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By NASA
An artist’s concept design of NASA’s Lunar Terrain Vehicle.Credit: NASA NASA has selected three instruments to travel to the Moon, with two planned for integration onto an LTV (Lunar Terrain Vehicle) and one for a future orbital opportunity.
The LTV is part of NASA’s efforts to explore the lunar surface as part of the Artemis campaign and is the first crew-driven vehicle to operate on the Moon in more than 50 years. Designed to hold up to two astronauts, as well as operate remotely without a crew, this surface vehicle will enable NASA to achieve more of its science and exploration goals over a wide swath of lunar terrain.
“The Artemis Lunar Terrain Vehicle will transport humanity farther than ever before across the lunar frontier on an epic journey of scientific exploration and discovery,” said Nicky Fox, associate administrator, Science Mission Directorate at NASA Headquarters in Washington. “By combining the best of human and robotic exploration, the science instruments selected for the LTV will make discoveries that inform us about Earth’s nearest neighbor as well as benefit the health and safety of our astronauts and spacecraft on the Moon.”
The Artemis Infrared Reflectance and Emission Spectrometer (AIRES) will identify, quantify, and map lunar minerals and volatiles, which are materials that evaporate easily, like water, ammonia, or carbon dioxide. The instrument will capture spectral data overlaid on visible light images of both specific features of interest and broad panoramas to discover the distribution of minerals and volatiles across the Moon’s south polar region. The AIRES instrument team is led by Phil Christensen from Arizona State University in Tempe.
The Lunar Microwave Active-Passive Spectrometer (L-MAPS) will help define what is below the Moon’s surface and search for possible locations of ice. Containing both a spectrometer and a ground-penetrating radar, the instrument suite will measure temperature, density, and subsurface structures to more than 131 feet (40 meters) below the surface. The L-MAPS instrument team is led by Matthew Siegler from the University of Hawaii at Manoa.
When combined, the data from the two instruments will paint a picture of the components of the lunar surface and subsurface to support human exploration and will uncover clues to the history of rocky worlds in our solar system. The instruments also will help scientists characterize the Moon’s resources, including what the Moon is made of, potential locations of ice, and how the Moon changes over time.
In addition to the instruments selected for integration onto the LTV, NASA also selected the Ultra-Compact Imaging Spectrometer for the Moon (UCIS-Moon) for a future orbital flight opportunity. The instrument will provide regional context to the discoveries made from the LTV. From above, UCIS-Moon will map the Moon’s geology and volatiles and measure how human activity affects those volatiles. The spectrometer also will help identify scientifically valuable areas for astronauts to collect lunar samples, while its wide-view images provide the overall context for where these samples will be collected. The UCIS-Moon instrument will provide the Moon’s highest spatial resolution data of surface lunar water, mineral makeup, and thermophysical properties. The UCIS-Moon instrument team is led by Abigail Fraeman from NASA’s Jet Propulsion Laboratory in Southern California.
“Together, these three scientific instruments will make significant progress in answering key questions about what minerals and volatiles are present on and under the surface of the Moon,” said Joel Kearns, deputy associate administrator for Exploration, Science Mission Directorate at NASA Headquarters. “With these instruments riding on the LTV and in orbit, we will be able to characterize the surface not only where astronauts explore, but also across the south polar region of the Moon, offering exciting opportunities for scientific discovery and exploration for years to come.”
Leading up to these instrument selections, NASA has worked with all three lunar terrain vehicle vendors – Intuitive Machines, Lunar Outpost, and Venturi Astrolab – to complete their preliminary design reviews. This review demonstrates that the initial design of each commercial lunar rover meets all of NASA’s system requirements and shows that the correct design options have been selected, interfaces have been identified, and verification methods have been described. NASA will evaluate the task order proposals received from each LTV vendor and make a selection decision on the demonstration mission by the end of 2025.
Through Artemis, NASA will address high priority science questions, focusing on those that are best accomplished by on-site human explorers on and around the Moon by using robotic surface and orbiting systems. The Artemis missions will send astronauts to explore the Moon for scientific discovery, economic benefits, and build the foundation for the first crewed missions to Mars.
To learn more about Artemis, visit:
https://www.nasa.gov/artemis
-end-
Karen Fox / Molly Wasser
Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
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Last Updated Jul 10, 2025 LocationNASA Headquarters Related Terms
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By NASA
7 min read
NASA’s Parker Solar Probe Snaps Closest-Ever Images to Sun
KEY POINTS
NASA’s Parker Solar Probe has taken the closest ever images to the Sun, captured just 3.8 million miles from the solar surface. The new close-up images show features in the solar wind, the constant stream of electrically charged subatomic particles released by the Sun that rage across the solar system at speeds exceeding 1 million miles an hour. These images, and other data, are helping scientists understand the mysteries of the solar wind, which is essential to understanding its effects at Earth. On its record-breaking pass by the Sun late last year, NASA’s Parker Solar Probe captured stunning new images from within the Sun’s atmosphere. These newly released images — taken closer to the Sun than we’ve ever been before — are helping scientists better understand the Sun’s influence across the solar system, including events that can affect Earth.
“Parker Solar Probe has once again transported us into the dynamic atmosphere of our closest star,” said Nicky Fox, associate administrator, Science Mission Directorate at NASA Headquarters in Washington. “We are witnessing where space weather threats to Earth begin, with our eyes, not just with models. This new data will help us vastly improve our space weather predictions to ensure the safety of our astronauts and the protection of our technology here on Earth and throughout the solar system.”
