign and future human expeditions to Mars.
The research during Crew-11 includes simulated lunar landings, tactics to safeguard vision, and other human physiology studies led by NASA’s Human Research Program.
Select crew members will participate in a series of simulated Moon landings, before, during, and after their flight. Using a handheld controller and multiple screens, the astronauts will fly through simulated scenarios created to resemble the lunar South Pole region that Artemis crews plan to visit. This experiment allows researchers to evaluate how different gravitational forces may disorient astronauts and affect their ability to pilot a spacecraft, like a lunar lander.
“Even though many landing tasks are automated, astronauts must still know how to monitor the controls and know when to take over to ensure a safe landing,” said Scott Wood, a neuroscientist at NASA’s Johnson Space Center in Houston coordinating the scientific investigation. “Our study assesses exactly how changes in gravity affect spatial awareness and piloting skills that are important for navigating these scenarios.”
A ground control group completing the same tasks over a similar timeframe will help scientists better understand gravitational effects on human performance. The experiment’s results could inform the pilot training needed for future Artemis crews.
“Experiencing weightlessness for months and then feeling greater levels of gravity on a planet like Mars, for example, may increase the risk of disorientation,” said Wood. “Our goal is to help astronauts adapt to any gravitational change, whether it’s to the Moon, a new planet, or landing back on Earth.”
Other studies during the mission will explore possible ways to treat or prevent a group of eye and brain changes that can occur during long-duration space travel, called spaceflight associated neuro-ocular syndrome (SANS).
Some researchers suspect the redistribution of bodily fluids in constant weightlessness may increase pressure in the head and contribute to SANS. One study will investigate fluid pressure on the brain while another will examine how the body processes B vitamins and whether supplements can affect how astronauts respond to bodily fluid shifts. Participating crew members will test whether a daily B vitamin supplement can eliminate or ease symptoms of SANS. Specific crew members also will wear thigh cuffs to keep bodily fluids from traveling headward.
Crew members also will complete another set of experiments, called CIPHER (Complement of Integrated Protocols for Human Exploration Research), which measures how multiple systems within the human body change in space. The study includes vision assessments, MRI scans, and other medical exams to provide a complete overview of the whole body’s response to long-duration spaceflight.
Several other studies involving human health and performance are also a part of Crew-11’s science portfolio. Crew members will contribute to a core set of measurements called Spaceflight Standard Measures, which collects physical data and biological samples from astronauts and stores them for other comparative studies. Participants will supply biological samples, such as blood and urine, for a study characterizing how spaceflight alters astronauts’ genetic makeup. In addition, volunteers will test different exercise regimens to help scientists explore what activities remain essential for long-duration journeys.
After landing, participating crew members will complete surveys to track any discomfort, such as scrapes or bruises, acquired from re-entry. The data will help clarify whether mission length increases injury risks and could help NASA design landing systems on future spacecraft as NASA prepares to travel to the Moon, Mars, and beyond.
NASA’s Human Research Program pursues methods and technologies to support safe, productive human space travel. Through science conducted in laboratories, ground-based analogs, and aboard the International Space Station, the program investigates how spaceflight affects human bodies and behaviors. Such research drives NASA’s quest to innovate ways that keep astronauts healthy and mission-ready.
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
The high-altitude WB-57 aircraft departed July 8, 2025, from Ellington Field in Houston, Texas, headed to the Texas Hill Country. The aircraft will use the DyNAMITE (Day/Night Airborne Motion Imager for Terrestrial Environments) sensor system to take video mosaics of the area to assist with the emergency response effort. Photo Credit: NASA/Morgan Gridley In response to recent flooding near Kerrville, Texas, NASA deployed two aircraft to assist state and local authorities in ongoing recovery operations.
The aircraft are part of the response from NASA’s Disasters Response Coordination System, which is activated to support emergency response for the flooding and is working closely with the Texas Division of Emergency Management, the Federal Emergency Management Agency (FEMA), and the humanitarian groups Save the Children and GiveDirectly.
