In February 2017, the Earth Observing One (EO-1) satellite mission successfully completed sixteen years and three months of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for new orbital sensors. Hyperion has provided the only available global sample of the Earth's surface with: (i) passive optical mid-morning observations at moderate spatial resolution (30 m) to match the Landsat series; and (ii) spectral coverage over almost the full optical spectrum in 10 nm contiguous bands, in visible through shortwave infrared (VSWIR, 0.4-2.5 μm) wavelengths. Consequently, Hyperion is a heritage platform for future full-spectrum VSWIR orbital spectrometers, including the German mission, EnMAP (2019), and the NASA pre-Phase A (yet unscheduled) mission, the Hyperspectral InfraRed Imager (HyspIRI), defined by the 2007 Decadal Survey conducted by the US National Research Council. We provide an overview of the mission's lifetime and Hyperion's scientific and application accomplishments, including calibration & validation activities, data quality evaluations during end of mission precession changes to the orbit and overpass time, and the development of a user-friendly science quality archive.

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu “Wheel” and the analytics used to process hyperspectral data produced daily by NASA’s Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. The resultant products of the analytics are made accessible through an API for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

The GMSEC (Goddard Mission Services Evolution Center) Services Suite (GSS) is a collection of tools and software services along with a robust customizable web-based portal that enables the user to capture, monitor, report, and analyze system-wide GMSEC data. Given our plug-and-play architecture and the needs for rapid system development, we opted to follow the Scrum Agile Methodology for software development. Being one of the first few projects to implement the Agile methodology at NASA GSFC, in this presentation we will present our approaches, tools, successes, and challenges in implementing this methodology. The GMSEC architecture provides a scalable, extensible ground and flight system for existing and future missions. GMSEC comes with a robust Application Programming Interface (GMSEC API) and a core set of Java-based GMSEC components that facilitate the development of a GMSEC-based ground system. Over the past few years, we have seen an upbeat in the number of customers who are moving from a native desktop application environment to a web based environment particularly for data monitoring and analysis. We also see a need to provide separation of the business logic from the GUI display for our Java-based components and also to consolidate all the GUI displays into one interface. This combination of separation and consolidation brings immediate value to a GMSEC-based ground system through increased ease of data access via a uniform interface, built-in security measures, centralized configuration management, and ease of feature extensibility.

Spaceborne spectrometers require spectral-temporal stability characterization to aid in validation of derived data products. Earth Observation 1 (EO-1) began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 pseudoinvariant calibration site (PICS), this resulted in a progressive shift from a mean local equatorial crossing time of ~10:00 A.M. in 2011 to ~8:30 A.M. in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were <;5% in the visible near infrared (VNIR) and <;10% for most of the shortwave infrared (SWIR). Combined coefficient of variation estimates in the VNIR ranged from 0.025 to 0.095, and in the SWIR it ranged from 0.025 to 0.06, excluding bands near atmospheric absorption features. Reflectances produced with different atmospheric models were correlated (R2) in VNIR from 0.25 to 0.94 and in SWIR from 0.12 to 0.88 (p <; 0.01). The uncertainties in all the models increased with a terrain slope up to 15° and selecting dune flats could reduce errors. We conclude that these data remain a valuable resource over this period for sensor intercalibration despite orbital decay.

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The resultant products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

Earth Observing 1 (E0-1) satellite has an imaging spectrometer (hyperspectral) instrument called Hyperion. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days. With slewing of the satellite and allowing for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that is sought is the ability to image natural hazards in a daily time series for space based imaging spectrometers. The Hyperion can not provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats, each with the same imaging spectrometer, positioned strategically in the same orbit, can be used to provide daily coverage, cost-effectively.

This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.

The authors on this paper are team members of the Earth Observing 1 (E0-1) mission which has flown an imaging spectrometer (hyperspectral) instrument called Hyperion for the past 15+ years. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days and with slewing, of the satellite for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that has been sought is the ability to image natural hazards in a daily time series for space-based imaging spectrometers. The Hyperion cannot provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats, each with the same imaging spectrometer, positioned strategically can be used to provide daily coverage or even diurnal coverage, cost-effectively. This paper sought to design a cubesat constellation mission that would accomplish this goal and then to articulate the key tradeoffs.

