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Information is given in viewgraph form on the Earth Observing System (EOS) nickel hydrogen battery. Information is given on the life evaluation test, cell characteristics, acceptance and characterization tests, and the battery system description.
Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.
This document summarizes the experimental concepts and preliminary analyses (as of December 1975) for each of the 28 scientific experiments conducted during the Apollo-Soyuz Test Project from July 15 to 24, 1975. The scientific topics are X- ray and extreme ultraviolet astronomy, solar astronomy, gamma- ray detectors, Earth studies (including the upper atmosphere, meteorological phenomena, hydrology, oceanography, geology, desert studies, and gravity field studies), microbiology, heavy cosmic particle interaction with live cells, vestibular system studies, and materials processing (including high-temperature and ambient-temperature processing of industrial materials and electrophoretic processing of biological materials) .
Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.
Assigning the material species to each asteroid spectral type and finding out the corresponding meteorite category is crucial to make the global material map in the whole asteroid belt and to understand the evolution of the asteroid belt. Recent direct observations by spacecrafts are revealing new intriguing aspects of asteroids which cannot be obtained solely from ground-based observations or meteorite studies. However identification of the real material species constituting asteroids and their corresponding meteorite analogs are still ambiguous. Space weathering makes difficult to identify the true material, and there is still a great gap between the remote sensing data on the global surface and the local microscopic data from meteorites. Sample return from asteroids are inevitable to solve these problems. For this purpose sample return missions to asteroids belonging to various spectral classes are required. The HAYABUSA spacecraft (prelaunch name is MUSESC) launched last year is the first attempt on this concept. This report presents outline of the mission with special stress on its science.
Hyperspectral Earth-reflected solar measurements contain rich, detailed information about clouds, aerosols, and the atmosphere. This information compounds when paired with high, SI-traceable radiometric accuracy. Unlike spectral resolution, which can be resampled to coarser spectral resolutions, radiometric accuracy cannot be recovered once a measurement is made, making the choice to invest in this capability now a decision with lasting consequences for the climate record. CLARREO Pathfinder is demonstrating that this combination of hyperspectral sampling and benchmark-quality accuracy is achievable today, but no follow-on mission is currently planned within NASA's Earth Science Division portfolio, and the recent cancellation of the European Space Agency TRUTHS mission has heightened the urgency of sustaining this capability. We recommend prioritizing investment in a follow-on to CLARREO Pathfinder, along with dedicated support for the science community to fully exploit these measurements. This capability would reduce long-standing uncertainties in cloud feedback, aerosol forcing, and global energy imbalance, while providing a degradation-resistant, gap-resilient calibration foundation for the broader Earth observing system. Building on flight-proven technology and an engaged science community, this is a near-term, fiscally realistic opportunity to secure a foundational Earth observation capability for the coming decade.
Geocentric tracking station coordinates and short-period Earth-orientation variations can be measured with Global Positioning System (GPS) measurements. Unless calibrated, geocentric coordinate errors and changes in Earth orientation can lead to significant deep-space tracking errors. Ground-based GPS estimates of daily and subdaily changes in Earth orientation presently show centimeter-level precision. Comparison between GPS-estimated Earth-rotation variations, which are the differences between Universal Time 1 and Universal Coordinated Time (UT1-UTC), and those calculated from ocean tide models suggests that observed subdaily variations in Earth rotation are dominated by oceanic tidal effects. Preliminary GPS estimates for the geocenter location (from a 3-week experiment) agree with independent satellite laser-ranging estimates to better than 10 cm. Covariance analysis predicts that temporal resolution of GPS estimates for Earth orientation and geocenter improves significantly when data collected from low Earth-orbiting satellites as well as from ground sites are combined. The low Earth GPS tracking data enhance the accuracy and resolution for measuring high-frequency global geodynamical signals over time scales of less than 1 day.
The Aeolus-Earth Mission (DWTS-Helio) is a recently selected NASA Heliophysics investigation that will apply the innovative Doppler Wind Temperature Sounder (DWTS) technique to the Space Atmosphere Interaction Region (SAIR) at ~105-180 km. The technique, invented by Gordley & Marshall (2011), applies the Doppler Scanning with Gas Filter (DSGF) technique using a cryo-cooled MWIR radiometer to view the limb of the atmosphere from LEO (orbital velocity creates the Doppler shifts required by the technique). For this implementation of DWTS, a Nitric Oxide (NO) low pressure gas cell is used. The limb of the atmosphere is observed from a spacecraft at orbital velocity, and as an observed air volume passes through the field of view, this velocity creates a set of Doppler-shifted images. These images are used to create Doppler Integrated Pass (DIP) signals, from which atmospheric temperature and wind profiles are extracted (it may be considered a converse of the successful in-flight calibration of gas cells used for the HALOE instrument). The technique is relative, removing the need for absolute radiances, and the signal-to-noise ratio for this technique is high, resulting in good measurement precision. Different gas cells can be used to create different altitude coverage and may be combined to measure a more continuous altitude range. For example, using a NO cell captures the lower atmosphere from 30-50km as well as the SAIR, while using the 13 isotopologue of CO2 would capture 50-120km. Due to the desired data volume as well as power and thermal considerations, this initial Aeolus-Earth flight will be more suited for a ‘hosted’ payload on an Orbital Maneuvering Vehicle platform instead of the ‘free-flyer’ option originally investigated. Lastly, the technique is sufficiently powerful that a two-channel system could produce full Mars atmosphere data not currently possible.
