HR: 1330h
AN: G23A-06    [Abstracts]
TI: Challenges and Solutions to Producing a Useful High Resolution Soil Moisture Product
AU: * Houser, P R
EM: Paul.Houser@nasa.gov
AF: NASA, Code 614.3, Greenbelt, MD 20771
AU: Walker, J P
EM: j.walker@unimelb.edu.au
AF: The University of Melbourne, Room 409, Building D, Melbourne, VIC 3010 Australia
AB: Information about surface soil moisture conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, fire prediction, water supply, military mobility, etc.. Near-surface soil moisture is currently available from non-ideal sensor configuration observations, and two missions targeted at measuring near-surface soil moisture with ideal sensor configuration are expected before the end of the decade (the European Space Agency (ESA), Soil Moisture and Ocean Salinity (SMOS) mission, and the National Aeronautics and Space Administration (NASA), Hydrospheric states "Hydros" mission). Though remote sensing can make spatially comprehensive measurements of surface soil moisture, it cannot provide information on the entire land surface hydrologic system, and the measurements represent only a snap shot in time. Alternatively, land surface hydrology process models may be used to predict the temporal and spatial hydrologic system variations, but these predictions are often poor, due to model initialization, parameter and forcing errors, and inadequate model physics and/or resolution. Therefore, an attractive future prospect is to optimally merge the spatially comprehensive but limited soil moisture remote sensing observations with the complete but typically poor predictions of a hydrologic model to yield the best possible hydrologic system state estimation, and utilize limited point measurements to calibrate the model(s) and validate the assimilation results. While hydrologic data assimilation is still very much in its infancy, a few hydrologic models have been developed that can use remotely sensed soil moisture observations. Through these studies, land surface data assimilation has shown significant potential to improve the realism of land surface model soil moisture predictions. This becomes especially relevant when using the models' outputs as a basis for decision support in the context of resources management (e.g. irrigation water allocation), risk reduction (e.g., fire control), and transportation (e.g. terrain-state monitoring for vehicle mobility), among others. By assimilating satellite observations into decision support systems, one can move these land data assimilation research results into the applications realm, with clear benefits for society. This is accomplished through the use of well established modeling and data assimilation systems, customized for each specific application.
DE: 0933 Remote sensing
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2005 Joint Assembly