ABSTRACT:

Over the past 40 years there have been significant improvements in weather forecasting. These improvements are primarily due to (1) improved model physics and increased numerical grid resolution made possible by ever-increasing computational power, and (2) improved model initialization made possible by the use of satellite-derived remotely sensed data. In spite of these improvements, however, we are still not able to consistently and accurately forecast some of the most complex nonlinear diabatic mesoscale phenomena, such as propagating tropical mesoscale convective systems/cloud clusters, tropical storms, and intense extratropical storms. These phenomena develop over very fine spatial scales of motion and temporal periods and are dependent on convection for their existence. Poor observations of convection, boundary layer dynamics, and the larger scale pre-convective environment are often the cause of these substandard simulations and thus require improved observational data density and numerical forecast grid resolution.

This paper performs a set of Observing System Simulation Experiments (OSSE). The objective of the OSSE experiments is to demonstrate that an adaptive (targeted) observational strategy can improve forecast accuracy over existing more conventional observational strategies in terms of enhancing the initial conditions and subsequent accuracy of the simulations of a numerical weather prediction model. For the proof of this concept, hurricane Floyd (1999) is chosen as a test case. The set of experiments starts from a baseline high-resolution forecast of hurricane Floyd using the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). This baseline run serves as the truth set for the OSSE under a "perfect model" assumption. From the baseline run, atmospheric vertical profiles were extracted to simulate "pseudo-observations" using different adaptive strategies. These data extracts were used to create new coarse-resolution forecasts of hurricane Floyd that were then compared against the both baseline and real atmospheric observations. In general, the experiments show that additional adaptive observations in sensitive areas can help to reduce hurricane forecast errors significantly from a Numerical Weather Prediction (NWP) model.

ABSTRACT:

Currently, more and more earth observation data have been acquired by many kinds of sensors on different platform, such as optic sensors, microwave sensors, infrared sensors, hyperspectral sensors, etc. Thanks to giant resource being required to store and transmit these tremendous data so that the cost is very large and the efficiency is low, investigators are compelled to process them on-board as possible as they can. So far, on-board data processing only settles on some simple preprocessing, such as correction, denoising, compensation, etc. Information extraction not only is the objective of earth observation, but can distill large amount data so that amount of data needing to be stored and transmitted is reduced greatly. Feature extraction, change detection, and object recognition executed on-board will provide us an efficient information extraction system for earth observation.

Data fusion technique has been widely used to process earth observation data on the ground, which can generate data with higher quality and extract better information from multisource or multitemporal data. Furthermore, data fusion can also be used to extract better information from these data on-board, simultaneously, the redundant data will be eliminated greatly so as to accelerate data processing and reduce data for storage and transmission. However, on-board data fusion processing will confront more difficulty, one of the most principal troubles is that on-board data processing system must be completely autonomous, which results in some procedures such as image registration, feature extraction, change detection, object recognition becoming more complicated, while they can be processed by help of manual operates despite being difficult on the ground. Of course, the tremendous advantage of data fusion for on-board data processing will promote investigators to remove the obstacles on the road to on-board data fusion-based information extraction.

ABSTRACT:

This paper describes a system for planning and scheduling science observations for fleets of Earth observing satellites. Input requests for imaging time on an Earth observing satellite are specified in terms of the type of data desired, the location to be observed, and an objective priority of satisfying the request. The problem is to find a sequence of start times for observations and supporting activities such as instrument slewing and enforcement of instrument thermal duty cycles, that satisfy a set of temporal and resource constraints describing the physical operation of the spacecraft. We assume that there are more requests that can possibly be serviced over a given scheduling window, and that images may vary in their scientific utility, leading to an optmization problem. This paper presents an approach to solve this problem employing a formal declarative model of the problem, stochastic sampling methods to find plans, and special purpose heuristics based on a generalized contention measure.

