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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) Aeroshell Performance

On November 10, 2022, NASA launched the Low-earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) reentry vehicle (RV)as a secondary payload mounted inside the launch vehicle (LV) adaptor on an Atlas V 401 out of the Vandenburg Space Force Base (VSFB). The primary payload, Joint Polar Satellite System-2 (JPSS-2), was delivered successfully to a sun-synchronous trajectory shortly after launch, at which point the Centaur upper stage performed a burn to de-orbit the system. Once on the desired trajectory to enter the atmosphere, the top of the payload adaptor was ejected to expose the LOFTID RV. Next, the LOFTID aeroshell, a Hypersonic Inflatable Aerodynamic Decelerator (HIAD), was deployed and inflated without issue. The Centaur pointed the LOFTID RV to the desired attitude to enter the atmosphere, spun the assembly to three rpm, separated the LOFTID RV, and finally performed a divert maneuver to prevent the Centaur from re-contacting the LOFTID RV after atmospheric entry. The LOFTID RV maintained the design attitude and spin rate from separation to atmospheric entry, a duration of roughly 30 minutes, demonstrating the aeroshell did not experience any energy damping from inflatable structure flexibility. The LOFTID RV entered the atmosphere over Alaska at >8km/sec and decelerated as designed, demonstrating aerodynamically stable flight from hypersonic entry through subsonic parachute deployment. On-board visible-spectrum cameras captured the physical response of the heatshield through all phases of flight. Co-located infrared cameras captured the temperature distribution of the aftside of the heat shield anchored to a distribution of thermocouples on the inflatable structure (IS) in the field of view. Thermocouples were also embedded in the forward side of the aeroshell both in the Flexible Thermal Protection System (FTPS) as well as the IS. This paper will document aeroshell’s response to the atmospheric entry. The visible-spectrum cameras captured mechanical response of the deployable aeroshell to the loads encountered in the different phases of entry. Events seen on the visible-spectrum cameras correlate with the measurements of the load cell pins on webbing elements that attach the aeroshell to the centerbody structure. Thermocouple data captured verifies the aerothermal response of the aeroshell was in-kind with pre-flight analysis predictions although somewhat lower in most locations. This data will enable enhancement of HIAD predictive capability. Post-flight inspection of the aeroshell after splashdown and ocean recovery shows that all the aeroshell materials and manufacturing techniques developed over the past two decades of HIAD technology development performed as expected at large scale, a scale which cannot be replicated in ground test facilities. The LOFTID RV remained high in the water after touching down under parachute, and maintained buoyancy and integrity for the hour it took to position the recovery vessel near the vehicle after splashdown. There was no indication the HIAD would have experienced an issue maintaining buoyancy for many more hours after water impact as the component maintained adequate inflation pressure more than 24 hours after water extraction with no additional inflation gas in tanks, as the inflation gas remaining in the tank was purposely vented prior to splashdown.

HIAD↗
NASA NTRSDate not supplied

Reconfigurable Metasurface Optics at NASA LaRC Towards Space Image Sensing

Reconfigurable optical metasurfaces are rapidly emerging as a major frontier in photonics research, development, and applications. They promise compact, lightweight, and energy-efficient reconfigurable optical systems with unprecedented performance and functions that can be dynamically defined on-demand. Space applications represent an emerging area in which these characteristics are highly prized. The ability to dynamically tune optical functions through selective modulation of electromagnetic waves is crucial to the advancement of a variety of sensing applications, from imaging spectrometers to light detection and ranging (LiDAR). This presentation introduces a reconfigurable metasurface optic project led by a research team at NASA Langley Research Center (NASA LaRC) since 2019. It covers advances in phase change material-based reconfigurable optics, performance data on reliability enhancement of photonic devices, image sensing system architectures, and mission concepts enabled through these advances.

metasurface↗
NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗
NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗
NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗
NASA NTRS2000

Subsetting and Formatting Landsat-7 LOR ETM+ and Data Products

The Landsat-7 Processing System (LPS) processes Landsat-7 Enhanced Thematic Mapper (ETM+) instrument data into large, contiguous segments called "subintervals" and stores them in Level OR (LOR) data files. The LPS processed subinterval products must be subsetted and reformatted before the Level I processing systems can ingest them. The initial full subintervals produced by the LPS are stored mainly in HDF Earth Observing System (HDF-EOS) format which is an extension to the Hierarchical Data Format (HDF). The final LOR products are stored in native HDF format. Primarily the EOS Core System (ECS) and alternately the DAAC Emergency System (DES) subset the subinterval data for the operational Landsat-7 data processing systems. The HDF and HDF-EOS application programming interfaces (APIs) can be used for extensive data subsetting and data reorganization. A stand-alone subsetter tool has been developed which is based on some of the DES code. This tool makes use of the HDF and HDFEOS APIs to perform Landsat-7 LOR product subsetting and demonstrates how HDF and HDFEOS can be used for creating various configurations of full LOR products. How these APIs can be used to efficiently subset, format, and organize Landsat-7 LOR data as demonstrated by the subsetter tool and the DES is discussed.

