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288 records · Page 16

Opinion: Aerosol Remote Sensing Over the Next Twenty Years

More than two decades ago, aerosol remote sensing underwent a revolution with the launch of the Terra and Aqua satellites. Advancement continued via additional launches carrying new passive and active sensors. Capable of retrieving parameters characterizing aerosol loading, rudimentary particle properties and in some cases aerosol layer height, the satellite view of Earth’s aerosol system came into focus.The modeling communities have made similar advances. Now the efforts have continued long enough that we can see developing trends in both remote sensing and modeling communities, allowing us to speculate about the future and how the community will approach aerosol remote sensing twenty years from now. We anticipate technology that will replace today’s standard multi-wavelength radiometers with hyperspectral and/or polarimetry all viewing in multiple angles.These will be supported by advanced active sensors with the ability to measure profiles of aerosol extinction in addition to backscatter. The result will be greater insight into aerosol particle properties. Algorithms will move from being primarily physically-based to include an increasing degree of Machine Learning methods, but physically-based techniques will not go extinct. However, the practice of applying algorithms to a single sensor will be in decline. Retrieval algorithms will encompass multiple sensors and all available ground measurements into a unifying framework, and these inverted products will be ingested directly into assimilation systems, becoming “cyborgs”: half observations, half model. In twenty years we will see a true democratization in space with nations large and small, private organizations and commercial entities of all sizes launching space sensors. With this increasing amount of data and aerosol products available, there will be a lot of bad data. User communities will organize to set standards and the large national space agencies will lead the effort to maintain quality by deploying and maintaining validation ground networks and focused field experiments. Through it all, interest will remain high in the global aerosol system and how that system affects climate, clouds, precipitation and dynamics, air quality, the environment and public health, transport of pathogens and fertilization of ecosystems, and how these processes are adapting to a changing climate.

aerosol remote sensing↗

Initial Results from the Micro-pulse Lidar Network (MPL-Net)

The micro-pulse lidar system (MPL) was developed in the early 1990s and was the first small, eye-safe, and autonomous lidar built for full time monitoring of cloud and aerosol vertical distributions. In 2000, a new project using MPL systems was started at NASA Goddard Space Flight Center. This new project, the Micro-pulse Lidar Network or MPL-Net, was created to provide long-term observations of aerosol and cloud vertical profiles at key sites around the world. This is accomplished using both NASA operated sites and partnerships with other organizations owning MPL systems. The MPL-Net sites are co-located with NASA AERONET sunphotometers to provide aerosol optical depth data needed for calibration of the MPL. In addition to the long-term sites, MPL-Net provides lidar support for a limited number of field experiments and ocean cruises each year. We will present an overview of the MPL-Net project and show initial results from the first two MPL-Net sites at the South Pole and at Goddard Space Flight Center. Observations of dust layers transported from the Gobi desert, across the Pacific Ocean, to the east coast of the United States will also be shown. MPL-Net affiliated instruments were in place at the desert source region in China, on a research vessel in the Sea of Japan, at ARM sites in Alaska and Oklahoma, and finally at our home site in Maryland (GSFC) during the massive dust storms that occurred in April 2001. The MPL observations of dust layers at each location are shown in comparison to dust layers predicted using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport model (GOCART). Finally, the MPL-Net project is the primary ground-validation program for the Geo-Science Laser Altimeter System (GLAS) satellite lidar project (launch date 2002). We will present an overview demonstrating how MPL-Net results are used to help prepare the GLAS data processing algorithms and assist in the calibration/validation of the GLAS data products.

Welton, Ellsworth J.↗

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

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.

Neha Khadka↗

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗

New Data Source for Studying and Modelling the Topside Ionosphere

The existing uncertainties about density profiles in the topside ionosphere, i.e., in the height regime from hmF2 to approx. 2000 km, requires the search for new data sources. Millions of ionograms had been recorded by the ISIS and Alouette satellites in the sixties and seventies, that never were analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. This paper shows how the digital ionograms are processed and the electron density profiles (from satellite orbit altitude, 1400 km for ISIS-2, down to the F peak) are calculated. The most difficult part of the task is the automatic scaling of the echo traces in the ISIS ionograms. Unlike the ionograms from modern ionosondes, the ISIS ionograms do not identify the wave polarization of the different echo traces, so physical logic must be applied to identify the ordinary ()) and extraordinary (X) traces, and this is not always successful. Characteristic resonance features seen in the topside ionograms occur at the gyro and plasma frequencies. An elaborate scheme was developed to identify these resonance frequencies in order to determine the local plasma and gyrofrequencies. This information helps in the identification of the O and X traces, and it provides the starting density of the electron density profile. The inversion of the echo traces into electron density profiles uses the same modified Chebyshev polynomial fitting technique that is successfully applied in the ground-based Digisonde network. The automatic topside ionogram scaler with true height algorithm TOPIST is successfully scaling approx. 70% of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The home page for the ISIS project is at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. It provides access to as of January 2001, 3000,000 digitized ISIS ionogram data and to related software. A search page lets users select data location, time, and a host of other search criteria. The automated processing of the ISIS ionograms will begin later this year and the electron density profiles will be made available from the project home page. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.