A passive infrared sensor is described for remote sounding of the wind field in the stratosphere and mesosphere from near Earth orbital spacecraft. The instrument uses gas correlation spectroscopy together with electro-optic phase modulation techniques to measure winds in the 20- to 120-km altitude range globally, both in the day and at night, and with a vertical resolution of better than the atmospheric scale height. Measurement of temperature and the amounts of key atmospheric species may also be made simultaneously and in coincident fields of view with the wind observations. The sensor is currently being developed at the Jet Propulsion Laboratory as a candidate for the upcoming NASA Earth Observation System.
As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.
Atmospheric deposition of micro-nutrients like Fe has been shown to be important for ocean biogeochemistry. The largest source of atmospheric Fe and other elements (e.g., Ca, Al, Si, and Ti) is desert dust, although there are significant non-dust sources in some regions. However, past estimates of these elements have been substantially uncertain due to limited information about the composition of the desert source regions. Here we use elemental distributions estimated from new Earth Surface Mineral Dust Source Investigation (EMIT) observations, which provide mineralogical composition at the surface of the Earth based on imaging spectroscopy measurements from the International Space Station. We add in other sources of these elements (anthropogenic and natural) and compare to a compilation of available surface concentration data from stations over land and from shipborne observations. Our results suggest that the modeled distribution is similar to available observations, but discrepancies still exist in both natural desert dust regions as well as regions dominated by anthropogenic sources. Global budgets for the elements Ca, Al, Fe, Si, and Ti suggest that desert dust remains the dominant source for these elements but anthropogenic or volcanic sources are also important for these elements. Changes in elemental distributions since preindustrial times were also estimated.
The principal known types of lunar rocks are briefly reviewed, and their chemical relationships discussed. In the suite of low-KREEP highland rocks, Fe/(Fe + Mg) in the normative mafic minerals increases and the albite content of normative plagioclase decreases as the total amount of normative plagioclase increases, the opposite of the trend predicted by the Bowen reaction principle. Lunar highland samples analyzed are uniformly distributed in this sequence, in which normative plagioclase contents range from ~ 40 percent to ~ 100 percent. The distribution of compositions of rocks from terrestrial layered mafic intrusives is substantially different: here the analyses fall in several discrete clusters (anorthositic rocks, norites, granophyres and ferrogabbros, ultramafics), and the chemical trends noted above are not reproduced. It is suggested that the observed trends in lunar highland rocks could be produced by crystal fractionation in a deep global surface magma system if (1) plagioclase tended to float, upon crystallization, and (2) the magma was kept agitated and well mixed (probably by thermal convection) until crystallization was far advanced and relatively little residual liquid was left. When such a system was finally immobilized, the Fe-, Na-rich residual liquid would produce Fe-rich mafic minerals in the upper levels of the system, but could not much alter the composition of abundant calcic plagioclase. Conversely, the same liquid would produce sodic plagioclase deep in the sequence, but could not much alter the composition of abundant magnesian mafic minerals. After the crustal system solidified, but before extensive cooling had developed a thick, strong lithosphere, mantle convection was able to draw portions of the lunar anorthositic crust down into the mantle in a manner analogous to the present-day behavior of the terrestrial mantle and crust. At depth, the crustal material was heated; KREEP-rich norite was extracted by partial melting and erupted at the surface as a lava, analogous to terrestrial andesite eruptions.
