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

Wednesday, 25 January 2012: 9:00 AM
SMOS OBSERVATIONS to Evaluate SMAP SOIL Moisture Algorithms
Room 350/351 (New Orleans Convention Center )
Rajat Bindlish, USDA, Beltsville, MD; and T. J. Jackson, M. Cosh, T. Zhao, S. Chan, P. O'Neill, E. Njoku, A. Colliander, and Y. Kerr

One of the products of the proposed SMAP (Soil Moisture Active Passive) mission is soil moisture at a 36 km resolution based solely on the passive microwave radiometer measurements. In this paper we contribute to the development of this level 2 radiometer-only soil moisture algorithm by exploiting the data available from the Soil Moisture Ocean Salinity (SMOS) satellite. SMOS brightness temperatures provide a global real-world, rather than simulated, input for evaluating the radiometer-only soil moisture algorithm alternatives. Here, microwave observations from the SMOS mission are reprocessed to simulate SMAP observations at a constant incidence angle of 40 degrees. This provides a brightness temperature data set that closely matches the observations that would be provided by the SMAP radiometer. This SMOS/SMAP data is then used to evaluate the different pre-launch SMAP soil moisture algorithm options.

Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. These include the (a) Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected for the effects of temperature, vegetation (ancillary data base derived from MODIS data), roughness and soil texture (static ancillary data sets). (b) Land Parameter Retrieval Model (LPRM), which is a two-parameter retrieval model (soil moisture and vegetation water content) based on a microwave radiative transfer model. It uses the microwave polarization difference index at 1.4 GHz and emissivity to parameterize vegetation water content and estimate soil moisture. (c) Dual Channel Algorithm, which uses both polarizations to iteratively solve for soil moisture and vegetation water content.

The planned analyses will also aid in the development and selection of the different land surface parameters (roughness and vegetation parameters) and ancillary data sets needed in the soil moisture algorithm. The ancillary datasets required are dependent on the choice of the soil moisture algorithm. For example, the SCA might use (a) SMOS estimated vegetation optical depth, (b) MODIS-based vegetation climatology data, or (c) actual real-time MODIS observations. Soil moisture observations from a set of four watersheds in the U.S. were used to evaluate the soil moisture estimates from the different methodologies.

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