Huang, Xue-Qin↗

Intelligent Observation Strategies for Geosynchronous Remote Sensing for Natural Hazards

Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked thing with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a users smart phone as any other smart appliance.

On Development of Three-Dimensional Visualization Capabilities in Glenn Research Center Communication Analysis Suite

With NASA’s upcoming mission to return to the Moon sustainably by 2024 and using that success as a means to step onto the barren world of Mars, it remains more important than ever to conduct research and planning as thoroughly and efficiently as possible. In a mission as complex as landing humans onto another celestial body, a network of orbiting satellites and ground stations must accurately and reliably communicate with each other, enabling crucial data communications throughout the mission. Visualizing this important data communication increases the understanding of the data and can accelerate analyses efforts. The purpose of this software development is to create an interactive visualization with data taken MATLAB® scripts in the GRC Communication Analysis Suite that is easy to understand, can show all necessary data, and display the data accurately. The main types of data to visualize are from the State Propagation, Line of Sight and Dynamic Link Margin scripts. These all show positions and orbits of satellites and ground stations, while the Line of Sight data shows when they have the ability to communicate with each other based on their respective antenna positions and fields of view. Additionally, the Dynamic Link Margin mode color-codes the communication link performance onto the Line of Sight access lines. Visualization requires a graphics language that is easily accessible, has the needed features, and able to easily read data produced by the GRC Communication Analysis Suite MATLAB® scripts. ThreeJS, a graphics library for Web Graphics Library, coded in JavaScript was selected for the visualization. The next part of the software development was to move the data from MATLAB® to the JavaScript. The best way to accomplish this was to implement a MATLAB® function converting the output data of the scripts to a JavaScript Object Notation file. A key part of the development was creating the visualization within JavaScript and ThreeJS to visualize any combination of planets, moons, orbits, satellites, ground stations, line of sight links, and handle future features without changing major parts of the code. The current visualization capability runs directly from MATLAB®, and can dynamically create any scene. This software development currently supports the lunar communications analysis underway by NASA, and can be easily expanded upon in the future to aid any analysis requirements to help plan current and future space missions.

Visualization↗

Modeling and Simulation of Phased Array Antennas to Support Next-Generation Satellite Design

Developing enhanced simulation capabilities has become a significant priority for the Space Communications and Navigation (SCaN) project at NASA as new space communications technologies are proposed to replace aging NASA communications assets, such as the Tracking and Data Relay Satellite System (TDRSS). When developing the architecture for these new space communications assets, it is important to develop updated modeling and simulation methodologies, such that competing architectures can be weighed against one another and the optimal path forward can be determined. There have been many simulation tools developed here at NASA for the simulation of single RF link budgets, or for the modeling and simulation of an entire network of spacecraft and their supporting SCaN network elements. However, the modeling capabilities are never fully complete and as new technologies are proposed, gaps are identified. One such gap is the ability to rapidly develop high fidelity simulation models of electronically steerable phased array systems. As future relay satellite architectures are proposed that include optical communications links, electronically steerable antennas will become more desirable due to the reduction in platform vibration introduced by mechanically steerable devices. In this research, we investigate how modeling of these antennas can be introduced into out overall simulation and modeling structure. The ultimate goal of this research is two-fold. First, to enable NASA engineers to model various proposed simulation architectures and determine which proposed architecture meets the given architectural requirements. Second, given a set of communications link requirements for a proposed satellite architecture, determine the optimal configuration for a phased array antenna. There is a variety of tools available that can be used to model phased array antennas. To meet our stated goals, the first objective of this research is to compare the subset of tools available to us, trading-off modeling fidelity of the tool with simulation performance. When comparing several proposed architectures, higher- fidelity modeling may be desirable, however, when iterating a proposed set of communication link requirements across ranges of phased array configuration parameters, the practicality of performance becomes a significant requirement. In either case, a minimum simulation - fidelity must be met, regardless of performance considerations, which will be discussed in this research. Given a suitable set of phased array modeling tools, this research then focuses on integration with current SCaN modeling and simulation tools. While properly modeling the antenna elements of a system are vital, this is only a small part of the end-to-end communication path between a satellite and the supporting ground station and/or relay satellite assets. To properly model a proposed simulation architecture, this toolset must be integrated with other commercial and government development tools, such that the overall architecture can be examined in terms of communications, reliability, and cost. In this research, integration with previously developed communication tools is investigated.

