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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 NASA
The Axiom Mission 4 and Expedition 73 crews join together for a group portrait inside the International Space Station’s Harmony module. In the front row (from left) are Ax-4 crewmates Tibor Kapu, Peggy Whitson, Shubhanshu Shukla, and Sławosz Uznański-Wiśniewski with Expedition 73 crewmates Anne McClain and Takuya Onishi. In the rear are, Expedition 73 crewmates Alexey Zubritskiy, Kirill Peskov, Sergey Ryzhikov, Jonny Kim, and Nichole Ayers.Credit: NASA NASA will provide live coverage of the undocking and departure of the Axiom Mission 4 private astronaut mission from the International Space Station.
The four-member astronaut crew is scheduled to undock from the space-facing port of the station’s Harmony module aboard the SpaceX Dragon spacecraft at approximately 7:05 a.m. EDT Monday, July 14, pending weather, to begin their return to Earth and splashdown off the coast of California.
Coverage of departure operations will begin with hatch closing at 4:30 a.m. on NASA+. Learn how to watch NASA content through a variety of platforms, including social media.
Peggy Whitson, former NASA astronaut and director of human spaceflight at Axiom Space, ISRO (Indian Space Research Organization) astronaut Shubhanshu Shukla, ESA (European Space Agency) project astronaut Sławosz Uznański-Wiśniewski of Poland, and HUNOR (Hungarian to Orbit) astronaut Tibor Kapu of Hungary, will have spent about two weeks in space at the conclusion of their mission.
The Dragon spacecraft will return with more than 580 pounds of cargo, including NASA hardware and data from over 60 experiments conducted throughout the mission.
NASA’s coverage is as follows (all times Eastern and subject to change based on real-time operations):
Monday, July 14
4:30 a.m. – Hatch closing coverage begins on NASA+.
4:55 a.m. – Crew enters spacecraft followed by hatch closing.
6:45 a.m. – Undocking coverage begins on NASA+, Axiom Space, and SpaceX channels.
7:05 a.m. – Undocking
NASA’s coverage ends approximately 30 minutes after undocking when space station joint operations with Axiom Space and SpaceX conclude. Axiom Space will resume coverage of Dragon’s re-entry and splashdown on the company’s website.
A collaboration between NASA and ISRO allowed Axiom Mission 4 to deliver on a commitment highlighted by President Trump and Indian Prime Minister Narendra Modi to send the first ISRO astronaut to the station. The space agencies participated in five joint science investigations and two in-orbit science, technology, engineering, and mathematics demonstrations. NASA and ISRO have a long-standing relationship built on a shared vision to advance scientific knowledge and expand space collaboration.
The private mission also carried the first astronauts from Poland and Hungary to stay aboard the space station.
The International Space Station is a springboard for developing a low Earth orbit economy. NASA’s goal is to achieve a strong economy off the Earth where the agency can purchase services as one of many customers to meet its science and research objectives in microgravity. NASA’s commercial strategy for low Earth orbit provides the government with reliable and safe services at a lower cost, enabling the agency to focus on Artemis missions to the Moon in preparation for Mars while also continuing to use low Earth orbit as a training and proving ground for those deep space missions.
Learn more about NASA’s commercial space strategy at:
https://www.nasa.gov/commercial-space
-end-
Claire O’Shea
Headquarters, Washington
202-358-1100
claire.a.o’shea@nasa.gov
Anna Schneider
Johnson Space Center, Houston
281-483-5111
anna.c.schneider@nasa.gov
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Last Updated Jul 11, 2025 LocationNASA Headquarters Related Terms
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By NASA
6 min read
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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2 min read
Polar Tourists Give Positive Reviews to NASA Citizen Science in Antarctica
Citizen science projects result in an overwhelmingly positive impact on the polar tourism experience. That’s according to a new paper analyzing participant experiences in the first two years of FjordPhyto, a NASA Citizen Science project..
The FjordPhyto citizen science project invites travelers onboard expedition cruise vessels to gather data and samples during the polar summer season, helping researchers understand changes in microalgae communities in response to melting glaciers. Travelers in Antarctica from November to March help collect phytoplankton and ocean data from polar regions facilitated by trained expedition guides.
The new research found that ninety-seven percent of respondents reported that participating in citizen science enriched their travel experience. The paper provides a first understanding of the impact of citizen science projects on the tourism experience.
“I was worried that I would feel guilty being a tourist in a place as remote and untouched as Antarctica,” said one anonymous FjordPhyto participant. “But being able to learn and be a part of citizen science, whilst constantly being reminded of our environmental responsibilities, made me feel less like just a visitor and more a part of keeping the science culture that Antarctica is known for alive and well.”
For more information and to sign up, visit the FjordPhyto website.
Travelers in Antarctica participate in collecting phytoplankton and ocean data from polar regions facilitated by trained expedition guides. Credit: Mathew Farrell courtesy of Robert Gilmore Share
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Last Updated Jul 09, 2025 Related Terms
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