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NASA NTRS2010

GPS Software Packages Deliver Positioning Solutions

"To determine a spacecraft s position, the Jet Propulsion Laboratory (JPL) developed an innovative software program called the GPS (global positioning system)-Inferred Positioning System and Orbit Analysis Simulation Software, abbreviated as GIPSY-OASIS, and also developed Real-Time GIPSY (RTG) for certain time-critical applications. First featured in Spinoff 1999, JPL has released hundreds of licenses for GIPSY and RTG, including to Longmont, Colorado-based DigitalGlobe. Using the technology, DigitalGlobe produces satellite imagery with highly precise latitude and longitude coordinates and then supplies it for uses within defense and intelligence, civil agencies, mapping and analysis, environmental monitoring, oil and gas exploration, infrastructure management, Internet portals, and navigation technology."

Source record↗

NASA NTRS2012

Microbial Monitoring of Pathogens by Comparing Multiple Real-Time PCR Platforms for Potential Space Applications

The International Space Station (ISS) is a closed environment wi~h rotations of crew and equipment each introducing their own microbial flora making it necessary to monitor the air, surfaces, and water for microbial contamination. Current microbial monitoring includes labor and time intensive methods to enumerate total bacterial and fungal cells with limited characterization during in-flight testing. Although this culture-based method has been sufficient for monitoring the ISS, future long duration missions will need to perform more comprehensive characterization in-flight, since sample return and ground characterization may not be available. A workshop was held in 2011 at the Johnson Space Center to discuss alternative methodologies and technologies suitable for microbial monitoring for these longterm exploration missions where molecular-based methodologies, such as polymerase chain reaction (PCR), were recommended. In response, a multi-center (Marshall Space Flight Center, Johnson Space Center, Jet Propulsion Laboratory, and Kennedy Space Center) collaborative research effort was initiated to explore novel commercial-off-the-shelf hardware options for spaceflight environmental monitoring. The goal was to evaluate quantitative/semi-quantitative PCR approaches to space applications for low cost in-flight rapid identification of microorganisms affecting crew safety. The initial phase of this project identified commercially available platforms that could be minimally modified to perform nominally in microgravity followed by proof-of-concept testing on the highest qualifying candidates with a universally available test organism, Salmonella enterica. The platforms evaluated during proof-of-concept testing included the iCubate 2.0(TradeMark) (iCubate, Huntsville, AL), RAZOR EX (BioFire Diagnostics; Salt Lake City, Utah) and SmartCycler(TradeMark) (Cepheid; Sunnyvale, CA). The analysis identified two potential technologies (iCubate 2.0 and RAZOR EX) that were able to perform sample-to-answer testing with cell sample concentrations between SO to 400 cells. In addition, the commercial systems were evaluated for initial flight safety and readiness, sample concentration needs were reviewed, and a competitive procurement of commercially available platforms was initiated.

Birmele, Michele↗

NASA NTRS2013

Microbial Monitoring of Common Opportunistic Pathogens by Comparing Multiple Real-Time PCR Platforms for Potential Space Applications

Because the International Space Station is a closed environment with rotations of astronauts and equipment that each introduce their own microbial flora, it is necessary to monitor the air, surfaces, and water for microbial contamination. Current microbial monitoring includes labor- and time-intensive methods to enumerate total bacterial and fungal cells, with limited characterization, during in-flight testing. Although this culture-based method is sufficient for monitoring the International Space Station, on future long-duration missions more detailed characterization will need to be performed during flight, as sample return and ground characterization may not be available. At a workshop held in 2011 at NASA's Johnson Space Center to discuss alternative methodologies and technologies suitable for microbial monitoring for these long-term exploration missions, molecular-based methodologies such as polymerase chain reaction (PCR) were recommended. In response, a multi-center (Marshall Space Flight Center, Johnson Space Center, Jet Propulsion Laboratory, and Kennedy Space Center) collaborative research effort was initiated to explore novel commercial-off-the-shelf hardware options for space flight environmental monitoring. The goal was to evaluate quantitative or semi-quantitative PCR approaches for low-cost in-flight rapid identification of microorganisms that could affect crew safety. The initial phase of this project identified commercially available platforms that could be minimally modified to perform nominally in microgravity. This phase was followed by proof-of-concept testing of the highest qualifying candidates with a universally available challenge organism, Salmonella enterica. The analysis identified two technologies that were able to perform sample-to-answer testing with initial cell sample concentrations between 50 and 400 cells. In addition, the commercial systems were evaluated for initial flight safety and readiness.