Phased Array↗

CHESS 2025: Location data for field observations and sampling

This dataset represents geolocation data associated with field observations and sampling from the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Location data were collected using Trimble DA2 Global Navigation Satellite System (GNSS) receivers with Trimble Catalyst 2 centimeter (cm) positioning service and the Environmental Systems Research Institute (Esri) Field Maps mobile app. Files in this data package include meadow site polygons, shrub site polygons, tree site polygons and stem point locations, and Leaf Area Index (LAI) plot polygons (.geojson). The geojson files can be opened with open-source GIS software (e.g, QGIS). A csv file is also provided with point coordinates for all locations. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

Current Trends in Satellite Laser Ranging

Satellite Laser Ranging (SLR) techniques are used to accurately measure the distance from ground stations to retroreflectors on satellites and the moon. SLR is one of the fundamental techniques that define the international Terrestrial Reference Frame (iTRF), which is the basis upon which we measure many aspects of global change over space, time, and evolving technology. It is one of the fundamental techniques that define at a level of precision of a few mm the origin and scale of the ITRF. Laser Ranging provides precision orbit determination and instrument calibration/validation for satellite-borne altimeters for the better understanding of sea level change, ocean dynamics, ice budget, and terrestrial topography. Laser ranging is also a tool to study the dynamics of the Moon and fundamental constants. Many of the GNSS satellites now carry retro-reflectors for improved orbit determination, harmonization of reference frames, and in-orbit co-location and system performance validation. The GNSS Constellations will be the means of making the reference frame available to worldwide users. Data and products from these measurements support key aspects of the GEOSS 10-Year implementation Plan adopted on February 16, 2005, The ITRF has been identified as a key contribution of the JAG to GEOSS and the ILRS makes a major contribution for its development since its foundation. The ILRS delivers weekly additional realizations that are accumulated sequentially to extend the ITRF and the Earth Orientation Parameter (EOP) series with a daily resolution. Additional products are currently under development such as precise orbits of satellites, EOP with daily availability, low-degree gravitational harmonics for studies of Earth dynamics and kinematics, etc. SLR technology continues to evolve toward the next generation laser ranging systems as programmatic requirements become more stringent. Ranging accuracy is improving as higher repetition rate, narrower pulse lasers and faster detectors are implemented. Automation and pass interleaving at some stations is already expanding temporal coverage. Web-based safety keys are allowing the SLR network stations to range to optically vulnerable satellites. Some stations are experimenting with two-wavelength operation as a means of better understanding the atmospheric refraction and with very low power laser to improve eye-safety conditions. New retroreflector designs are improving the signal link and enable daylight ranging. Dramatic improvements have also been made with lunar ranging with the new APOLLO Site in New ?Mexico, USA and the upgraded lunar station "MEO" in Grasse,

Pearlman, M. R.↗

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

A Suite of High-Resolution Atmospheric Carbon Dioxide Simulations in Support of the OCO-3 Snapshot Area Mapping (SAM) Mode Observation: PSU-WRF, CSU-OLAM and NASA GEOS

