The proposed HyspIRI mission is evaluating a X-band Direct Broadcast capability that would enable data to be delivered to ground stations virtually as it is acquired. However the HyspIRI VSWIR and TIR instruments are expected to produce over 800 × 10 6 bits per second of data while the Direct Broadcast capability is approximately 10 × 10 6 bits per second for a ~ 80x oversubscription. In order to address this data throughput mismatch a Direct Broadcast concept called the Intelligent Payload Module (IPM) has been developed to determine which data to downlink based on both the type of surface the spacecraft is overlying and onboard processing of the data to detect events. For example, when the spacecraft is overlying polar regions it might downlink a snow/ice product. Additionally the onboard software would search for thermal signatures indicative of a volcanic event or wild fire and downlink summary information (extent, spectra) when detected. Earth Observing One (EO-1) has served as a test bed and pathfinder for this type of onboard product generation. As part of the Autonomous Sciencecraft (ASE), EO-1 implemented in ίight software the ability to analyze and develop products for a limited swath of the Hyperion hyperspectral instrument onboard the spacecraft. In a series of technology demonstrations that became part of the operational EO-1 system over 5000 science products have been generated onboard EO-1 and down linked via engineering S-band contacts, a routine automated process that continues to this day. We describe the onboard products demonstrated in EO-1 operations and show how they have paved the way for the HyspIRI Intelligent Payload Module concept.
The launch of the NASA Earth Observing 1 (EO-1) platform in November 2000 marked the establishment of spaceborne hyperspectral technology for land imaging. The Hyperion sensor onboard EO-1 operates in the 0.4-2.5 micrometer spectral range, with 10 nanometer spectral resolution and 30-meter spatial resolution. Spectral unmixing has been one of the most successful approaches to analyze Hyperion data since its launch. It estimates the abundance of spectrally pure constituents (endmembers) in each observation collected by the sensor. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation. In this paper, we develop a cloud implementation of a full hyperspectral unmixing chain made up of the following steps: 1) dimensionality reduction; 2) automatic endmember identification; and 3) fully constrained abundance estimation. The unmixing chain will be available online within the Web Coverage Processing Service (WCPS), an image processing framework that can run on the cloud, as part of the NASA SensorWeb suite of web services. The proposed implementation has been demonstrated using the EO-1 Hyperion imagery. Our experimental results with a hyperspectral scene collected over the Okavango Basin in Botswana suggest the (present and future) potential of spectral unmixing for improved exploitation of spaceborne hyperspectral data. The integration of the unmixing chain in the WCPS framework as part of the NASA SensorWeb suite of web services is just the start of an international collaboration in which many more processing algorithms will be made available to the community through this service. This paper is not so much focused on the theory and results of unmixing (widely demonstrated in other contributions) but about the process and added value of the proposed contribution for ground processing on the cloud and onboard migration of those algorithms to support the generation of low-latency products for new airborne/spaceborne missions.
Abstract Between 24 March and 5 June 2010, the Hyperion hyperspectral imager and Advanced Land Imager (ALI) on NASA's Earth Observing 1 ( EO‐1 ) spacecraft obtained an unprecedented sequence of 50 observation pairs of the eruptions at Fimmvörðuháls and Eyjafjallajökull, Iceland. This high acquisition rate was possible only through the use of data flow streamlined by using the autonomously operating NASA Volcano Sensor Web (VSW). The VSW incorporates notifications of volcanic activity from multiple sources to retask EO‐1 and process Hyperion data to extract eruption parameters from high spatial and spectral resolution visible and short‐wavelength infrared data. Physical changes in eruption style and magnitude were charted as the eruptions ran their course. Rapid data downlink and automatic data‐processing algorithms generated a variety of products which are compared with estimates from ground‐based observations and post‐eruption in situ measurements. Estimates of effusion rate from heat loss measurements underestimate actual effusion rate (while still following broad eruption rate trends) but are closer to in situ estimates for effusive eruptions (Fimmvörðuháls) than explosive, ash‐rich eruptions (Eyjafjallajökull). During the later stages of the 2010 eruption, VSW‐generated products were rapidly delivered to end‐users in Iceland to aid in the assessment of risk and hazard. The success of the VSW led to Icelandic Meteorological Office (IMO) in situ sensors being incorporated into the VSW, and in May 2011 an IMO seismic alert autonomously triggered EO‐1 observations of a new eruption at Grímsvötn volcano. Finally, the VSW demonstrates an autonomy‐driven, multi‐asset, spacecraft retasking and data processing system that maximizes science return, a desirable capability for future NASA missions.