ABSTRACT:

The work of the Space Science Centre at the University of Sussex in the development of intelligent space plasma instruments is presented here. Previously the Centre has included various intelligent techniques within space instruments flown on a number of space missions. A neural network was included in the SPREE instruments flown on Shuttle flights STS-46 (1992), and STS-75 (1996). Fuzzy Logic control of telemetry compression and buffering was designed for the ELISMA instrument on MARS-96. Sussex pioneered the use of particle correlation via hardware and software processing as a means of studying plasma wave-particle interactions using particle detection pulses within particle sensors, (AMPTE UKS, CRRES, STS-46, STS-75, ESA Cluster II, and auroral sounding rockets). Large interacting arrays of microprocessors were employed to provide processing for the above activities (e.g. 20 separate processors were used within SPREE). Also fault-tolerant arrays of processors were designed for the MARS-96 ELISMA instrument. Current research at the Space Science Centre concentrates on the development of flexible space instruments compatible with on-board intelligence and on increased use of Field Programmable Gate Arrays, FPGA, for fast real-time implementations of dedicated complex algorithms. For example real-time plasma simulations of the spacecraft's plasma environment are being implemented in FPGA with local measurements used directly as input parameters. These simulations can then be used to optimise instantaneous instrument parameters and, most significantly, by comparing simulation results with actual measured parameters concentrate data transmission on phenomena whose physics is least understood.

ABSTRACT:

The general trend in remote sensing is on one hand to increase the number of spectral bands and the geometric resolution of the imaging sensors which leads to higher data rates and data volumes. On the other hand the user is often only interested in special information of the received sensor data and not in the whole data mass. Concerning these two tendencies a main part of the signal pre-processing can already be done for special users and tasks on-board a satellite. For the BIRD (Bispectral InfraRed Detection) mission a new approach of an on-board data processing is made. The main goal of the BIRD mission is the fire recognition and the detection of hot spots.

This paper describes the technical solution and the first results, of an on-board image data processing system based on the sensor system on two new IR-Sensors and the stereo line scanner WAOSS (Wide-Angle-Optoelectronic-Scanner). The aim of this data processing system is to reduce the data stream from the satellite due to generations of thematic aps. This reduction will be made by a multispectral classification. For this classification a special hardware based on the neural network processor NI1000 was designed. This hardware is integrated in the payload data handling system of the satellite.

ABSTRACT:

While executing tasks such as sea surveilling, maritime searching and rescue, sea pollution monitoring utilizing remote sensed images, the coastal line feature should be determined at first. Thresholding methods is a type of simple but valid methods for image segmentation, likewise, they can be used to detect coastal line feature in remote sensed images. However, while conventional thresholding methods used to do it, they are always short of enough discriminating ability to objects' shadow, weak-scattering vegetations, dark artificial buildings, sea gulf blurred by noise along costal line. This paper proposes a multi-threshold based morphological approach, which divides the isolated regions by thresholding detecting into intra-continent, exterior-sea, and along-coastal isolated regions at first, and then utilizes two definitions and morphological operators to process along-coastal regions further so as to improve the detecting accuracy and decreasing false detecting, especially to enhance detecting accuracy for above objects' shadow, vegetations and dark artificial builds. Experiments are executed and the results exhibit the proposed approach possessing better performance than conventional thresholding approach.

ABSTRACT:

We propose to optimize the use of new computer power, observational data systems, and telecommunication capabilities to extract and utilize remote sensed data available from a variety of means. This optimization is crucial in the sense that extracted both meteorological and surface characteristics datasets will be crucial for the use in an emergency response system. We will combine real-time remote sensing systems, existing remote sensing databases, conventional weather observational databases, and GIS together to provide necessary high spatial and temporal data sets necessary for an emergency response system to mitigate against hazardous material releases into the atmosphere. In this article, we give an overall design and also discuss potential issues for developing such a system. The GIS system will be used to provide access to the geographic information, to support GIS-based computing, and to display the results. Remote sensing databases will be used to provide local area terrain and man-made configuration information such as building shapes. Real-time remote sensing mission is needed for updated information after a massive deconstructive event and for related data assimilation. The distributed online weather data information system will be used to retrieve current and predicted weather parameters. The weather information and the local area geographic information may then be used to feed the selected fast atmospheric transport and dispersion model. The information will be accessed through the Internet following a system-wide specific protocol or open protocols such as those specified by OGC. The system will invoke the model run and convert the model results into GIS compatible format for displaying and for further computation. The final results will be displayed by a GIS/WebGIS based interface, tailored to particular user/agencies.