Cities and power plants are responsible more than 70% of GHG emissions. The emissions from the subnational localized sources need to be accurately quantified and properly managed in order to achieve the Paris Climate Agreement goals. The accurate estimation of these emission is also crucial for assessing the capacity of natural sinks to uptake the carbon released into the atmosphere that ultimately defines our emission allowance for the 1.5 or 2.0 degree temperature goals. New data collected by the Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Mapping (SAM) observations should provide a tremendous new opportunity for us to study CO2 emissions from targeted large localized sources, such as cities, power plants and beyond. Since 2018 (prior to the OCO-3 launch), we have studied the observational strategies for the SAM mode observation in order to collect the useful data for estimating CO2 emissions from target sources. To maximize the benefit of the SAM mode observation data for quantifying CO2 emission, it is important to define how to observe the localized sources depending on the local environmental and emission specificities. We employ a suite of state-of-the-art CO2 modeling systems, such as PSU's WRF-CO2, CSU's OLAM and NASA's GEOS models. All of these CO2 modeling systems are prescribedwith the high-resolution fuel CO2 emission estimates from the ODIAC data product to achieve realistic urban CO2 variations. We focus on cities with established ground-based observation networks, such as Los Angeles, Indianapolis, and Paris. We have examined the urban emission signal detectability in response to the influence of local background conditions that can observed by the SAM and biospheric contributions that will be a new challenge for urban emission inverse estimation. Based on the results of our simulation experiments, we plan to propose city-specific observation strategies. Upon the availability of the OCO-3 data, we will attempt to estimate city emissions using inverse models. We also developed synthetic OCO-3 data using NASA's GEOS5 model, which should be useful to assess the utility of the OCO-3 data in combination with data collected by carbon satellites in other orbits, such as NASA's OCO-2 and Japanese GOSAT-1/2. The synthetic data also provide an opportunity to study the errors due to clouds and aerosols, which have been not fully studied in the past.

Oda, Tomohiro↗

Duplex Direct Data Distribution System

The NASA Glenn Research Center (GRC) is developing and demonstrating communications and network technologies that are helping to enable the near-Earth space Internet. GRC envisions several service categories. The first of these categories is direct data distribution or D3 (pronounced "D-cubed"). Commercially provided D3 will make it possible to download a data set from a spacecraft, like the International Space Station. as easily as one can extract a file from a remote server today, using a file transfer protocol. In a second category, NASA spacecraft will make use of commercial satellite communication (SATCOM) systems. Some of those services will come from purchasing time on unused transponders that cover landmasses. While it is likely there will be gaps in service coverage, Internet services should be available using these systems. This report addresses alternative methods of implementing a full duplex enhancement of the GRC developed experimental Ka-Band Direct Data Distribution (D3) space-to-ground communication link. The resulting duplex version is called the Duplex Direct Data Distribution (D4) system. The D4 system is intended to provide high-data-rate commercial direct or internet-based communications service between the NASA spacecraft in low earth orbit (LEO) and the respective principal investigators associated with these spacecraft. Candidate commercial services were assessed regarding their near-term potential to meet NASA requirements. Candidates included Ka-band and V-band geostationary orbit and non-geostationary orbit satellite relay services and direct downlink ("LEO teleport") services. End-to-end systems concepts were examined and characterized in terms of alternative link layer architectures. Alternatives included a Direct Link, a Relay Link, a Hybrid Link, and a Dual Mode Link. The direct link assessment examined sample ground terminal placements and antenna angle issues. The SATCOM-based alternatives examined existing or proposed commercial SATCOM services that could be available in the 2005 time frame. The alternatives were evaluated and compared in terms of average daily system throughput and cost per bit. Throughput was estimated based on hypothetical scenarios supporting the International Space Station and polar orbiting missions. The feasibility of using standard TCP and a modified TCP was evaluated and risks were identified. An estimate of the TCP acknowledgment data rate required to support a return channel rate of 622 Mbps was developed using OPNET.

