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Title:   Author  item
                                            Anderson, Marthaitem
                                            Kustas, William

                                                         - *Bill*item
                                            NORMAN, JOHN - UNIV. OF WI
  Submitted to: Workshop Proceedings
                                    Publication Type: Proceedings
                                    Publication Acceptance Date: 8/5/2006
                                    Publication  Date: 8/5/2006
                                    Citation: Anderson, M.C., Kustas, W.P., Norman, J. 2006. A multi-scale remote sensing-based modeling system for estimating local and regional fluxes. In: Proceedings of Earth Observation for Vegetation Monitoring and Water Management, November 10-11, 2006, Naples, Italy. p. 146-153.Interpretive Summary:
                                Technical Abstract: An effective flux-scaling scheme must be able to bridge the gap between the field scale of interest to agricultural and resource managers (~100 m) and the regional scale (~10-100 km), the resolutions used by operational climate and weather forecast models. An approach with operational capabilities is described, which employs a flux disaggregation strategy.  Fluxes can be mapped over regional or continental scales in the U.S. at 5-10 km resolution each day using coarse-scale thermal-infrared imagery from a geostationary platform such as Geostationary Operational Environmental Satellite (GOES).  These coarse-scale flux estimates can then be spatially disaggregated to finer scales at sites and times of particular interest using higher resolution imagery from satellite sensors such as Landsat. In this way, the temporal sampling power of the geostationary satellites (images every 15 minutes) can be combined with the spatial resolution of polar orbiters (15m – 1km).  The disaggregation process serves both as a means for quantitatively validating the regional flux predictions and for examining complex landscapes having a diverse mixture of land use and crop cover types that exist in many agricultural regions around the globe. Examples of the application of this modeling framework are presented.


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