ABSTRACT:

Remote sensing data and Geographic information systems (GIS) are relatively new and potentially valuable tools for coastal zone management. This paper examines the effectiveness of using remote sensing data to detect sea level change. Since resolution is such an important and vital element of spatial digital data for use in geographic information systems, it is important to know how to assess its quality, accuracy and level of precision. Using remote sensing data to detect sea level change also requires accurate historical baseline spatial data and knowledge of how the coastline is defined and mapped. Map datum refers to the various locations to which geographic measurements are referenced. This referencing system is an important item on the list of cartographic components that help to identify and categorize individual maps. For example, many North American maps have been, or will soon be, converted to a horizontal map datum known as NAD83. Along with horizontal datum, maps are also referenced to vertical datum. The choice of vertical and horizontal map datum along with other cartographic elements such as map projection, scale and meta data will determine to what level of precision coastal change can be accurately measured. This paper will explain how to select the most appropriate baseline spatial data as well as the type of the remote sensing data that will provide the most reliable results for the detection of seal level change. Cobscook Bay, Maine was used for two case studies to demonstrate some of these coastal mapping parameters

ABSTRACT:

MultiSat WebService is a spaceborne service concept primarily to support, extend or substitute information services for mobility and traffic purposes. It allows the determination of traffic data from space on a global and near-real-time scale. Main objective is to provide a profitable service for mobility and traffic management. A market survey being made shows that spaceborne online information services may be viable. The service provides the possibility to receive pre-processed, near-real-time Earth surface data with E-commerce compatible methods. The system design gives the opportunity to freely configure the space system according to customers needs.

The MultiSat infrastructure design features a satellite constellation with imaging Synthetic Aperture Radar (SAR) and optical payloads combined with low-rate communication especially established to support this service. Also included is a scalable, fault tolerant, multi-computer system. The development cycle focuses on an airborne demonstration of the service idea as a first milestone.

The MultiSat WebService concept is being created and designed by a consortium consisting of German Aerospace Center (DLR), Technische Universität Dresden and Fraunhofer Gesellschaft FIRST and presented here as a visionary feasibility study.

ABSTRACT:

This paper describes the preparation of vegetation map of Ganges river basin that could be used for various hydrological analysis that could be useful for water resources planning, flood forecasting and disaster mitigation. This study focuses on vegetation mapping of the Ganges river basin covering from 70° E to 95° E and 35° N to 20° N using NOAA AVHRR.

Initial vegetation cover of the area was prepared by monthly maximum NDVI data. The reason for aggregate daily NOAA-AVHRR data for monthly average was the presence of considerable amount of cloud cover in this region. Having generated the basic vegetation map based on NDVI, it was further classified according to climatic and elevation zones. The final vegetation map represents vegetation classes that are interpreted considering their temporal climatic and altitudinal variation that are needed to be considered for hydrological analysis.

ABSTRACT:

Remote satellites regularly supply lots of remote images representing surface status. We can extract lots of information such as land cover, vegetation distribution. (Townshend et al.1991.1). In the meanwhile, Because of the temporal dynamics and changes in land surface, remote sensing is the only practical means for monitoring land-cover changes (S. Liang, 2001, 2).

With environmental changes, population's explosion, city expands. Correspondingly, it also makes land cover and land use structure vary. Northwest of Beijing is one of a source of duststorm in spring, so it's surface variation will influence environmental changes in Beijing. Vegetation's reduction makes the increase of naked zone. More and more sand dust suspends in the air, and it increases the absorbable grains in the air, which leads to the decrease of environmental quality and influences the environment in Beijing to great degree. (5,Li Lingjun, et al. 2001). Researching surface status of circumjacent zone by remote technology in Beijing will produce meaningful influence on environmental protection and sustainable development.

We select Northwest of Beijing as the research zone, and use remote sensing data of TM and MSS to make the land cover statistics. By this, we research the changes of surface status in northwest Beijing. VI can reflect vegetation types, vegetation growing status and the variations of vegetation types, which is an important parameter to the research of surface status. Influenced by land cover and climates, NDVI will vary in different period. We can find the variation of surface status by comparing NDVI in different period.

