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Title:   Author  item
                                            BOLTEN, J. - National Aeronautics And Space Administration (NASA)item
                                            MLADENOVA, I. - National Aeronautics And Space Administration (NASA)item
                                            Crow, Wadeitem
                                            DE JU, R.A.M - Collaborator
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 7/1/2015
                                    Publication  Date: N/A
                                    Citation: N/AInterpretive Summary:
                                Technical Abstract: A primary operational goal of the United States Department of Agricultural (USDA) is to improve foreign market access for U.S. agricultural products. To this end, USDA operates programs designed to build new markets and improve the competitive position of U.S. agriculture inthe global marketplace. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis performedby USDA’s International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS). This analysis is used to enhance the accuracy and reliability of foreign crop
production, area, and yield forecasts during the growing season. One critical concern for analysts is capturing the impact of agricultural drought (i.e. the lack of root-zone soil moisture) on crop health and eventual yield. Baseline soil moisture estimates within the CADRE system are currently based on output from a modified Palmer two-layer soil moisture accounting model derived in the late-1970s, and an updated product assimilating near-real time observations of soil moisture from Advanced Microwave Scanning Radiometer (AMSR-E), and the Soil Moisture Ocean Salinity (SMOS) satellite (Bolten et al., 2009). Input data required by the modified-Palmer two-layer soil moisture includes daily ecipitation estimates and daily minimum/maximum temperature measurements, observations from SMOS. Precipitation and temperature data are based on ground meteorological station measurements from the World Meteorological Organization (WMO), and gridded weather data from the US Air Force Weather Agency (AFWA).

In this study, we explore the application of vegetation optical depth estimates from the Land Parameter Retrieval Model (LPRM) - a radiative transfer-based approach to derive global land surface moisture and vegetation optical depth from satellite observations of microwave brightness temperature (Owe et al., 2001; De Jeu and Owe, 2003). Polarization ratios, such as the Microwave polarization Difference Index (MPDI), are frequently used to remove the temperature dependence, resulting in a parameter that is quantitatively and more highly related to the dielectric properties of the emitting surface(s). The MPDI is mainly a function of the overlying vegetation, and consequently a good indicator of the canopy density. By applying LPRM-based optical depth estimates, we dynamically adjust the AMSR-E observation error at a temporal and spatial scale consistent with the native soil moisture retrievals. Surface soil moisture retrievals are obtained from gridded 0.25 (LPRM) products provided by VU University Amsterdam based on Advanced Microwave Scanning Radiometer-EOS (AMSRE) brightness temperature products [Njoku et al , 2003] between June 2002 and October 2011 [de Jeu , 2003]. The effective measurement depth of LPRM surface soil moisture retrievals is estimated to be 1– 2 cm. For the purposes of this analysis, we assume these retrievals reflect the equivalent soil moisture estimated in the surface layer of the 2-Layer Palmer model.

Results demonstrate the potential of assimilating the LPRM-AMSR-E and future Soil Moisture Active Passive (SMAP) surface soil moisture and vegetation optical depth retrievals for substantially improving the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the where high-quality rainfall observations are unavailable.


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