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The long-term vision of Earth Science measurements involves sensor webs that can provide information at conforming spatial and temporal sampling scales, and at selectable times and locations, depending on the phenomena under observation. Each of the six strategic focus areas of NASA Earth Science (climate, carbon, surface, atmosphere, weather, and water) has a number of measurement needs, many of which will ultimately need to be measured via such a sensor web architecture. Here, we develop technologies that enable key components of a sensor web for an example measurement need, namely, soil moisture.
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Soil moisture is a measurement need in four out of the six NASA strategic focus area roadmaps (climate, carbon, weather, and water roadmaps). It is used in all land surface models, all water and energy balance models, general circulation models, weather prediction models, and ecosystem process simulation models. Depending on the particular application area, this quantity may need to be measured with a number of different sampling characteristics. Traditional remote sensing techniques using radars and radiometers fail to meet such requirements due to their large footprints.
This project introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors. A potential application for this technology is the validation of products from Soil Moisture Active/Passive (SMAP) mission. Spatially, the total variability in soil-moisture fields comes from variability in processes on various scales. Temporally, variability is caused by external forcings, landscape heterogeneity, and antecedent conditions. Installing a dense in-situ network to sample the field continuously in time for all ranges of variability is impractical. However, a sparser but smarter network with an optimized placement plan and optimal measurement schedule can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic physics-based hydrologic and sensor modeling system. The design of this smart sensor web consists of the control architecture, sensor placement and scheduling algorithms, physics-based hydrologic and sensor models, and actuation and communication hardware.
This work is being carried out at the University of Michigan and at the Massachusetts Institute of Technology through a grant from the National Aeronautics and Space Administration Earth Science Technology Office (NASA ESTO), Advanced Information Systems Technologies program.
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