Near-Earth asteroid 2024 YR 4 was discovered on 2024-12-27 and its probability of Earth impact in December 2032 peaked at about 3% on 2025-02-18. Additional observations ruled out Earth impact by 2025-02-23. However, the probability of lunar impact in December 2032 then rose, reaching about 4% by the end of the apparition in May 2025. James Webb Space Telescope (JWST) observations on 2025-03-26 estimated the asteroid's diameter at 60 +/- 7 m. Studies of 2024 YR 4 's potential lunar impact effects suggest lunar ejecta could increase micrometeoroid debris flux in low Earth orbit up to 1000 times above background levels over just a few days, possibly threatening astronauts and spacecraft. In this work, we present options for space missions to 2024 YR 4 that could be utilized if lunar impact is confirmed. We cover flyby & rendezvous reconnaissance, deflection, and robust disruption of the asteroid. We examine both rapid-response and delayed launch options through 2032. We evaluate chemical and solar electric propulsion, various launch vehicles, optimized deep space maneuvers, and gravity assists. Re-tasking extant spacecraft and using built spacecraft not yet launched are also considered. The best reconnaissance mission options launch in late 2028, leaving only approximately three years for development at the time of this writing in August 2025. Deflection missions were assessed and appear impractical. However, kinetic robust disruption missions are available with launches between April 2030 and April 2032. Nuclear robust disruption missions are also available with launches between late 2029 and late 2031. Finally, even if lunar impact is ruled out there is significant potential utility in deploying a reconnaissance mission to characterize the asteroid.
The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.
In the exploration of destinations outside of Earth's neighborhood, specifically Mars, scientific and engineering inquiries have occurred by two means; observations from satellites and observations by landed spacecraft. Satellite observations (Mariner, MRO, Mars Odyssey, provide global-scale spatial and temporal data while landed spacecraft (Viking, Mars Pathfinder, Spirit, Opportunity, Phoenix Mars Lander) investigate highly localized areas of the surface of the planet. In preparation for human exploration, extensive knowledge of the surface and atmospheric environments should be known before the first human leaves Earth. The primary goal of performing reconnaissance on Mars on a sub-global scale is to know as much as possible about the environment to which crews will be subjected. At the current rate of launching and landing probes to Mars, it will take a very long time to understand the surface and atmospheric conditions associated with the regions where prospective crews may land. Meanwhile electronics and electrical systems are rapidly getting smaller. One can argue that to acquire the knowledge of the region, one must take hundreds, maybe thousands of measurements simultaneously. One means to perform such a task is to deploy a swarm of sensors. Such a swarm would perform an in-situ assessment of the region. Imagine a close flyby mission to Mars for example, where mini- to micro-sensors are deposited into the atmosphere over half an orbit or more. The sensors, captured by the atmospheric drag and Martian gravity slowly descend buffeted about by Martian winds and weather until they settle on the surface a great time later (think of how long dust takes to settle). As they descend they communicate a vast array of data; temperature, chemistry, pressure, radiation dose, electric or magnetic properties from a region of the planet and an individual sensor need not measure the same quantity as its neighbors. Initially, they could move at the whim of the environment but later versions could have locomotion or propulsion mechanisms. Humans wouldn't need to decide where the sensors go, the sensors do that for themselves. This is a key strength of a sensor swarm. The intelligence relies on the group not on a decision maker on earth. Real time sensor inputs direct what the swarm considers most interesting to investigate resulting in emergent behavior. We issued a $20,000 challenge to the global innovators to provide solutions as to how such a swarm could be initialized and by what protocols and methodologies by which they operate. Over 400 innovators from 49 countries took a look at the problem, with three receiving partial awards for solutions.
Clouds have a pronounced influence on the Earth?s climate. Relative to cloud free conditions, they cool the planet by increasing the amount of solar radiation reflected back to space and reducing the amount of sunlight reaching the surface, but they warm the planet by decreasing the amount of thermal infrared radiation escaping to space and increasing the amount reaching the surface (a greenhouse effect). The global mean net cloud radiative effect (CRE) is about -20 W/m2, a cooling effect at both the top-ofatmosphere and surface. Given the magnitude of CRE?s, it is expected that changes in cloud properties could be a significant factor in climate change due to anthropogenic forcing?s, yet cloud feedbacks are not well known and remain one of the largest uncertainties in climate prediction. This paper explores relationships between coincident observations of atmospheric aerosols, clouds and radiation derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and from the Clouds and the Earth?s Radiant Energy System (CERES) instruments on the Aqua satellite. We investigate several interesting regional trends that have emerged in the nearly 18-year satellite record that suggest correlation between changes in low-level cloud properties and changes in aerosol optical depth that may be associated with changes in pollution emissions and possibly with other factors. MERRA reanalysis of meteorological conditions and aerosol particulate species are investigated to help better understand the potential mechanisms responsible for the observed cloud property trends. Finally, we analyze a new CERES flux by cloud type dataset in order to try and isolate the associated trends in low-level cloud radiative effects. It is anticipated that this study using long-term observations of clouds, aerosols and radiative fluxes combined with model reanalysis data will contribute to an improved understanding of cloud climate feedbacks.
Cycle life analysis of the nickel cadmium battery is given. The life prediction equation is limited to spacecraft in low Earth orbits.