HR: 17:30h
AN: IN24A-07    [Abstracts]
TI: Initial Analyses and Demonstration of a Soil Moisture Smart Sensor Web Using Data Assimilation and Optimal Control
AU: * Moghaddam, M
EM: mmoghadd@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Entekhabi, D
EM: darae@mit.edu
AF: Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139,
AU: Liu, M
EM: mingyan@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Teneketzid, D
EM: teneket@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Goykhman, Y
EM: yuriy@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Shuman, D
EM: dishuman@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Mahajan, A
EM: adityam@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AU: Nayyar, A
EM: anayyar@umich.edu
AF: The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109,
AB: We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. The sensor nodes are guided to perform as a macro-instrument measuring processes at the scale of the satellite footprint, hence meeting the requirements for the difficult problem of validation of satellite measurements. The science measurement considered is the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in- situ sensors. Satellites allow global mapping but with coarse footprints. The total variability in soil-moisture fields comes from variability in processes on various scales. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network 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 data assimilation system. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware will be presented in this paper. The results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture sensors and their physical measurement model, a dynamic soil moisture time-evolution model (SWAP), and an optimal control strategy will then be shown.
DE: 1640 Remote sensing (1855)
DE: 1694 Instruments and techniques
DE: 1848 Monitoring networks
DE: 1895 Instruments and techniques: monitoring
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting