Availability of data is critical to the success of NASA's ESE. The EOS Data and Information System , as described on this EOSDIS home page site, must be responsive to this requirement. EOSDIS manages data from NASA's past and current Earth science research satellites and field measurement programs. During the coming EOS era, EOSDIS will allow command and control of satellites and instruments, and will generate data products based on orbital observations. In addition, EOSDIS will generate data sets made by assimilation of satellite and in situ observations into global climate models.
EOSDIS's services will include:
NASA planners base the present plans for EOSDIS on an open system, with distributed architecture. This permits data controllers to allocate EOSDIS elements to various locations to take best advantage of different institutional capabilities and science expertise.
EOSDIS consists of several components including:
Together, these components will address all command and control; data acquisition, transport, reduction, storage, and visualization; and user access needs.
As we noted earlier, the basic impetus for the kinds of studies we described is to provide input to planners and policy makers. Having access to raw (or even processed) data is of little use to such groups. What they need is information for making decisions about allocating and, perhaps, regulating resources. This information answers questions, such as, "If we do not curb automobile pollution, what may be its effect on atmospheric composition and temperatures over the next 5-10 years?" Or, "If we continue to deforest the Pacific northwest, what may be the effect on biodiversity, land cover, land use, and water run-off patterns?"
No one can actually predict the future accurately, but we can create mathematical models that generate useful predictive functions. Such models can be as simple as that shown in the next chart, which describes the distribution of incoming solar energy through major portions of the Earth's systems. It's similar to balancing the books: The sum of all components that use solar energy must equal the amount of incoming solar radiation. If they don't add up, our model is wrong, or we're missing something.
Christopherson, R.W., GEOSYSTEMS: An Introduction to Physical Geography, 2nd Ed. © 1994. Reproduced by permission of Prentice Hall, Upper Saddle River, New Jersey)
But the models we need for an Earth-system, predictive capability are much more complex than what we show here. Such models must account for energy budgets, sources and sinks of carbon and other biogeochemically related materials, effects of temperature on wind speed and direction, precipitation patterns, land use patterns, speed and direction of oceanic currents, increases in so-called greenhouse gases, and more. A full understanding of how these components interact with each other supports our willingness to trust the output from such models. We show an example of the complexity of these models, along with a color-coded indication of how EOS and other MTPE platforms contribute to a better understanding, in the figure below, which is based on the Bretherton diagram shown on page 16-4:
Because of the complexity of such models, EOS users must accumulate multitudes of timely data from many different sources, over a relatively long period, by a highly advanced data and information system that ingests, processes, and distributes those data to interested parties around the world.
Based on the predictive models described above, we must make decisions based on our understanding of the potential magnitude of global change, in order for planners and policy-makers to define strategies for mitigation or adaptation. These strategies may have widely different economic and societal impact, involving health, standard of living, and quality of life. EOS studies address how climate changes affect water resources, agriculture and ecosystems, and provide fundamental data sets on land-cover change, and measures of sea level change. These EOS measurements and the resulting predictive models address such topics as marine productivity, ozone depletion, air quality, and resources monitoring. These activities give us the tools we need to understand our Earth system and its many subsystems, and to understand the role we play in modifying such systems, and the roles they play in our daily lives.
We bring to your attention another related program that will rely on a variety of satellites and sensors, and the participation of many nations, to monitor and assess aspects of the environmental that relate to natural and manmade disasters. This program is an outgrowth of agreements worked out during a May 1996 Workshop on International Cooperation in Space. We summarize the results of their recommendation to use remote sensing for this purpose in the table shown here. Other satellites have since been added to the list, and EOS will contribute to the monitoring capabilities. (See also, the summary of satellite programs planned over the next ten years given in Section 21 [page 21-1]).
|System||Status||Observation capabilities||Types of applications|
|Weather satellites||many existing||global day and night observations||prediction/monitoring of hurricanes, typhoons, tornadoes, volcanic eruptions|
|Landsat-7||operational||visual 30 m and multispectral 80 meter land observations||land use, flood extent, environmental monitoring|
|SPOT 1-4||operational||visual 10 to 30 m land observations||3 dimensional mapping, flood extent, damage assessment, crop identification|
|IRS-1C||operational||visual 6-30 m land and sea observations||3 dimensional mapping, oil spill detection, flood extent, damage assessment|
|RESURS-O1||operational||visual and multispectral 160-600 meter land and sea observations||regional environmental mapping, monitoring of coastal zones, crop development, drought, flood areas and fires|
|ERS||operational||all weather 25-500 m land and sea observations||3 dimensional mapping, oil spill detection, flood extent, damage assessment, night coverage|
|Radarsat||operational||all weather 10-100 m land and sea radar observations||3 dimensional mapping, oil spill detection, flood extent, damage assessment, night coverage|
|JERS||operational||all weather 18 m land and sea observations||3 dimensional mapping, oil spill detection, flood extent, damage assessment, night coverage|
|SeaWiFS||launched in 1997||multispectral 1 & 4 km sea observations||oil spill detection, ocean pollution monitoring, algae detection|
|Cosmos, KVR-1000||operational||visual 2 m land observations (not near-real-time)||high-resolution mapping, infrastructure identification, terrain analysis|
|Space Imaging IKONOS-2)||first launched in 1999||visual 1 m and multispectral 4 m land observations||high-resolution mapping, infrastructure identification, terrain analysis, crop identification|
|OrbView-2||launched in 1999||visual 1 & 2 m and multispectral 4 m land observations||high-resolution mapping, infrastructure identification, terrain analysis, crop identification|
|QuickBird-2||launched in 2001||visual 1 m land observations||high-resolution mapping, infrastructure identification, terrain analysis|
|SPOT 5A||launched in 2002||visual 5 m land observations||high-resolution mapping, infrastructure identification, terrain analysis, crop identification|
|OrbView-3||launched in 2003||visual <1 m and multispectral 4 m land observations||high-resolution mapping, infrastructure identification, terrain analysis, crop identification|