Persistent cloud-cover has made it difficult to obtain clear satellite imagery, so the Disasters Program coordinated with NASA’s Airborne Science Program at NASA’s Johnson Space Flight Center in Houston to conduct a series of flights to gather observations of the impacted regions. NASA is sharing these data directly with emergency response teams to inform their search and rescue efforts and aid decision-making and resource allocation.
The high-altitude WB-57 aircraft operated by NASA Johnson departed from Ellington Field on July 8 to conduct aerial surveys. The aircraft is equipped with the DyNAMITE (Day/Night Airborne Motion Imager for Terrestrial Environments) sensor.
The DyNAMITE sensor views the Guadalupe River[KA1] [RC2] and several miles of the surrounding area, providing high-resolution imagery critical to assessing damage and supporting coordination of ground-based recovery efforts. This system enables real-time collection and analysis of data, enhancing situational awareness and accelerating emergency response times.
In addition, the agency’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is flying out of NASA’s Armstrong Flight Research Center in Edwards, California, aboard a Gulfstream III. Managed by the agency’s Jet Propulsion Laboratory in Southern California, the UAVSAR team is planning to collect observations over the Guadalupe, San Gabriel, and Colorado river basins Wednesday, Thursday, and Friday. Because UAVSAR can penetrate vegetation to spot water that optical sensors are unable to detect, the team’s goal is to characterize the extent of flooding to help with understanding the amount of damage within communities.
Flights are being coordinated with FEMA, the Texas Division of Emergency Management, and local responders to ensure data is quickly delivered to those making decisions on the ground. Imagery collected will be sent to NASA’s Disaster Response Coordination System.
Additionally, the Disasters Program, which is part of NASA’s Earth Science Division, is working to produce maps and data to assess the location and severity of flooding in the region and damage to buildings and infrastructure. These data are being shared on the NASA Disasters Mapping Portal as they become available.
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Last Updated Jul 09, 2025 Related Terms
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Smarter Searching: NASA AI Makes Science Data Easier to Find
Image snapshot taken from NASA Worldview of NASA’s Global Precipitation Measurement (GPM) mission on March 15, 2025 showing heavy rain across the southeastern U.S. with an overlay of the GCMD Keyword Recommender for Earth Science, Atmosphere, Precipitation, Droplet Size. NASA Worldview Imagine shopping for a new pair of running shoes online. If each seller described them differently—one calling them “sneakers,” another “trainers,” and someone else “footwear for exercise”—you’d quickly feel lost in a sea of mismatched terminology. Fortunately, most online stores use standardized categories and filters, so you can click through a simple path: Women’s > Shoes > Running Shoes—and quickly find what you need.
Now, scale that problem to scientific research. Instead of sneakers, think “aerosol optical depth” or “sea surface temperature.” Instead of a handful of retailers, it is thousands of researchers, instruments, and data providers. Without a common language for describing data, finding relevant Earth science datasets would be like trying to locate a needle in a haystack, blindfolded.
That’s why NASA created the Global Change Master Directory (GCMD), a standardized vocabulary that helps scientists tag their datasets in a consistent and searchable way. But as science evolves, so does the challenge of keeping metadata organized and discoverable.
To meet that challenge, NASA’s Office of Data Science and Informatics (ODSI) at the agency’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, developed the GCMD Keyword Recommender (GKR): a smart tool designed to help data providers and curators assign the right keywords, automatically.
Smarter Tagging, Accelerated Discovery
The upgraded GKR model isn’t just a technical improvement; it’s a leap forward in how we organize and access scientific knowledge. By automatically recommending precise, standardized keywords, the model reduces the burden on human curators while ensuring metadata quality remains high. This makes it easier for researchers, students, and the public to find exactly the datasets they need.
It also sets the stage for broader applications. The techniques used in GKR, like applying focal loss to rare-label classification problems and adapting pre-trained transformers to specialized domains, can benefit fields well beyond Earth science.