ABSTRACT:

Imaging satellites are traditionally scheduled in a static fashion; namely the schedule is created off-line and then uploaded to one or more imaging satellites to be executed as an immutable sequence of commands. In this paper, we make the case for dynamic scheduling of imaging satellites. Dynamic schedules will allow satellite systems to take advantage of information gathered during the execution of the schedule and react to changes in the environment, desired tasking, and the availability of resources. We develop the remote sensing scheduling problem and discuss contingency conditions under which the satellite scheduling problem becomes dynamic. We then review existing work on contingency scheduling and conditional scheduling and propose extensions to address the dynamic satellite scheduling problem. Dynamic schedules will yield improved mission schedules and reduced mission costs.

ABSTRACT:

With the development of SAR processing techniques, high image precision and high real time rate have becoming an important index, especially on military filed. This paper presents a medium grained parallel processing algorithm for SAR imaging. In this parallel processing algorithm, every processing stage is done in parallel, and the degree of parallelism is task-level. It is fit for the parallel computer with good communication capacity. The experiments on DAWNING3000 shows this parallel processing algorithm can get good result on real time rate and processing efficiency.

ABSTRACT:

With increasing numbers of Earth observing satellites in space, huge volumes of remote sensing data will be produced. Traditional remote sensing data processing and distribution methods may not be sufficient for various end users, including novice, intermeddle and advance user communities to efficiently use such datasets. Information and knowledge distribution with these data may help the data usage more efficiently. The Earth remote sensing data processing and distribution will face a new challenge. Maintaining the increasing volumes of data in forms that are readily accessible and that meet the needs of very diverse user communities presents intellectual challenges that are at least the equal of the challenges of building and launching hardware into space. Information distribution may be as important as data distribution. The following issues may be crucial for wider usage of the Earth observing remote sensing datasets: 1. Huge data volumes; 2. Complex data formats, such as, HDF (Hierarchical Data Format) and HDF-EOS (Hierarchical Data Format Earth Observing System); 3.Different map projections; 4.Geographic information system (GIS) applications; 5.Communication protocol and capability; and 6.Processing time. Customized real-time remote sensing data with GIS/Web-GIS compatible formats may become very important for a lot of end users. End users need to obtain Earth observing remote sensing data in more useful forms. On the other hand, more widely distributed Earth observing remote sensing data in different formats through diversified protocols will result in better usage of future Earth observing satellite systems. To address these issues, data compressing and pre-processing (sub-setting and sub-sampling), data format conversing (easy accessing data format such as, GIS compatible format), GIS and Open GIS applications, and simple real time data processing for future Earth observing satellite systems will be discussed in the paper.

ABSTRACT:

Sponsored by NASA's Intelligent Systems Project, a conceptual architecture study is under way to address the problem of getting the most societal value from the large volumes of scientific data that NASA expects to accumulate in the future. Beyond improvements in hardware technologies, advances are needed in concepts and tools to enable intelligent data understanding and utilization. Some of the challenges besides large and ever-growing volumes of data are: data acquisition and accumulation rates tend to outpace the ability to access and analyze them; the variety of data implies a heterogeneous and distributed set of data providers and users; unassisted human-based manipulation of vast quantities of archived data is intellectually overwhelming and cost prohibitive; for applying NASA technologies to operational agencies' decision support systems, it is necessary to demonstrate feasibility of near-real-time utilization of vast quantities of data and the derived information and knowledge; and future data access and usage are difficult to anticipate. The objective of the study is to formulate ideas and concepts and to provide recommendations that lead to research by the computer science community in the near-term, prototyping to demonstrate feasibility in the mid-term, and operational implementation in the period from 2012 to 2025. An abstracted architecture is defined for an intelligent archive showing functionality without regard to physical distribution. The architecture shows significantly enhanced functionality anticipated by NASA as required to serve its and society's future needs. This expression of functionality can help target research by the computer science and information technology communities.

ABSTRACT:

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

THE INFLUENCE OF THE CLUSTER SATELLITE SYSTEM PARAMETER TO IMAGING PRECISION

Huang Shunji    Zhang Xiaoling     Zeng Bin

ABSTRACT:

The Cluster Micro-Satellite Synthetic Aperture Radar?SAR? System is an innovative SAR system brought forward recently. In the Cluster Micro-Satellite System, technology of cross-track interferometric SAR (INSAR) imaging processing is studied. Analyze the influence of the rotating of the cluster micro-satellite to the baseline and the phase of INSAR. Derive the corrected formula of the baseline and the INSAR phase. By computer simulation, compare the errors between corrected and uncorrected height measurement. The analysis results verify the correction of the formula and necessity to correct the baseline and the phase.

