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SPIE 2000,
recise Orbit
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Proceedings
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Jorenz, 1998.
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^s, Potsdam,
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hop on Real-
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IKONOS,
ol. 66, no. 9,
Istanbul 2004
SPACE-BASED SENSOR WEB FOR EARTH SCIENCE APPLICATIONS — AN
INTEGRATED ARCHITECTURE FOR PROVIDING SOCIETAL BENEFITS
Shahid Habib, Stephen J. Talabac
NASA
Goddard Space Flight Center
Greenbelt, Maryland, USA
KEY WORDS: Sensor Web, Intelligent, Invasive Species, Earth Science, Modeling
ABSTRACT:
There is a significant interest in the Earth Science research and user remote sensing community to substantially increase the number of
useful observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal,
spectral, and spatial coverage of the area(s) under investigation. However, there is little analysis available in terms of the benefits, costs
and the optimal set of sensors needed to make the necessary observations. Classic observing system solutions may no longer be
applicable because of their point design philosophy. Instead, a new "intelligent data collection system" paradigm employing both
reactive and proactive measurement strategies with adaptability to the dynamics of the phenomena should be developed. This is a
complex problem that should be carefully studied and balanced across various boundaries including: science, modeling, applications, and
technology. Modeling plays a crucial role in making useful predictions about naturally occurring or human-induced phenomena. In
particular, modeling can serve to mitigate the potentially deleterious impacts a phenomenon may have on human life, property, and the
economy. This is especially significant when one is interested in learning about the dynamics of, for example, the spread of forest fires,
regional to large-scale air quality issues, the spread of the harmful invasive species, or the atmospheric transport of volcanic plumes and
ash. This paper identifies and examines these challenging issues and presents architectural alternatives for an integrated sensor web to
provide observing scenarios driving the requisite dynamic spatial, spectral, and temporal characteristics to address these key application
areas. A special emphasis is placed on the observing systems and its operational aspects in serving the multiple users and stakeholders in
providing societal benefits. We also address how such systems will take advantage of technological advancement in small spacecraft and
emerging information technologies, and how sensor web options may be realized and made affordable. Specialized detector subsystems
and precision flying techniques may still require substantial innovation, development time and cost: we have presented the considerations
for these issues. Finally, data and information gathering and compression techniques are also briefly described.
1. INTRODUCTION
Since the inception of the first space-based observing system
more than 45 years ago researchers have been investigating
science questions/phenomena by deploying and operating single
platform ^ missions. Although instrument and platform
technologies and on-orbit capabilities have evolved significantly
since that time, today’s platforms are typically multi-instrument
satellites having multiple science objectives. The recent trend is a
shift to single instrument platforms. However, there is no
conventional way indicating what is the right or best way to do
the job. By and large, it is still based on the need and the
availability of funds to perform the scientific investigations.
Either of the approaches (i.e., multi-instrument and single
instrument platforms), have been in use for reasons such as:
- Laws of physics dictating the instrument design
- Technology miniaturization limitations
— Heavy reliance on hardware because of unexplored or
lagging development in the software and algorithmic
techniques
— Cost and physical limitations inhibiting the accessibility to
space
- Limited interest and/or perceived low return on investment
by the private sector to conduct science research from space
- Limited international cooperation
Traditionally, the National Aeronautics and Space Administration
(NASA), in conjunction with other US Government Agencies
such as the National Oceanic and Atmospheric Administration
(NOAA) and the United States Geological Survey (USGS), have
built many large platform missions for Earth science research.
For example, Nimbus, Landsat, Polar-Orbiting Operational
Environmental Satellite (POES), Upper Atmospheric Research
Satellite (UARS), Terra and recently launched Aqua, and
Environmental Satellite (Envisat) by European Space Agency
(ESA), fall under the large platform classification. These
satellites carried many instruments to perform remote sensing in
the UV, Visible, IR, microwave, and radio frequency spectral
bands. In retrospect if one analyzes an integrated development life
cycle for any of these platforms, the duration is approximately 7-
10 years and the cost can be nearly half a billion real year dollars.
At the same time, let's not forget a large infrastructure cost on the
ground in terms of ground communication networks and
computational facilities. The idea here is not to play down the
important contributions being made by such space assets but to
learn critical lessons in order to take advantage of emerging
technologies to meet the future scientific measurement needs.
2. WHY SENSOR WEBS?
Since the sensor web concept and the properties that characterize
it are still evolving, a high level description of it and the
terminology we have used is useful to establish a foundation with
which to understand what is meant by these terms. We have
defined a sensor web as: a coherent set of distributed "nodes "
interconnected by a communications fabric that collectively
behave as a single, dynamically adaptive, observing system.
Through the exchange of sensor measurements and other
information, produced and consumed by its nodes, the sensor web