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, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of a 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 an open messaging architecture and web services. SensorWebs provide easier access to sensor data, automated data product production and rapid data product delivery. Disasters are the perfect arena to test SensorWeb functionality since emergency workers and managers need easy and rapid access to satellite, airborne and in-situ sensor data as decision support tools. The Namibia Early Flood Warning SensorWeb pilot project was established to experiment with various aspects of sensor interoperability and SensorWeb functionality. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team began to examine how to measure economic impact of SensorWeb technology infusion. This paper describes the architecture and software components that were developed along with performance improvements that were experienced. Also, problems and challenges that were encountered are described along with a vision for future enhancements to mitigate some of the problems.
Abstract The Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture. To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed. This system enables scientists, ministry officials, nongovernmental organizations, and other interested parties to freely access all available water information produced by the project, including comparisons of NASA satellite imagery to model forecasts of flooding or drought. The local expertise needed to generate and enhance these water information products has been grown through a series of training meetings bringing together national government officials, regional stakeholders, and local university students and faculty. Aided by online training materials, these exercises have resulted in additional capacity-building activities with CREST and EF5 beyond Namibia as well as the initial implementation of a global flood monitoring and forecasting system.
The Earth Observing One (EO-1) mission has been a pathfinder in demonstrating autonomous operations paradigms. In 2010-2012 (and continuing), EO-1 has been supporting sensorweb operations to enable autonomous tracking of flooding in Thailand. In this approach, the Moderate Imaging Spectrometer (MODIS) is used to perform broad-scale monitoring to track flooding at the regional level (500 m/pixel) and EO-1 is autonomously tasked in response to alerts to acquire higher resolution (30 m/pixel) Advanced Land Imager (ALI) data. This data is then automatically processed to derive products such as surface water extent and volumetric water estimates. These products are then automatically pushed to relevant authorities in Thailand for use in damage estimation, relief efforts, and damage mitigation. EO-1 has served as a testbed and pathfinder to this type of sensorweb operations. Beginning with EO-1, these techniques for monitoring are being extended to other space sensors (such as Radarsat-2, Landsat, Worldview-2, TRMM) and integrated with hydrological models, and integration with in-situ sensors.
Volcanic activity can occur with little or no warning. Increasing numbers of space borne assets can enable coordinated measurements of volcanic events to enhance both scientific study and hazard response. We describe the use of space and ground measurements to target further measurements as part of a worldwide volcano monitoring system. We utilize a number of alert systems including the MODVOLC, GOESVOLC, US Air Force Weather Advisory, and Volcanic Ash Advisory Center (VAAC) alert systems. Additionally we use in-situ data from ground instrumentation at a number of volcanic sites, including Iceland.
The purpose of this presentation is for a Technical Interchange Meeting with the Namibia Hydrological Services (NHS) in Namibia. The meeting serves as a capacity building exercise. This presentation goes over existing software functionality developed in collaboration with NHS over the past five years called the Namibia Flood Dashboard. Furthermore, it outlines new functionality developed over the past year and future functionality that will be developed. The main purpose of the Dashboard is to assist in decision support for flood warning. The Namibia Flood Dashboard already exists online in a cloud environment and has been used in prototype mode for the past few years.Functionality in the Dashboard includes river gauge hydrographs, TRMM estimate rainfall, EO-1 flood maps, infrastructure maps and other related functions. Future functionality includes attempting to integrate interoperability standards and crowd-sourcing capability. To this end, we are adding OpenStreetMap compatibility and an Applications Program Interface (API) called a GeoSocial API to enable discovery and sharing of data products useful for decision support via social media.
The Earth Observing One (EO-1) satellite was launched in November 2000 as a technology demonstration mission with an estimated 1-year lifespan. It has now successfully completed 12 years of high spatial resolution imaging operations from low Earth orbit. EO-1's two main instruments, Hyperion and the Advanced Land Imager (ALI), have both served as prototypes for new generation satellite missions. ALI, an innovative multispectral instrument, is the forerunner of the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission's (LDCM) Landsat-8 satellite, recently launched in Feb. 2013. Hyperion, a hyperspectral instrument, serves as the heritage orbital spectrometer for future global platforms, including the proposed NASA Hyperspectral Infrared Imager (HyspIRI) and the forthcoming (in 2017) German satellite, EnMAP.