ON-BOARD CHANGE DETECTION WITH NEURAL NETWORKS

M. Spigai^a, *, G. Oller ^a,
^a ALCATEL SPACE INDUSTRIES, 26 av. JF. Champollion, BP 1187, 31037 Toulouse, France - (marc.spigai@space.alcatel.fr, guillaume.oller@space.alcatel.fr)

ABSTRACT:

The aim of this paper is to describe a potential on-board change detection chain by earth observation satellites (optical and SAR). The benefits of such an on-board chain are multiple : reduction of the amount of data to transmit from board to ground and autonomy of the system for example. We describe particularly one component of the chain : a detection/classification module based on the neural network, this type of algorithm being a part of a more global future fusion-based classification module. First experiments have allowed to validate the algorithm based on neural networks and first results are satisfying. The continuation consists first to enlarge the classification module with the implementation of other classification algorithms and to compare them with a more exhaustive set of data.

ABSTRACT:

Significant advancements in Earth observation are expected to come about by developing more systematic capabilities for assimilating remote sensing observations and in situ measurements for use in models, at relevant scales, to generate geophysical and biospheric information products. This paper provides an overview of the role of in situ sensing in the context of integrated Earth sensing. It also defines a framework for taking advantage of intelligent sensorwebs based on the converging technologies of micro-sensors, computers, and wireless telecommunications in support of critical activities such as the monitoring of remote environments, risk assessment and hazard mapping, and renewable resource information management. The knowledge gleaned from integrated Earth sensing has the potential to empower managers and decision makers to act on critical climate, sustainable development, natural resource, and environmental issues. The paper also describes early work on a prototype wireless intelligent sensorweb evaluation in the context of watershed management and flood forecasting.

ABSTRACT:

Satellite remote sensing for disease surveillance will play a major role in public health in the coming years. Although the ability to predict epidemic outbreaks is still limited by current research and technology, satellite remote sensing has the potential to become an important tool for assisting epidemiologists in locating areas where disease outbreaks are likely to occur. This will permit the optimization of resources and save lives, especially in developing countries where health related resources are limited and disease outbreaks have far-reaching social and economic consequences.

In order to make satellite sensors reliable tools for epidemiological research, we need to improve upon the capabilities of the current sensors, which are providing data on key epidemiological variables. The most useful remote sensing systems for public health applications will require instruments which can integrate data and information among spectral, spatial and temporal characteristics of remotely-sensed images and disease vector profiles. The ultimate goal of an optimal sensor system, however, is to achieve the capability of using remote sensed data to monitor areas in real time and predict disease outbreaks so that effective preventive actions may be taken. This goal could be accomplished through the creation of a dedicated mission comprised of a collection of instruments and sensors tuned to acquire information directly related to the disease organisms, vectors, reservoirs, hosts, geographic specifications, and environmental variables associated with health problems.

To take advantage of the intelligent space-based remote sensing systems potentially available by 2010 and beyond, we propose to initiate the process of selecting the ideal suite of measurements needed for the development of a dedicated Health and Environment satellite mission. The project will combine the flexibility and expertise in data management and product generation provided by the Center for Earth Observing and Space Research (CEOSR) in the George Mason University (GMU) and its long-standing relation with the NASA Goddard Space Flight Center (GSFC).

ABSTRACT:

Based upon the sparse array composed of cluster SAR satellites, this paper analyzes the space-time and space-frequency characteristic of the received signal in detail, and proposes an algorithm for the moving targets detection and imaging based on array signal processing and Wigner-Ville Distribution (WVD). The results of theoretical analysis and simulation show that the cluster SAR system can achieve the targets SAR image even if the targets is being moving slowly.

ABSTRACT:

In the last three decades, GIS, in terms of both its theory and methodology, has been going rapidly towards its maturity from its very infancy. Nowadays, the most fundamental task of GIS is extending its application in different fields. In retrospection, we can find that the most attention has been paid to development planning forecast?and subsequent evaluation and reflection. But few researches have been done on real-time or near real-time monitoring and decision-making, which is a very important field deserving more attention. To perform real-time monitoring and decision-making, support from telecommunication technology is needed. In fact, only through the integration of GIS and telecommunication technology, could real-time monitoring be enabled. In this paper, we argue that a promising direction in GIS applications is the integration of GIS and telecommunication. Then the concept of Telegeomatics is presented, and its basic structural characteristics analyzed.