Reid, Michael R.↗
NASA NTRS1983

The effects of solar incidence angle over digital processing of LANDSAT data

A technique to extract the topography modulation component from digital data is described. The enhancement process is based on the fact that the pixel contains two types of information: (1) reflectance variation due to the target; (2) reflectance variation due to the topography. In order to enhance the signal variation due to topography, the technique recommends the extraction from original LANDSAT data of the component resulting from target reflectance. Considering that the role of topographic modulation over the pixel information will vary with solar incidence angle, the results of this technique of digital processing will differ from one season to another, mainly in highly dissected topography. In this context, the effects of solar incidence angle over the topographic modulation technique were evaluated. Two sets of MSS/LANDSAT data, with solar elevation angles varying from 22 to 41 deg were selected to implement the digital processing at the Image-100 System. A secondary watershed (Rio Bocaina) draining into Rio Paraiba do Sul (Sao Paulo State) was selected as a test site. The results showed that the technique used was more appropriate to MSS data acquired under higher Sun elevation angles. Topographic modulation components applied to low Sun elevation angles lessens rather than enhances topography.

Parada, N. D. J.↗
NASA NTRS1994

Gravity model improvement using the DORIS tracking system on the SPOT 2 satellite

A high-precision radiometric satellite tracking system, Doppler Orbitography and Radio-positioning Integrated by Satellite system (DORIS), has recently been developed by the French space agency, Centre National d'Etudes Spatiales (CNES). DORIS was designed to provide tracking support for missions such as the joint United States/French TOPEX/Poseidon. As part of the flight testing process, a DORIS package was flown on the French SPOT 2 satellite. A substantial quantity of geodetic quality tracking data was obtained on SPOT 2 from an extensive international DORIS tracking network. These data were analyzed to assess their accuracy and to evaluate the gravitational modeling enhancements provided by these data in combination with the Goddard Earth Model-T3 (GEM-T3) gravitational model. These observations have noise levels of 0.4 to 0.5 mm/s, with few residual systematic effects. Although the SPOT 2 satellite experiences high atmospheric drag forces, the precision and global coverage of the DORIS tracking data have enabled more extensive orbit parameterization to mitigate these effects. As a result, the SPOT 2 orbital errors have been reduced to an estimated radial accuracy in the 10-20 cm RMS range. The addition of these data, which encompass many regions heretofore lacking in precision satellite tracking, has significantly improved GEM-T3 and allowed greatly improved orbit accuracies for Sun-synchronous satellites like SPOT 2 (such as ERS 1 and EOS). Comparison of the ensuing gravity model with other contemporary fields (GRIM-4C2, TEG2B, and OSU91A) provides a means to assess the current state of knowledge of the Earth's gravity field. Thus, the DORIS experiment on SPOT 2 has provided a strong basis for evaluating this new orbit tracking technology and has demonstrated the important contribution of the DORIS network to the success of the TOPEX/Poseidon mission.

Nerem, R. S.↗
NASA NTRS2011

Bit of History and Some Lessons Learned in Using NASA Remote Sensing Data in Public Health Applications

The NASA Applied Sciences Program's public health initiative began in 2004 to illustratethe potential benefits for using remote sensing in public health applications. Objectives/Purpose: The CDC initiated a st udy with NASA through the National Center for Environmental Health (NCEH) to establish a pilot effort to use remote sensing data as part of its Environmental Public Health Tracking Network (EPHTN). As a consequence, the NCEH and NASA developed a project called HELIX-Atlanta (Health and Environment Linkage for Information Exchange) to demonstrate a process for developing a local environmental public health tracking and surveillance network that integrates non-infectious health and environment systems for the Atlanta metropolitan area. Methods: As an ongo ing, systematic integration, analysis and interpretation of data, an EPHTN focuses on: 1 -- environmental hazards; 2 -- human exposure to environmental hazards; and 3 -- health effects potentially related to exposure to environmental hazards. To satisfy the definition of a surveillance system the data must be disseminated to plan, implement, and evaluate environmental public health action. Results: A close working r elationship developed with NCEH where information was exchanged to assist in the development of an EPHTN that incorporated NASA remote sensing data into a surveillance network for disseminating public health tracking information to users. This project?s success provided NASA with the opportunity to work with other public health entities such as the University of Mississippi Medical Center, the University of New Mexico and the University of Arizona. Conclusions: HELIX-Atlanta became a functioning part of the national EPHTN for tracking environmental hazards and exposure, particularly as related to air quality over Atlanta. Learning Objectives: 1 -- remote sensing data can be integral to an EPHTN; 2 -- public tracking objectives can be enhanced through remote sensing data; 3 -- NASA's involvement in public health applications can have wider benefits in the future.

