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Title:   Author  item
                                            ZHAN, X - NOAA NESDISitem
                                            Crow, Wadeitem
                                            Jackson, Thomasitem
                                            O'NEILL, PEGGY - NASA GSFC
  Submitted to: IEEE Transactions on Geoscience and Remote Sensing
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 12/28/2007
                                    Publication  Date: 4/15/2008
                                    Citation: Zhan, X., Crow, W.T., Jackson, T.J., O'Neill, P. 2008. Improving space-borne radiometer soil moisture retrievals with alternative aggregation rules for ancillary parameters in highly heterogeneous vegetated areas. Institute of Electrical and Electronic Engineering, Transaction on Geoscience and Remote Sensing. 2:261-265.Interpretive Summary: Remotely-sensed surface soil moisture retrievals are of potentially great value for a range of agricultural applications including yield forecasting, drought monitoring, water quality modeling and irrigation scheduling.  The ARS Hydrology and Remote Sensing Laboratory is at the forefront of efforts to fully develop these applications.  Part of the challenge in obtaining accurate remotely-sensed estimates of surface soil moisture is adequately dealing with the land surface heterogeneity in vegetation, topography and moisture conditions within the soil moisture retrieval algorithm.  This study presents a numerical sensitivity analysis for the impact of certain types of land surface heterogeneity on surface soil moisture retrievals and derives a simple procedure that corrects retrievals for the impact of non-resolved land surface spatial heterogeneity.  The eventual application of this approach may improve our ability to operationally monitor soil moisture within heterogeneous agricultural landscapes.
                                Technical Abstract: Retrieving soil moisture from space-borne microwave radiometer observations often requires ancillary parameters such as surface vegetation opacity or vegetation water content. The conventional approach for deriving representative footprint-scale values of these parameters is to simply average the corresponding parameters of high-resolution pixels over the entire radiometer footprint.  However, previous work has shown that simple averaging of ancillary parameter values may result in biased soil moisture retrievals from space-borne radiometers.  This study uses an Observing System Simulation Experiment (OSSE) framework to demonstrate that ancillary vegetation water content or canopy opacity parameter values obtained with an alternative aggregation rule may significantly reduce the magnitude of soil moisture retrieval errors for highly heterogeneous vegetated areas.


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