Greenfield, Israel↗

Radar Observations of Convective Systems from a High-Altitude Aircraft

Reflectivity data collected by the precipitation radar on board the tropical Rainfall Measuring Mission (TRMM) satellite, orbiting at 350 km altitude, are compared to reflectivity data collected nearly simultaneously by a doppler radar aboard the NASA ER-2 flying at 19-20 km altitude, i.e. above even the deepest convection. The TRMM precipitation radar is a scanning device with a ground swath width of 215 km, and has a resolution of about a4.4 km in the horizontal and 250 m in the vertical (125 m in the core swath 48 km wide). The TRMM radar has a wavelength of 217 cm (13.8 GHz) and the Nadir mirror echo below the surface is used to correct reflectivity for loss by attenuation. The ER-2 Doppler radar (EDOP) has two antennas, one pointing to the nadir, 34 degrees forward. The forward pointing beam receives both the normal and the cross-polarized echos, so the linear polarization ratio field can be monitored. EDOP has a wavelength of 3.12 cm (9.6 GHz), a vertical resolution of 37.5 m and a horizontal along-track resolution of about 100 m. The 2-D along track airflow field can be synthesized from the radial velocities of both beams, if a reflectivity-based hydrometer fall speed relation can be assumed. It is primarily the superb vertical resolution that distinguishes EDOP from other ground-based or airborne radars. Two experiments were conducted during 1998 into validate TRMM reflectivity data over convection and convectively-generated stratiform precipitation regions. The Teflun-A (TEXAS-Florida Underflight) experiment, was conducted in April and May and focused on mesoscale convective systems mainly in southeast Texas. TEFLUN-B was conducted in August-September in central Florida, in coordination with CAMEX-3 (Convection and Moisture Experiment). The latter was focused on hurricanes, especially during landfall, whereas TEFLUN-B concentrated on central; Florida convection, which is largely driven and organized by surface heating and ensuing sea breeze circulations. Both TEFLUN-A and B were amply supported by surface data, in particular a dense raingauge network, a polarization radar, wind profilers, a mobile radiosonde system, a cloud physics aircraft penetrating the overflown storms, and a network of 10 cm Doppler radars(WSR-88D). This presentation will show some preliminary comparisons between TRMM, EDOP, and WSR-88D reflectivity fields in the case of an MCS, a hurricane, and less organized convection in central Florida. A validation of TRMM reflectivity is important, because TRMM's primary objective is to estimate the rainfall climatology with 35 degrees of the equator. Rainfall is estimated from the radar reflectivity, as well from TRMM's Microwave Imager, which measures at 10.7, 19.4, 21.3, 37, and 85.5 GHz over a broader swath (78 km). While the experiments lasted about three months the cumulative period of near simultaneous observations of storms by ground-based, airborne and space borne radars is only about an hour long. Therefore the comparison is case-study-based, not climatological. We will highlight fundamental differences in the typical reflectivity profiles in stratiform regions of MCS's, Florida convection and hurricanes and will explain why Z-R relationships based on ground-based radar data for convective systems over land should be different from those for hurricanes. These catastrophically intense rainfall from hurricane Georges in Hispaniola and from Mitch in Honduras highlights the importance of accurate Z-R relationships, It will be shown that a Z-R relationship that uses the entire reflectivity profile (rather than just a 1 level) works much better in a variety of cases, making an adjustment of the constants for different precipitation system categories redundant.

Heymsfield, G.↗

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗

The NASA Soil Moisture Active Passive (SMAP) Mission - Algorithm and Cal/Val Activities and Synergies with SMOS and Other L-Band Missions

NASA's Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has as its key measurement objective the frequent, global mapping of near-surface soil moisture and its freeze-thaw state. SMAP soil moisture and freeze/thaw measurements at 10 km and 3 km resolutions respectively, would enable significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. Soil moisture control of these fluxes is a key factor in the performance of atmospheric models used for weather forecasts and climate projections Soil moisture measurements are also of great importance in assessing floods and for monitoring drought. In addition, observations of soil moisture and freeze/thaw timing over the boreal latitudes can help reduce uncertainties in quantifying the global carbon balance. The SMAP measurement concept utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The SMAP radiometer and radar flight hardware and ground processing designs are incorporating approaches to identify and mitigate potential terrestrial radio frequency interference (RFI). The radar and radiometer instruments are planned to operate in a 680 km polar orbit, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments would yield global maps of soil moisture and freeze/thaw state to be provided at 10 km and 3 km resolutions respectively, every two to three days. Plans are to provide also a radiometer-only soil moisture product at 40-km spatial resolution. This product and the underlying brightness temperatures have characteristics similar to those provided by the Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are unique opportunities for common data product development and continuity between the two missions. SMAP also has commonalities with other satellite missions having L-band radiometer and/or radar sensors applicable to soil moisture measurement, such as Aquarius, SAO COM, and ALOS-2. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. The algorithms are developed and evaluated in the SDS Testbed using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including SMOS. The SMAP project is developing a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level I) and derived geophysical products (Level 2 and higher). In this presentation we report on the development status of the SMAP data product algorithms, and the planning and implementation of the SMAP Cal/Val program. Several components of the SMAP algorithm development and Cal/Val plans have commonality with those of SMOS, and for this reason there are shared activities and resources that can be utilized between the missions, including in situ networks, ancillary data sets, and long-term monitoring sites.