Real time↗

NASA NTRS2013

Common Web Mapping and Mobile Device Framework for Display of NASA Real-time Data

Scientists have strategic goals to deliver their unique datasets and research to both collaborative partners and more broadly to the public. These datasets can have a significant impact locally and globally as has been shown by the success of the NASA Short‐term Prediction Research and Transition (SPoRT) Center and SERVIR programs at Marshall Space Flight Center. Each of these respective organizations provides near real‐time data at the best resolution possible to address concerns of the operational weather forecasting community (SPoRT) and to support environmental monitoring and disaster assessment (SERVIR). However, one of the biggest struggles to delivering the data to these and other Earth science community partners is formatting the product to fit into an end user's Decision Support System (DSS). The problem of delivering the data to the end‐user's DSS can be a significant impediment to transitioning research to operational environments especially for disaster response where the deliver time is critical. The decision makers, in addition to the DSS, need seamless access to these same datasets from a web browser or a mobile phone for support when they are away from their DSS or for personnel out in the field. A framework has been developed for MSFC Earth Science program that can be used to easily enable seamless delivery of scientific data to end users in multiple formats. The first format is an open geospatial format, Web Mapping Service (WMS), which is easily integrated into most DSSs. The second format is a web browser display, which can be embedded within any MSFC Science web page with just a few lines of web page coding. The third format is accessible in the form of iOS and Android native mobile applications that could be downloaded from an "app store". The framework developed has reduced the level of effort needed to bring new and existing NASA datasets to each of these end user platforms and help extend the reach of science data.

Burks, Jason E.↗

NASA NTRS2013

Common Web Mapping and Mobile Device Framework for Display of NASA Real-time Data

Burks, Jason↗

NASA NTRS2014

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

With support from a NASA's Applied Sciences Program, The Short‐term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real‐time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real‐time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Pre‐event imagery provided by the NASA funded Web‐Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

McGrath, Kevin M.↗

NASA NTRS2015

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud‐computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high‐performance computing environments, supporting multi‐node systems required for near real‐time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on‐demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on‐demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre‐compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF‐EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher‐resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short‐term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity‐building applications that extend numerical weather prediction to developing countries are intended to provide near real‐time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather‐related hazards, or impacts that local weather events may have during the recovery phase.

Molthan, Andrew↗

NASA NTRS2015

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

NASA NTRS2015

Inflight Microbial Monitoring- An Alternative Method to Culture Based Detection Currently Used on the International Space Station

Previous research has shown that potentially destructive microorganisms and human pathogens have been detected on the International Space Station (ISS). The likelihood of introducing new microorganisms occurs with every exchange of crew or addition of equipment or supplies. Microorganisms introduced to the ISS are readily transferred between crew and subsystems (i.e. ECLSS, environmental control and life support systems). Current microbial characterization methods require enrichment of microorganisms and at least a 48-hour incubation time. This increases the microbial load while detecting only a limited number of the total microorganisms. The culture based method detects approximately 1-10% of the total organisms present and provides no identification. To identify and enumerate ISS microbes requires that samples be returned to Earth for complete analysis. Therefore, a more expedient, low-cost, in-flight method of microbial detection, identification, and enumeration is warranted. The RAZOR EX, a ruggedized, commercial off the shelf, real-time PCR field instrument was tested for its ability to detect microorganisms at low concentrations within one hour. Escherichia coli, Salmonella enterica Typhimurium, and Pseudomonas aeruginosa were detected at low levels using real-time DNA amplification. Total heterotrophic counts could also be detected using a 16S gene marker that can identify up to 98% of all bacteria. To reflect viable cells found in the samples, RNA was also detectable using a modified, single-step reverse transcription reaction.