This presentation lays out the data processing products that exist and are planned for the Matsu cloud for Earth Observing 1. The presentation focuses on a new feature called co-registration of Earth Observing 1 with Landsat Global Land Survey chips.

The method presented in this paper utilizes Global Land Survey (GLS) maps to register Earth Observing-1 (EO-1) data, either using entire scenes or utilizing chips extracted from the GLS maps. The automated registration algorithm is based on the optimization of wavelet or wavelet-like features extracted from both reference and input image data. After testing the method on several ALI scenes, results and conclusions are presented.

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.

The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, in addition to collecting science data from its instruments, the EO-1 mission has been used as a testbed for a variety of technologies which provide various automation capabilities and which have been used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with a messaging architecture and web services. This paper provides an overview of the various technologies that were tested and eventually folded into normal operations. As these technologies were folded in, the nature of operations transformed. The SensorWeb software enables easy connectivity for collaboration with sensors, but the side benefit is that it improved the EO-1 operational efficiency. This paper presents the various phases of EO-1 operation over the past 12 years and also presents operational efficiency gains demonstrated by some metrics.

This research effort prototypes an implementation of a standard interface, Web Coverage Processing Service (WCPS), which is an Open Geospatial Consortium(OGC) standard, to enable users to define, test, upload and execute algorithms for on-orbit sensor systems. The user is able to customize on-orbit data products that result from raw data streaming from an instrument. This extends the SensorWeb 2.0 concept that was developed under a previous Advanced Information System Technology (AIST) effort in which web services wrap sensors and a standardized Extensible Markup Language (XML) based scripting workflow language orchestrates processing steps across multiple domains. SensorWeb 3G extends the concept by providing the user controls into the flight software modules associated with on-orbit sensor and thus provides a degree of flexibility which does not presently exist. The successful demonstrations to date will be presented, which includes a realistic HyspIRI decadal mission testbed. Furthermore, benchmarks that were run will also be presented along with future demonstration and benchmark tests planned. Finally, we conclude with implications for the future and how this concept dovetails into efforts to develop "cloud computing" methods and standards.

Architectural styles impose constraints on both the topology and the interaction behavior of involved parties. In this paper, we propose an approach for analyzing implemented systems based on the publisher subscriber architectural style. From the style definition, we derive a set of reusable questions and show that some of them can be answered statically whereas others are best answered using dynamic analysis. The paper explains how the results of static analysis can be used to orchestrate dynamic analysis. The proposed method was successfully applied on the NASA's Goddard Mission Services Evolution Center (GMSEC) software product line. The results show that the GMSEC has a) a novel reusable vendor-independent middleware abstraction layer that allows the NASA's missions to configure the middleware of interest without changing the publishers' or subscribers' source code, and b) a high-priority bug due to behavioral discrepancies, which were eluded during testing and code reviews, among different implementations of the same APIs for different vendors.

The concept of sensor webs will require a certain amount of underpinning to enable the interaction of the various constituent components. The SWAMO project is providing an intelligent agent based framework for allowing sensor systems to intercommunicate via standard interfaces such as SensorML, Sensor Web Enablement standards, and web service calls. The culmination of the project will allow for the discovery of sensing assets, query capabilities, requesting task feasibilities, and even charge the system with direct tasking. This paper describes the work to date on designing and prototyping this framework.

One of the shared problems for new space mission developers is that it is extremely difficult to infuse new technology into new missions unless that technology has been flight validated. Therefore, the issue is that new technology is required to fly on a successful mission for flight validation. We have been experimenting with new technology on existing satellites by retrofitting primarily the flight software while the missions are on-orbit to experiment with new operations concepts. Experiments have been using Earth Observing 1 (EO-1), which is part of the New Millennium Program at NASA. EO-1 finished its prime mission one year after its launch on November 21,2000. From November 21,2001 until the present, EO-1 has been used in parallel with additional science data gathering to test out various sensor web concepts. Similarly, the Cosmic Hot Interstellar Plasma Spectrometer (CHIPS) satellite was also a one year mission flown by the University of Berkeley, sponsored by NASA and whose prime mission ended August 30,2005. Presently, CHIPS is being used to experiment with a seamless space to ground interface by installing Core Flight System (cFS), a plug-and-play architecture developed by the Flight Software Branch at NASA/GSFC on top of the existing space-to-ground Internet Protocol (IP) interface that CHIPS implemented. For example, one targeted experiment is to connect CHIPS to a rover via this interface and the Internet, and trigger autonomous actions on CHIPS, the rover or both. Thus far, having satellites to experiment with new concepts has turned out to be an inexpensive way to infuse new technology for future missions. Relevant experiences thus far and future plans will be discussed in this presentation.

