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By NASA
4 min read
Expanded AI Model with Global Data Enhances Earth Science Applications
On June 22, 2013, the Operational Land Imager (OLI) on Landsat 8 captured this false-color image of the East Peak fire burning in southern Colorado near Trinidad. Burned areas appear dark red, while actively burning areas look orange. Dark green areas are forests; light green areas are grasslands. Data from Landsat 8 were used to train the Prithvi artificial intelligence model, which can help detect burn scars. NASA Earth Observatory NASA, IBM, and Forschungszentrum Jülich have released an expanded version of the open-source Prithvi Geospatial artificial intelligence (AI) foundation model to support a broader range of geographical applications. Now, with the inclusion of global data, the foundation model can support tracking changes in land use, monitoring disasters, and predicting crop yields worldwide.
The Prithvi Geospatial foundation model, first released in August 2023 by NASA and IBM, is pre-trained on NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset and learns by filling in masked information. The model is available on Hugging Face, a data science platform where machine learning developers openly build, train, deploy, and share models. Because NASA releases data, products, and research in the open, businesses and commercial entities can take these models and transform them into marketable products and services that generate economic value.
“We’re excited about the downstream applications that are made possible with the addition of global HLS data to the Prithvi Geospatial foundation model. We’ve embedded NASA’s scientific expertise directly into these foundation models, enabling them to quickly translate petabytes of data into actionable insights,” said Kevin Murphy, NASA chief science data officer. “It’s like having a powerful assistant that leverages NASA’s knowledge to help make faster, more informed decisions, leading to economic and societal benefits.”
AI foundation models are pre-trained on large datasets with self-supervised learning techniques, providing flexible base models that can be fine-tuned for domain-specific downstream tasks.
Crop classification prediction generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. Focusing on diverse land use and ecosystems, researchers selected HLS satellite images that represented various landscapes while avoiding lower-quality data caused by clouds or gaps. Urban areas were emphasized to ensure better coverage, and strict quality controls were applied to create a large, well-balanced dataset. The final dataset is significantly larger than previous versions, offering improved global representation and reliability for environmental analysis. These methods created a robust and representative dataset, ideal for reliable model training and analysis.
The Prithvi Geospatial foundation model has already proven valuable in several applications, including post-disaster flood mapping and detecting burn scars caused by fires.
One application, the Multi-Temporal Cloud Gap Imputation, leverages the foundation model to reconstruct the gaps in satellite imagery caused by cloud cover, enabling a clearer view of Earth’s surface over time. This approach supports a variety of applications, including environmental monitoring and agricultural planning.
Another application, Multi-Temporal Crop Segmentation, uses satellite imagery to classify and map different crop types and land cover across the United States. By analyzing time-sequenced data and layering U.S. Department of Agriculture’s Crop Data, Prithvi Geospatial can accurately identify crop patterns, which in turn could improve agricultural monitoring and resource management on a large scale.
The flood mapping dataset can classify flood water and permanent water across diverse biomes and ecosystems, supporting flood management by training models to detect surface water.
Wildfire scar mapping combines satellite imagery with wildfire data to capture detailed views of wildfire scars shortly after fires occurred. This approach provides valuable data for training models to map fire-affected areas, aiding in wildfire management and recovery efforts.
Burn scar mapping generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. This model has also been tested with additional downstream applications including estimation of gross primary productivity, above ground biomass estimation, landslide detection, and burn intensity estimations.
“The updates to this Prithvi Geospatial model have been driven by valuable feedback from users of the initial version,” said Rahul Ramachandran, AI foundation model for science lead and senior data science strategist at NASA’s Marshall Space Flight Center in Huntsville, Alabama. “This enhanced model has also undergone rigorous testing across a broader range of downstream use cases, ensuring improved versatility and performance, resulting in a version of the model that will empower diverse environmental monitoring applications, delivering significant societal benefits.”
The Prithvi Geospatial Foundation Model was developed as part of an initiative of NASA’s Office of the Chief Science Data Officer to unlock the value of NASA’s vast collection of science data using AI. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT), based at Marshall, IBM Research, and the Jülich Supercomputing Centre, Forschungszentrum, Jülich, designed the foundation model on the supercomputer Jülich Wizard for European Leadership Science (JUWELS), operated by Jülich Supercomputing Centre. This collaboration was facilitated by IEEE Geoscience and Remote Sensing Society.
For more information about NASA’s strategy of developing foundation models for science, visit https://science.nasa.gov/artificial-intelligence-science.