Parker Solar Probe started its closest approach to the Sun on Dec. 24, 2024, flying just 3.8 million miles from the solar surface. As it skimmed through the Sun’s outer atmosphere, called the corona, in the days around the perihelion, it collected data with an array of scientific instruments, including the Wide-Field Imager for Solar Probe, or WISPR.
Parker Solar Probe has revolutionized our understanding of the solar wind thanks to the spacecraft’s many passes through the Sun’s outer atmosphere.
Credit: NASA’s Goddard Space Flight Center/Joy Ng The new WISPR images reveal the corona and solar wind, a constant stream of electrically charged particles from the Sun that rage across the solar system. The solar wind expands throughout of the solar system with wide-ranging effects. Together with outbursts of material and magnetic currents from the Sun, it helps generate auroras, strip planetary atmospheres, and induce electric currents that can overwhelm power grids and affect communications at Earth. Understanding the impact of solar wind starts with understanding its origins at the Sun.
The WISPR images give scientists a closer look at what happens to the solar wind shortly after it is released from the corona. The images show the important boundary where the Sun’s magnetic field direction switches from northward to southward, called the heliospheric current sheet. It also captures the collision of multiple coronal mass ejections, or CMEs — large outbursts of charged particles that are a key driver of space weather — for the first time in high resolution.
“In these images, we’re seeing the CMEs basically piling up on top of one another,” said Angelos Vourlidas, the WISPR instrument scientist at the Johns Hopkins Applied Physics Laboratory, which designed, built, and operates the spacecraft in Laurel, Maryland. “We’re using this to figure out how the CMEs merge together, which can be important for space weather.”
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This video, made from images taken by Parker Solar Probe’s WISPR instrument during its record-breaking flyby of the Sun on Dec. 25, 2024, shows the solar wind racing out from the Sun’s outer atmosphere, the corona. NASA/Johns Hopkins APL/Naval Research Lab When CMEs collide, their trajectory can change, making it harder to predict where they’ll end up. Their merger can also accelerate charged particles and mix magnetic fields, which makes the CMEs’ effects potentially more dangerous to astronauts and satellites in space and technology on the ground. Parker Solar Probe’s close-up view helps scientists better prepare for such space weather effects at Earth and beyond.
Zooming in on Solar Wind’s Origins
The solar wind was first theorized by preeminent heliophysicist Eugene Parker in 1958. His theories about the solar wind, which were met with criticism at the time, revolutionized how we see our solar system. Prior to Parker Solar Probe’s launch in 2018, NASA and its international partners led missions like Mariner 2, Helios, Ulysses, Wind, and ACE that helped scientists understand the origins of the solar wind — but from a distance. Parker Solar Probe, named in honor of the late scientist, is filling in the gaps of our understanding much closer to the Sun.
At Earth, the solar wind is mostly a consistent breeze, but Parker Solar Probe found it’s anything but at the Sun. When the spacecraft reached within 14.7 million miles from the Sun, it encountered zig-zagging magnetic fields — a feature known as switchbacks. Using Parker Solar Probe’s data, scientists discovered that these switchbacks, which came in clumps, were more common than expected.
When Parker Solar Probe first crossed into the corona about 8 million miles from the Sun’s surface in 2021, it noticed the boundary of the corona was uneven and more complex than previously thought.
As it got even closer, Parker Solar Probe helped scientists pinpoint the origin of switchbacks at patches on the visible surface of the Sun where magnetic funnels form. In 2024 scientists announced that the fast solar wind — one of two main classes of the solar wind — is in part powered by these switchbacks, adding to a 50-year-old mystery.
However, it would take a closer view to understand the slow solar wind, which travels at just 220 miles per second, half the speed of the fast solar wind.
“The big unknown has been: how is the solar wind generated, and how does it manage to escape the Sun’s immense gravitational pull?” said Nour Rawafi, the project scientist for Parker Solar Probe at the Johns Hopkins Applied Physics Laboratory. “Understanding this continuous flow of particles, particularly the slow solar wind, is a major challenge, especially given the diversity in the properties of these streams — but with Parker Solar Probe, we’re closer than ever to uncovering their origins and how they evolve.”
Understanding Slow Solar Wind
The slow solar wind, which is twice as dense and more variable than fast solar wind, is important to study because its interplay with the fast solar wind can create moderately strong solar storm conditions at Earth sometimes rivaling those from CMEs.
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This artist’s concept shows a representative state of Earth’s magnetic bubble immersed in the slow solar wind, which averages some 180 to 300 miles per second. NASA’s Goddard Space Flight Center Conceptual Image Lab Prior to Parker Solar Probe, distant observations suggested there are actually two varieties of slow solar wind, distinguished by the orientation or variability of their magnetic fields. One type of slow solar wind, called Alfvénic, has small-scale switchbacks. The second type, called non-Alfvénic, doesn’t show these variations in its magnetic field.
As it spiraled closer to the Sun, Parker Solar Probe confirmed there are indeed two types. Its close-up views are also helping scientists differentiate the origins of the two types, which scientists believe are unique. The non-Alfvénic wind may come off features called helmet streamers — large loops connecting active regions where some particles can heat up enough to escape — whereas Alfvénic wind might originate near coronal holes, or dark, cool regions in the corona.
In its current orbit, bringing the spacecraft just 3.8 million miles from the Sun, Parker Solar Probe will continue to gather additional data during its upcoming passes through the corona to help scientists confirm the slow solar wind’s origins. The next pass comes Sept. 15, 2025.
“We don’t have a final consensus yet, but we have a whole lot of new intriguing data,” said Adam Szabo, Parker Solar Probe mission scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland.
By Mara Johnson-Groh
NASA’s Goddard Space Flight Center, Greenbelt, Md.
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Last Updated Jul 10, 2025 Related Terms
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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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