The core of Telegeomatics is the integration of GIS and telecommunication, which will availably resolve the problem of obtainment and policy-making support for the real-time and near real-time spatial information.

1) In the case study, a real-time multi-factor environmental monitoring and information service system of Beijing is constructed. Through this research, we found that: 1) it is an effective way to integrate GIS, telecommunication and EO for real-time environment monitoring; 2) GIS is fundamental to the development of the proposed system; 3) The bottleneck of the proposed system is the development of Internet infrastructure, especially the development of IPV6. This indicates that it is urgent to develop IP infrastructure in order to further promote the development of the proposed system.

ABSTRACT:

One of the most promising approaches to solve the challenging task of urban land-use classification is integration of multi-spectral satellite image data and range data. In this paper, height data acquired by airborne laser scanning is not only applied for geometric correction of the multi-spectral satellite image data by the generation of ortho-images, but also integrated in urban environment classification. Laser scanning data acquired by Airborne 3D Imager is used to obtain information on the location height above the terrain surface for each pixel. This information can be applied in order to separate urban objects higher than the ground level from objects that are at ground level, e.g., buildings, trees, streets, grass-covered areas, water bodies and bare land etc. It is demonstrated that the classification of urban scenes is significantly improved by integrating multi-spectral and geometric datasets.

ABSTRACT:

The number of sensors for Earth environmental observation is growing. Several new sensors have been announced for the near future and several proposals for smart sensors have been proposed. In particular, distributed sensors, sensor network and sensor integration are being taken into account, and smart sensors, intelligent sensors, sensor's autonomy, etc. is beginning to be used in future earth observing satellites. This paper investigates the development of sensors in Earth observing (EO) satellites, analyzes the current high-resolution EO satellites and presents the tendency of new imaging systems in future Earth observing satellite. The purpose of this paper is to contribute to the growing interest in knowing and keeping track of the details of the imaging system of earth observing satellites. This project is of particular interest to those who are developing the skills to measure and understand the detail of our Earth environment through analysis of satellite data.

ABSTRACT:

In order to let the end-users can directly downlink satellite imagery that they specify area of interest (AOI), One of key challenging technologies is how the on-board data distributor autonomously retrieves the imagery according to user's command as well as simultaneously retrieves the other data sets, such as, temperature, moisture, geographic attribute data, e.g. street name, from other databases, which will be used for providing the user better visualization and understanding to the situation surrounding him/her, especially they are not familiar with the environment. This paper presents a whole new design of concept for on-board geo-data management system in future intelligent earth observing satellite system. The concept design is based on the idea of using satellite imagery, supported by geo-data database associated with attributes and DEM database, as data sources to describe the real world of AOI, where the users specifically query through uplink to satellite from ground. The query results will directly be downlinked to users with a form of geo-image-map. Thus, the new data model, data structure and database management system has to be designed and developed for this purpose. The designed data model is able to manage the huge data sets so that the raster satellite imagery and geo-database can better be queried, visualized, flexibly handled.

ABSTRACT:

This paper presents a simulated design of an envisioned future intelligent Earth observing satellite system (FIEOS). The proposed system is a space-based architecture for dynamic and comprehensive on-board integration of Earth observing sensors, data processors and communication systems. It is intended to enable simultaneous, global measurements and timely analyses of Earth's environment for a variety of users. The implementation strategies suggest a seamless integration of diverse components into a smart, adaptable, and robust Earth observation satellite system. The simulated design envisions a system that uses instruments capable of providing earth science measurements to a degree of precision and span of coverage not currently available. Common users would access data directly in a manner similar to selecting a TV channel. The imagery viewed would most likely be obtained directly from the satellite system. Real-time information systems are key to solving the challenges associated with this architecture. Realization of such a technologically complex system will require the contributions of scientists and engineers from many disciplines. Hopefully, this concept will impact how Earth observing satellite scientists conduct missions in decades to come.