NASA Logo, National Aeronautics and Space Administration

National Aeronautics and Space Administration

Goddard Space Flight Center

NASA Sensor Web Experiments

Nasa Sensor Webs

Two Columns

SensorWeb Evolution

For more information contact:Michael Flick
EO-1 Technology Transfer Manager
EO-1 Mission Office
NASA Goddard Space Flight Center
Greenbelt, MD 20771
Phone: 301-286-8146
Fax: 301-286-2840
E-Mail: Michael Flick

One of the original proponents of the SensorWeb concept at Goddard was Mark Schoeberl, formerly the Earth Observing System (EOS) Project Scientist back in the 1990’s at Goddard, who developed prelaunch the A-train concepts that would cross strap the data from the Earth Observing System (EOS) afternoon constellation satellites such Aqua, Aura, Calipso and others. This precursor concept sought to leverage the integrated data for useful societal benefits. He developed two videos entitled Vision 2020 and Vision 2030 to visualize how the public would use this integrated data in similar ways as people use the weather forecast now. Steve Talabac, a technologist in the Software Systems Division at Goddard developed the working definition of SensorWeb and explored a variety of SensorWeb concepts from approximately 2000 to his retirement recently and in particular the concept of automatically supplying key data observation to weather models (such as hurricane track models) from satellites to improve the model in realtime. One iteration of the SensorWeb definition is “SensorWeb is a set of sensors (land, marine, air, space) and processing which interoperate in a (semi) automated collaborative manner for scientific investigation, disaster management, resource management, and environmental intelligence”.

In 2003, our team leveraged some of the original SensorWeb concepts, and developed a preliminary “slow motion” prototype of a SensorWeb using Earth Observing 1 (EO-1) and one of its instruments, the Advanced Land Imager (ALI) along with the MODIS instrument which was on both Terra and Aqua, and was used to detect wildfires. This preliminary effort was done in collaboration with the U.S. Forest Service, University of Maryland and U.S Geological Survey (USGS). We used the National Interagency Fire Center (NIFC) major fire website to identify a key fire as a trigger. The University of Maryland automatically generated hot spot locations based on MODIS observations globally every few hours. The hot spot locations within the selected major fire was used to automatically trigger EO-1 to take a high resolution image at the location of the hot spot identified by the MODIS hot spot map. The resulting image from EO-1 was processed semi-automatically via a number of processing steps into a Burn Area Emergency Rehabilitation (BAER) map that is used by the U.S. Forest service to assess the degree of burn severity after a fire to determine which areas would recover on their own and which areas had to be reseeded to avoid soil erosion. In that demonstration, we were able to create the final product in about 24 hours. The Forest Service would like this product and other similar satellite products within a few hours since there is typically a daily planning meeting for the emergency workers to develop a rehabilitation strategies. Figure 1 depicts some of the highlights in using SensorWeb for wildfires.

The biggest problem at that time and that still exists is that there is only partial satellite coverage using existing assets when trying to detect fires and other disaster events at resolutions that could provide critical decision support.  The Forest Service has made use of airborne instruments such as Autonomous Modular Sensor (AMS) on B200 airplanes and Cessna Citation jets to answer questions like, “where is the fire now?”  But this can only cover small areas and at great expense.


This begs the question of how to generalize the creation of a cost-effective SensorWeb using satellite sensors, airborne sensors, and ground sensors in a way that is more responsive to the needs of a science user or the general public.  Furthermore, an additional complicating factor is that the volume of data emanating from sensors is rapidly increasing due to increased spatial, spectral and temporal resolution along with increased onboard computing performance and networking capacities.

Our team has been involved with six NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) multiyear research awards which examined various aspects of how to optimize SensorWeb responsiveness and performance.  We have used disaster management as our target problem to solve with SensorWebs.   Furthermore, our team is part of various international collaborations to extend interoperability with other teams working on similar problems.  In the last 11 years, there have been great strides forwards in performance, interoperability standards and responsiveness.    We are also poised to leap forward with innovations in hardware and software, focused on getting the best performance from our processors.