Njoku, Eni↗

Climatology of Global Precipitation Measurement Mission Precipitation Regimes and Implications for Global Estimates of Vertical Winds

The Global Precipitation Measurement (GPM) mission Validation Network (VN) framework leverages over 118 ground-based polarimetric Doppler radars to validate a large subset of precipitation measurements and retrievals from the GPM Dual-frequency Precipitation Radar (DPR). Recently, GPM DPR reflectivity profiles within the VN have been classified according to their convective regime using unsupervised machine learning techniques. The archetypal regimes are stratiform, convective, mixed stratiform-convective (e.g., transition regions), and “other” (e.g., peripheral regions of light precipitation). Subcategories within these four primary regimes vary according to the characteristic depth of included reflectivity profiles, resulting in 12 main GPM DPR precipitation profile categories. Polarimetry of ground-based Doppler radars in the VN offers additional insights into the types of precipitation, while pairs of radars positioned near each other enable retrieval of vertical winds via dual-Doppler analysis. Geometrically matched to the DPR reflectivity profiles in the GPM VN, these ground-based data and retrievals contribute more detailed characterization of the distinct kinematic and microphysical structures associated with each of the 12 DPR precipitation regimes. DPR reflectivity profiles linked with wind in the VN are restricted to GPM overpasses of proximal radar pairs that allow dual-Doppler analysis. Although a limited subset of DPR profiles in the VN are matched with vertical motion, agreement between the reflectivity structures paired with wind data and those of the greater DPR dataset in the VN suggest that estimates of vertical motion may be inferred in regions without ground-based measurements. We present a climatology of the 12 convective regimes identified within the DPR VN dataset as well as early efforts to estimate the kinematic and microphysical structures of precipitation profiles within the greater GPM DPR dataset by applying machine learning techniques. Precipitation data paired with global estimates of vertical winds from these efforts offer early insight to and support upcoming missions to retrieve convective mass flux, including the Investigation of Convective Updrafts (INCUS) in the Tropics and the global Atmosphere Observing System (AOS).

Precipitation↗

Satellite Broadcast of Graphical Weather Data Flight Tested

NASA Glenn Research Center at Lewis Field's aviation Weather Information Communications (WINCOMM) and NASA Langley Research Center's Aviation Weather Information (AWIN) programs collaborated in a flight test and evaluation of a worldwide weather data-link capability using satellites. This successful flight testing moves NASA closer to its goal of developing advanced communications and information technologies to enable high-quality and timely dissemination of aviation weather information to all relevant users on the aviation information network. Recognized as a major contributing factor in aviation accidents and incidents, weather contributes directly or indirectly to nearly 80 percent of fatal general aviation (small private aircraft) accidents. In 1997, the Aeronautics Safety Investment Strategy Team s weather team produced a prioritized list of investment areas under weather accident prevention. Weather data dissemination is the most critical and highest ranked priority on the list. NASA's Aviation Safety Program founded the Aviation Weather Information initiative to focus efforts on significantly reducing the number of weather-related aviation fatalities. Access to accurate and timely weather data could contribute to a major reduction of weather-related incidents and accidents. However, a cost-effective solution has eluded most general aviation pilots because of the high cost of onboard weather radar equipment. Rockwell Collins, through a contract with NASA and in cooperation with WorldSpace Corporation, successfully completed ground and flight testing of a receiver and antenna in Johannesburg, South Africa. This NASA/Rockwell Collins project is an evaluation of worldwide weather data-link capability using transmissions from the Satellite Digital Audio Radio Services (S DARS) AfriStar satellite. Owned and operated by WorldSpace, AfriStar is a geostationary satellite that broadcasts commercial digital audio services to stationary and mobile platforms. S DARS satellites are the most powerful communications satellites produced to date, allowing users to receive signals using simple, low-cost patch antennas instead of more expensive, beam-steered antenna arrays. Engineers connected an inexpensive, commercially available radio receiver to a laptop computer and an antenna designed and built by Rockwell Collins, enabling them to receive WorldSpace signals from the AfriStar satellite during flight tests. WorldSpace broadcast their composite color graphical weather data files, which were multiplexed with normal audio streams, to the flat patch antenna mounted on a single-engine aircraft. The aircraft was equipped with a modified commercial S-DARS receiver, a Global Positioning Satellite (GPS) receiver, and a laptop computer with color display. Continuous data reception occurred during normal aircraft maneuvers performed throughout takeoff, cruise, and landing operations. In addition, engineers monitored receiver power levels during steep turns and banks. In most instances, the receiver was able to maintain acceptable power levels during all phases of flight and to obtain weather data with little or with the successful completion of ground and flight testing of a receiver and antenna in Johannesburg, South Africa, the team has started to prepare for experiments using highspeed aircraft in areas of the world with limited access to timely weather data. NASA plans to provide a more advanced antenna design and consultation support. This successful test of real-time aviation-related weather data is a positive step toward solving communications-specific issues associated with the dissemination of weather data directly to the cockpit.

Mallasch, Paul G.↗