Quattrochi, Dale A.↗
NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗
NASA NTRS2022

Research Campaign: Advanced Manufacturing in Space: Properties, Structure and Simulations to Pioneer New Applications

This topic underpins synthesis of, and in many cases manufacturing of, value-added materials via liquid phase processing. Materials and processes range from cast metals and optical glasses through exploration-relevant technologies such as additive manufacturing, casting and in-situ resource utilization to support human space exploration. A recurring theme is the need for accurate thermophysical property and atomic structure data for relevant materials and the exploitation of reduced convection/sedimentation in microgravity to enhance the quality of data and to develop new and transformative technologies for materials processing.

Michael SanSoucie↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRS2020

Assimilating GCOM-W1 AMSR2 and TRMM TMI Radiance Data in GEOS Analysis and Reanalysis

The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) observed the Earth in lower latitudes between 1997 - 2015. Its conical-scan radiometer has nine channels and measured microwave radiances between 10 and 89 GHz. These data provide information on atmospheric temperature, humidity, clouds, precipitation, as well as sea surface temperature. Radiance data from other microwave radiometers such as Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) onboard various Defense Meteorological Satellite Program (DMSP) satellites are assimilated in clear-sky conditions in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. The GMAO's Hybrid 4D-EnVar-based Atmospheric Data Assimilation System (ADAS) is enhanced with an all-sky microwave radiance data assimilation capability in the real-time GEOS-Forward Processing (FP) system. Currently, the FP system assimilates Global Precipitation Measurement (GPM) microwave imager (GMI) radiance data utilizing this all-sky capability, and is being extended to use more all-sky data from other microwave radiometers. In this presentation, we will focus on impacts of all-sky TMI radiance data on GEOS analyses of atmospheric moisture, precipitation and other fields, and discuss their applications for future GEOS reanalyses.

Jin, Jianjun↗
NASA NTRS1985

Enhanced rock discrimination using Landsat-5 Thematic Mapper (TM) data

The application of TM data to rock discrimination is discussed. Sixteen specific terrains derived from geologic maps are examined on TM images of the Arabian shield obtained on Apr. 14, 1984; visual enhancement procedures are applied to the images. The rock types observed in the test site are described; the major sedimentary formations in the test area are laterite and sandstone. The data reveal that the layered rocks in the outcrop consist of a variety of metamorphosed volcanics, metamorphosed sediments, and amphibolite, and the intrusive complex is composed of several classes of mafic and acidic rocks.

Blodget, H. W.↗
NASA NTRS1982

Automation of vibroacoustic data bank for random vibration criteria development

A computerized data bank system was developed for utilization of large amounts of vibration and acoustic data to formulate component random vibration design and test criteria. This system consists of a computer, graphics tablet, and a dry-silver hard copier which are all desk-top type hardware and occupy minimal space. The data bank contains data from the Saturn V and Titan III flight and static test programs. The vibration and acoustic data are stored in the form of power spectral density and one-third octave band plots over the frequency range from 20 to 2000 Hz. The data was stored by digitizing each spectral plot by tracing with the graphics tablet. The digitized data was statistically analyzed and the resulting 97.5% probability levels were stored on tape along with the appropriate structural parameters. Standard extrapolation procedures were programmed for prediction of component random vibration test criteria for new launch vehicle and payload configurations. This automated vibroacoustic data bank system greatly enhances the speed and accuracy of formulating vibration test criteria. In the future, the data bank will be expanded to include all data acquired from the space shuttle flight test program.

Ferebee, R. C.↗
NASA NTRS2003

Recent Enhancements to the National Transonic Facility

The National Transonic Facility continues to make enhancements to provide quality data in a safe, efficient and cost effective method for aerodynamic ground testing. Recent enhancements discussed in this paper include the restoration of reliability and improved performance of the heat exchanger systems resulting in the expansion of the NTF air operations envelope. Additionally, results are presented from a continued effort to reduce model dynamics through the use of a new stiffer balance and sting

Kilgore, W. A.↗
NASA NTRS2003

Recent Enhancements to the National Transonic Facility

The National Transonic Facility continues to make enhancements to provide quality data in a safe, efficient and cost effective method for aerodynamic ground testing. Recent enhancements discussed in this paper include the restoration of reliability and improved performance of the heat exchanger systems resulting in the expansion of the NTF air operations envelope. Additionally, results are presented from a continued effort to reduce model dynamics through the use of a new stiffer balance and sting.

Kilgore, W. A.↗