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Sols 4236-4238: One More Time… for Contact Science at Mammoth Lakes
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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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Curiosity Navigation Curiosity Home Mission Overview Where is Curiosity? Mission Updates Science Overview Instruments Highlights Exploration Goals News and Features Multimedia Curiosity Raw Images Images Videos Audio Mosaics More Resources Mars Missions Mars Sample Return Mars Perseverance Rover Mars Curiosity Rover MAVEN Mars Reconnaissance Orbiter Mars Odyssey More Mars Missions Mars Home 4 min read
Curiosity Blog, Sols 4593-4594: Three Layers and a Lot of Structure at Volcán Peña Blanca
NASA’s Mars rover Curiosity used its Mast Camera (Mastcam) to acquire this image showing a part of Volcán Peña Blanca from about 10 meters away (about 33 feet). It is already possible to see the different layers and make out that some of them are parallel, while others are at an angle. Curiosity acquired this image on July 6, 2025 — Sol 4591, or Martian day 4,591 of the Mars Science Laboratory mission — at 10:13:13 UTC. NASA/JPL-Caltech/MSSS Written by Susanne P. Schwenzer, Professor of Planetary Mineralogy at The Open University, UK
Earth planning date: Monday, July 7, 2025
A few planning sols ago, we spotted a small ridge in the landscape ahead of us. Ridges and structures that are prominently raised above the landscape are our main target along this part of Curiosity’s traverse. There are many hypotheses on how they formed, and water is one of the likely culprits involved. That is because water reacts with the original minerals, moves the compounds around and some precipitate as minerals in the pore spaces, which is called “cement” by sedimentologists, and generally known as one mechanism to make a rock harder. It’s not the only one, so the Curiosity science team is after all the details at this time to assess whether water indeed was responsible for the more resistant nature of the ridges. Spotting one that is so clearly raised prominently above the landscape — and in easy reach of the rover, both from the distance but also from the path that leads up to it — was therefore very exciting. In addition, the fact that we get a side view of the structure as well as a top view adds to the team’s ability to read the geologic record of this area. “Outcrops,” as we call those places, are one of the most important tools for any field geologist, including Curiosity and team!
Therefore, the penultimate drive stopped about 10 meters away (about 33 feet) from the structure to get a good assessment of where exactly to direct the rover (see the blog post by my colleague Abby). You can see an example of the images Curiosity took with its Mast Camera above; if you want to see them all, they are on the raw images page (and by the time you go, there may be even more images that we took in today’s plan.
With all the information from the last parking spot, the rover drivers parked Curiosity in perfect operating distance for all instruments. In direct view of the rover was a part of Volcán Peña Blanca that shows several units; this blogger counts at least three — but I am a mineralogist, not a sedimentologist! I am really looking forward to the chemical data we will get in this plan. My sedimentologist colleagues found the different angles of smaller layers in the three bigger layers especially interesting, and will look at the high-resolution images from the MAHLI instrument very closely.
With all that in front of us, Curiosity has a very full plan. APXS will get two measurements, the target “Parinacota” is on the upper part of the outcrop and we can even clean it from the dust with the brush, aka DRT. MAHLI will get close-up images to see finer structures and maybe even individual grains. The second APXS target, called “Wila Willki,” is located in the middle part of the outcrop and will also be documented by MAHLI. The third activity of MAHLI will be a so-called dog’s-eye view of the outcrop. For this, the arm reaches very low down to align MAHLI to directly face the outcrop, to get a view of the structures and even a peek underneath some of the protruding ledges. The team is excitedly anticipating the arrival of those images. Stay tuned; you can also find them in the raw images section as soon as we have them!
ChemCam is joining in with two LIBS targets — the target “Pichu Pichu” is on the upper part of the outcrop, and the target “Tacume” is on the middle part. After this much of close up looks, ChemCam is pointing the RMI to the mid-field to look at another of the raised features in more detail and into the far distance to see the upper contact of the boxwork unit with the next unit above it. Mastcam will first join the close up looks and take a large mosaic to document all the details of Volcán Peña Blanca, and to document the LIBS targets, before looking into the distance at two places where we see small troughs around exposed bedrock.
Of course, there are also atmospheric observations in the plan; it’s aphelion cloud season and dust is always of interest. The latter is regularly monitored by atmosphere opacity experiments, and we keep searching for dust devils to understand where, how and why they form and how they move. Curiosity will be busy, and we are very much looking forward to understanding this interesting feature, which is one piece of the puzzle to understand this area we call the boxwork area.
For more Curiosity blog posts, visit MSL Mission Updates
Learn more about Curiosity’s science instruments
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Last Updated Jul 10, 2025 Related Terms
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Last Updated Jul 10, 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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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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