A viewgraph presentation to evolve sensor web capabilities in pursuit of capabilities to support Global Earth Observing System of Systems (GEOSS) is shown. The topics include: 1) Vision to Enable Sensor Webs with Hot Spots; 2) Vision Extended for Communication/Control Architecture for Missions to Mars; 3) Key Capabilities Implemented to Enable EO-1 Sensor Webs; 4) One of Three Experiments Conducted by UMBC Undergraduate Class 12-14-05 (1 - 3); 5) Closer Look at our Mini-Rovers and Simulated Mars Landscae at GSFC; 6) Beginning to Implement Experiments with Standards-Vision for Integrated Sensor Web Environment; 7) Goddard Mission Services Evolution Center (GMSEC); 8) GMSEC Component Catalog; 9) Core Flight System (CFS) and Extension for GMSEC for Flight SW; 10) Sensor Modeling Language; 11) Seamless Ground to Space Integrated Message Bus Demonstration (completed December 2005); 12) Other Experiments in Queue; 13) Acknowledgements; and 14) References.

Space and Ground Trades for Human Exploration and Wearable Computing Human exploration of the Moon and Mars will present unique trade study challenges as ground system elements shift to planetary bodies and perhaps eventually to the bodies of human explorers in the form of wearable computing technologies. This presentation will highlight some of the key space and ground trade issues that will face the Exploration Initiative as NASA begins designing systems for the sustained human exploration of the Moon and Mars, with an emphasis on wearable computing. We will present some preliminary test results and scenarios that demonstrate how wearable computing might affect the trade space noted below. We will first present some background on wearable computing and its utility to NASA’s Exploration Initiative. Next, we will discuss three broad architectural themes, some key ground and space trade issues within those themes and how they relate to wearable computing. Lastly, we will present some preliminary test results and suggest guidance for proceeding in the assessment and creation of a value-added role for wearable computing in the Exploration Initiative. The three broad ground-space architectural trade themes we will discuss are: 1. Functional Shift and Distribution: To what extent, if any, should traditional ground system functionality be shifted to, and distributed among, the Earth, MoodMars, and the human. explorer? 2: Situational Awareness and Autonomy: How much situational awareness (e.g. environmental conditions, biometrics, etc.) and autonomy is required and desired, and where should these capabilities reside? 3. Functioml Redundancy: What functions (e.g. command, control, analysis) should exist simultaneously on Earth, the MoodMars, and the human explorer? These three themes can serve as the axes of a three-dimensional trade space, within which architectural solutions reside. We wilI show how wearable computers can fit into this trade space and what the possible implications could be for the rest of the ground and space architecture(s). We intend this to be an example of “explorer-centric” thinking in a fully “integrated explorer” paradigm, where “integrated explorer” refers to a human explorer having instant access to all relevant data, knowledge of the environment, science models, health and safety-related events, and other tools and information via wearable computing technologies. The trade study approach will include involvement from the relevant stakeholders (Constellation Systems, CCCI, EVA Project Office, Astronaut office, Mission Operations, Space Life Sciences, etc.) to develop operations concepts (andor operations scenarios) from which a basic high-level set of requirements could be extracted. This set of requirements could serve as a foundation (along with stakeholder “buy-in”) that would help define the trade space and assist in identifying candidate technologies for further study and evolution to higher-level technology readiness levels. https://ntrs.nasa.gov/search.jsp?R=20060023338 2019-05-22T00:24:28+00:00Z