Inflight Microbial↗

NASA NTRS2015

In-Flight Microbial Monitor

Previous research has shown that potential human pathogens have been detected on the International Space Station (ISS). New microorganisms are introduced with every exchange of crew and cargo. Microorganisms introduced to the ISS are readily transferred between crew and subsystems (i.e., ECLSS, environmental control and life support systems). Current microbial characterization methods require a culture-based enrichment of microorganisms and at least a 48-hour incubation time. This increases the microbial load while detecting only a limited number of microorganisms. The culture-based method detects approximately 1-10% of the total organisms present and provides no identification. To identify and enumerate ISS samples requires that the microbes be returned to Earth for complete analysis. Therefore, a more expedient, low-cost, inflight method of microbial detection, identification, and enumeration is needed. The RAZOR EX, a ruggedized, commercial off the shelf, real-time PCR field instrument was tested for its ability to detect microorganisms at low concentrations within one hour. Escherichia coli, Salmonella enterica Typhimurium, and Pseudomonas aeruginosa were detected at low levels using real-time DNA amplification. Total heterotrophic counts could also be detected using a 16S gene marker that can identify up to 98% of all bacteria. To reflect viable cells found in the samples, RNA was also detectable using a modified, single-step reverse transcription reaction.

Technology Portfolio System↗

NASA NTRS2015

Inflight Microbial Monitoring-An Alternative Method to Culture Based Detection Currently Used on International Space Station

Previous research has shown that microorganisms and potential human pathogens have been detected on the International Space Station (ISS). The potential to introduce new microorganisms occurs with every exchange of crew or addition of equipment or supplies. Previous research has shown that microorganisms introduced to the ISS are readily transferred between crew and subsystems and back (i.e. ECLSS, environmental control and life support systems). Current microbial characterization methods require enrichment of microorganisms and a 48-hour incubation time. This increases the microbial load while detecting a limited number of microorganisms. The culture based method detects approximately 1-10% of the total organisms present and provides no identification, To identify and enumerate ISS samples requires that samples to be returned to Earth for complete analysis. Therefore, a more expedient, low-cost, in-flight method of microbial detection, identification, and enumeration is warranted. The RAZOR EX, a ruggedized, commercial off the shelf, real-time PCR field instrument was tested for its ability to detect microorganism at low concentrations within one hour. Escherichia coli, Salmonella enterica Typhimurium, and Pseudomonas aeruginosa were detected at low levels using real-time DNA amplification. Total heterotrophic counts could also be detected using a 16S gene marker that can identify up to 98% of all bacteria. To reflect viable cells found in the samples, RNA was also detectable using a modified, single-step reverse transcription reaction.

Inflight Microbial↗

NASA NTRS2015

On-Orbit Quantitative Real-Time Gene Expression Analysis Using the Wetlab-2 System

NASA Ames Research Center's WetLab-2 Project enables on-orbit quantitative Reverse Transcriptase PCR (qRT-PCR) analysis without the need for sample return. The WetLab-2 system is capable of processing sample types ranging from microbial cultures to animal tissues dissected on-orbit. The project developed a RNA preparation module that can lyse cells and extract RNA of sufficient quality and quantity for use as templates in qRT-PCR reactions. Our protocol has the advantage of using non-toxic chemicals and does not require alcohols or other organics. The resulting RNA is dispensed into reaction tubes that contain all lyophilized reagents needed to perform qRT-PCR reactions. System operations require simple and limited crew actions including syringe pushes, valve turns and pipette dispenses. The project selected the Cepheid SmartCycler (TradeMark), a Commercial-Off-The-Shelf (COTS) qRT-PCR unit, because of its advantages including rugged modular design, low power consumption, rapid thermal ramp times and four-color multiplex detection. Single tube multiplex assays can be used to normalize for RNA concentration and integrity, and to study multiple genes of interest in each module. The WetLab-2 system can downlink data from the ISS to the ground after a completed run and uplink new thermal cycling programs. The ability to conduct qRT-PCR and generate results on-orbit is an important step towards utilizing the ISS as a National Laboratory facility. Specifically, the ability to get on-orbit data will provide investigators with the opportunity to adjust experimental parameters in real time without the need for sample return and re-flight. On orbit gene expression analysis can also eliminate the confounding effects on gene expression of reentry stresses and shock acting on live cells and organisms or the concern of RNA degradation of fixed samples and provide on-orbit gene expression benchmarking prior to sample return. Finally, the system can also be used for analysis of air, surface, water, and clinical samples to monitor environmental pathogens and crew health. The validation flight of the WetLab-2 system using E. coli bacteria and mouse liver launched on SpaceX-7 in June 2015 and will remain on the ISS National Laboratory.