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Last Updated Dec 04, 2024 Related Terms
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By NASA
4 min read
NASA Satellites Reveal Abrupt Drop in Global Freshwater Levels
Earth (ESD) Earth Home Explore Climate Change Science in Action Multimedia Data For Researchers GRACE satellites measure gravity as they orbit the planet to reveal shifting levels of water on the Earth (artist’s concept). NASA/JPL-Caltech An international team of scientists using observations from NASA-German satellites found evidence that Earth’s total amount of freshwater dropped abruptly starting in May 2014 and has remained low ever since. Reporting in Surveys in Geophysics, the researchers suggested the shift could indicate Earth’s continents have entered a persistently drier phase.
From 2015 through 2023, satellite measurements showed that the average amount of freshwater stored on land — that includes liquid surface water like lakes and rivers, plus water in aquifers underground — was 290 cubic miles (1,200 cubic km) lower than the average levels from 2002 through 2014, said Matthew Rodell, one of the study authors and a hydrologist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “That’s two and a half times the volume of Lake Erie lost.”
During times of drought, along with the modern expansion of irrigated agriculture, farms and cities must rely more heavily on groundwater, which can lead to a cycle of declining underground water supplies: freshwater supplies become depleted, rain and snow fail to replenish them, and more groundwater is pumped. The reduction in available water puts a strain on farmers and communities, potentially leading to famine, conflicts, poverty, and an increased risk of disease when people turn to contaminated water sources, according to a UN report on water stress published in 2024.
The team of researchers identified this abrupt, global decrease in freshwater using observations from the Gravity Recovery and Climate Experiment (GRACE) satellites, operated by the German Aerospace Center, German Research Centre for Geosciences, and NASA. GRACE satellites measure fluctuations in Earth’s gravity on monthly scales that reveal changes in the mass of water on and under the ground. The original GRACE satellites flew from March 2002 to October 2017. The successor GRACE–Follow On (GRACE–FO) satellites launched in May 2018.
This map shows the years that terrestrial water storage hit a 22-year minimum (i.e., the land was driest) at each location, based on data from the GRACE and GRACE/FO satellites. A significantly large portion of the global land surface reached this minimum in the nine years since 2015, which happen to be the nine warmest years in the modern temperature record. Image by NASA Earth Observatory/Wanmei Liang with data courtesy of Mary Michael O’Neill The decline in global freshwater reported in the study began with a massive drought in northern and central Brazil, and was followed shortly by a series of major droughts in Australasia, South America, North America, Europe, and Africa. Warmer ocean temperatures in the tropical Pacific from late 2014 into 2016, culminating in one of the most significant El Niño events since 1950, led to shifts in atmospheric jet streams that altered weather and rainfall patterns around the world. However, even after El Niño subsided, global freshwater failed to rebound. In fact, Rodell and team report that 13 of the world’s 30 most intense droughts observed by GRACE occurred since January 2015. Rodell and colleagues suspect that global warming might be contributing to the enduring freshwater depletion.
Global warming leads the atmosphere to hold more water vapor, which results in more extreme precipitation, said NASA Goddard meteorologist Michael Bosilovich. While total annual rain and snowfall levels may not change dramatically, long periods between intense precipitation events allow the soil to dry and become more compact. That decreases the amount of water the ground can absorb when it does rain.
“The problem when you have extreme precipitation,” Bosilovich said, “is the water ends up running off,” instead of soaking in and replenishing groundwater stores. Globally, freshwater levels have stayed consistently low since the 2014-2016 El Niño, while more water remains trapped in the atmosphere as water vapor. “Warming temperatures increase both the evaporation of water from the surface to the atmosphere, and the water-holding capacity of the atmosphere, increasing the frequency and intensity of drought conditions,” he noted.
While there are reasons to suspect that the abrupt drop in freshwater is largely due to global warming, it can be difficult to definitively link the two, said Susanna Werth, a hydrologist and remote sensing scientist at Virginia Tech, who was not affiliated with the study. “There are uncertainties in climate predictions,” Werth said. “Measurements and models always come with errors.”
It remains to be seen whether global freshwater will rebound to pre-2015 values, hold steady, or resume its decline. Considering that the nine warmest years in the modern temperature record coincided with the abrupt freshwater decline, Rodell said, “We don’t think this is a coincidence, and it could be a harbinger of what’s to come.”
By James R. Riordon
NASA’s Earth Science News Team
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Last Updated Nov 15, 2024 Editor James Riordon Contact James Riordon james.r.riordon@nasa.gov Location NASA Goddard Space Flight Center Related Terms
Earth Goddard Space Flight Center GRACE (Gravity Recovery And Climate Experiment) GRACE-FO (Gravity Recovery and Climate Experiment Follow-on) Water on Earth Explore More
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