92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012: 9:45 AM
Aqua AMSR-E Soil Moisture Retrieval: Evaluation and Potential Algorithm Improvement
Room 350/351 (New Orleans Convention Center )
Iliana E. Mladenova, USDA/ARS, Beltsville, MD; and T. J. Jackson, R. Bindlish, M. Cosh, E. Njoku, and S. Chan

Global estimates of soil moisture derived from the Advanced Microwave Scanning Radiometer on Aqua (AMSR-E) have been an invaluable resource over the past decade for a broad spectrum of research and applications that include global hydrology, agriculture, and climate and weather forecasting. NASA, as well as other agencies and groups routinely generate daily products; however, there has been no general consensus on the algorithm used and in the case of the NASA soil moisture product a number of investigators have observed that it has bias and does not display the full range of soil moisture conditions. Considering these issues, a re-evaluation of the approach used for the NASA standard products is being conducted.

As noted above, in addition to the NASA standard product, there are several well established and validated passive microwave-based soil moisture retrieval techniques. Even though all of the available algorithms are based on the same radiative transfer model (tau-omega model), there are significant differences in the assumptions, ancillary data sets, screening criteria and correction techniques. As part of our effort to improve the NASA standard product, we will carefully examine each of the alternative algorithms/products to better understand what elements of each might be combined in a new approach. A total of eight algorithm/products are currently being assembled and implemented so that they can run under the same conditions.

In order to understand why these techniques differ in performance, it is necessary that we [1] gain an in-depth knowledge of the differences between the theoretical basis of the algorithms, [2] analyze the impact of the assumptions and simplifications made in deriving these solutions, and [3] assess algorithm sensitivity to input data, parameters and modules (i.e. roughness/temperature correction, dielectric model, etc.). Thus, in addition to improving the baseline algorithm, this gives the opportunity to better understand the merits of the existing approaches as well as develop and test validation techniques. Hopefully these efforts will lead to establishments of a more robust and transferable algorithm applicable to multiple instruments and platforms. In this presentation we will present an overview of the algorithms selected and preliminary results of inter-comparisons.

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