ISS tools↗

NASA NTRS2015

WetLab-2: Providing Quantitative PCR Capabilities on ISS

The objective of NASA Ames Research Centers WetLab-2 Project is to place on the ISS a system capable of conducting gene expression analysis via quantitative real-time PCR (qRT-PCR) of biological specimens sampled or cultured on orbit. The WetLab-2 system is capable of processing sample types ranging from microbial cultures to animal tissues dissected on-orbit. The project has developed a RNA preparation module that can lyse cells and extract RNA of sufficient quality and quantity for use as templates in qRT-PCR reactions. Our protocol has the advantage that it uses non-toxic chemicals, alcohols or other organics. The resulting RNA is transferred into a pipette and then dispensed into reaction tubes that contain all lyophilized reagents needed to perform qRT-PCR reactions. These reaction tubes are mounted on rotors to centrifuge the liquid to the reaction window of the tube using a cordless drill. System operations require simple and limited crew actions including syringe pushes, valve turns and pipette dispenses. The resulting process takes less than 30 min to have tubes ready for loading into the qRT-PCR unit.The project has selected a Commercial-Off-The-Shelf (COTS) qRT-PCR unit, the Cepheid SmartCycler, that will fly in its COTS configuration. The SmartCycler has a number of advantages including modular design (16 independent PCR modules), low power consumption, rapid thermal ramp times and four-color detection. The ability to detect up to four fluorescent channels will enable multiplex assays that can be used to normalize for RNA concentration and integrity, and to study multiple genes of interest in each module. The WetLab-2 system will have the capability to downlink data from the ISS to the ground after a completed run and to uplink new programs. The ability to conduct qRT-PCR on-orbit eliminates the confounding effects on gene expression of reentry stresses and shock acting on live cells and organisms or the concern of RNA degradation of fixed samples. The system can be used to validate terrestrial analyses of samples returned from ISS by providing on-orbit gene expression benchmarking prior to sample return. The ability to get on-orbit data will provide investigators with the opportunity to adjust experimental parameters in real time for subsequent trials, without the need for sample return and re-flight to sample multigenerational changes. The system can also be used for analysis of air, surface, water, and clinical samples to monitor environmental contaminants and crew health. The verification flight of the instrument is scheduled to launch on SpaceX-7 in June 2015. The WetLab-2 Project is supported by NASAs ISS Program at JSC, Code OZ.

ISS tools↗

NASA NTRS2017

Successful Validation of RNA Purification and Quantitative Real-Time PCR Analysis of Gene Expression on the International Space Station

The NASA Ames WetLab-2 system was developed to offer new on-orbit gene expression analysis capabilities to ISS researchers and can be used to conduct on-orbit RNA isolation and quantitative real time PCR (RT-qPCR) analysis of gene expression from a wide range of biological samples ranging from microbes to mammalian tissues. On orbit validation included three quantitative PCR (qPCR) runs using an E. coli genomic DNA template pre-loaded at three different concentrations. The flight Ct values for the DNA standards showed no statistically significant differences relative to ground controls although there was increased noise in Ct curves, likely due to microgravity-related bubble retention in the optical windows. RNA was successfully purified from both E. coli and mouse liver samples and successfully generated singleplex, duplex and triplex data although with higher standard deviations than ground controls, also likely due to bubbles. Using volunteer science activities, a potential bubble reduction strategy was tested and resulted in smooth amplification curves and tighter Cts between replicates. The WetLab-2 validation experiment demonstrates a novel molecular biology workbench on ISS which allows scientists to purify and stabilize RNA, and to conduct RT-qPCR analyses on-orbit with rapid results. This novel ability is an important step towards utilizing ISS as a National Laboratory facility with the capability to conduct and adjust science experiments in real time without sample return, and opens new possibilities for rapid medical diagnostics and biological environmental monitoring on ISS.

microgravity↗

NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗

NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗

NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗

NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