Metadata Matchmaker
The newly upgraded GKR model tackles a massive challenge in information science known as extreme multi-label classification. That’s a mouthful, but the concept is straightforward: Instead of predicting just one label, the model must choose many, sometimes dozens, from a set of thousands. Each dataset may need to be tagged with multiple, nuanced descriptors pulled from a controlled vocabulary.
Think of it like trying to identify all the animals in a photograph. If there’s just a dog, it’s easy. But if there’s a dog, a bird, a raccoon hiding behind a bush, and a unicorn that only shows up in 0.1% of your training photos, the task becomes far more difficult. That’s what GKR is up against: tagging complex datasets with precision, even when examples of some keywords are scarce.
And the problem is only growing. The new version of GKR now considers more than 3,200 keywords, up from about 430 in its earlier iteration. That’s a sevenfold increase in vocabulary complexity, and a major leap in what the model needs to learn and predict.
To handle this scale, the GKR team didn’t just add more data; they built a more capable model from the ground up. At the heart of the upgrade is INDUS, an advanced language model trained on a staggering 66 billion words drawn from scientific literature across disciplines—Earth science, biological sciences, astronomy, and more.
NASA ODSI’s GCMD Keyword Recommender AI model automatically tags scientific datasets with the help of INDUS, a large language model trained on NASA scientific publications across the disciplines of astrophysics, biological and physical sciences, Earth science, heliophysics, and planetary science. NASA “We’re at the frontier of cutting-edge artificial intelligence and machine learning for science,” said Sajil Awale, a member of the NASA ODSI AI team at MSFC. “This problem domain is interesting, and challenging, because it’s an extreme classification problem where the model needs to differentiate even very similar keywords/tags based on small variations of context. It’s exciting to see how we have leveraged INDUS to build this GKR model because it is designed and trained for scientific domains. There are opportunities to improve INDUS for future uses.”
This means that the new GKR isn’t just guessing based on word similarities; it understands the context in which keywords appear. It’s the difference between a model knowing that “precipitation” might relate to weather versus recognizing when it means a climate variable in satellite data.
And while the older model was trained on only 2,000 metadata records, the new version had access to a much richer dataset of more than 43,000 records from NASA’s Common Metadata Repository. That increased exposure helps the model make more accurate predictions.
The Common Metadata Repository is the backend behind the following data search and discovery services:
Earthdata Search International Data Network Learning to Love Rare Words
One of the biggest hurdles in a task like this is class imbalance. Some keywords appear frequently; others might show up just a handful of times. Traditional machine learning approaches, like cross-entropy loss, which was used initially to train the model, tend to favor the easy, common labels, and neglect the rare ones.
To solve this, NASA’s team turned to focal loss, a strategy that reduces the model’s attention to obvious examples and shifts focus toward the harder, underrepresented cases.
The result? A model that performs better across the board, especially on the keywords that matter most to specialists searching for niche datasets.
From Metadata to Mission
Ultimately, science depends not only on collecting data, but on making that data usable and discoverable. The updated GKR tool is a quiet but critical part of that mission. By bringing powerful AI to the task of metadata tagging, it helps ensure that the flood of Earth observation data pouring in from satellites and instruments around the globe doesn’t get lost in translation.
In a world awash with data, tools like GKR help researchers find the signal in the noise and turn information into insight.
Beyond powering GKR, the INDUS large language model is also enabling innovation across other NASA SMD projects. For example, INDUS supports the Science Discovery Engine by helping automate metadata curation and improving the relevancy ranking of search results.The diverse applications reflect INDUS’s growing role as a foundational AI capability for SMD.
The INDUS large language model is funded by the Office of the Chief Science Data Officer within NASA’s Science Mission Directorate at NASA Headquarters in Washington. The Office of the Chief Science Data Officer advances scientific discovery through innovative applications and partnerships in data science, advanced analytics, and artificial intelligence.
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Last Updated Jul 09, 2025 Related Terms
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