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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Using SMOS observations in the development of the SMAP level 4 surface and root-zone soil moisture project

Authors
item Reichle, Rolf -
item DE Lannoy, Gabrielle -
item Crow, Wade
item Koster, Randal -
item Kimball, John -

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: May 25, 2011
Publication Date: N/A

Technical Abstract: The Soil Moisture and Ocean Salinity (SMOS; [1]) mission was launched by ESA in November 2009 and has since been observing L-band (1.4 GHz) upwelling passive microwaves. Along with these brightness temperature observations, ESA also disseminates retrievals of surface soil moisture that are derived from the SMOS brightness temperature and ancillary data. The Soil Moisture Active and Passive (SMAP; [2]) mission is being developed by NASA for launch in 2014. The primary science objectives of SMAP are to enhance understanding of land surface controls on the water, energy and carbon cycles, and to determine their linkages. Moreover, the high-resolution soil moisture mapping provided by SMAP has practical applications in weather and seasonal climate prediction, agriculture, human health, drought and flood decision support. In this paper we describe our use of SMOS observations in the development of the planned SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) product [3]. SMAP and SMOS directly observe soil moisture in the top 5 cm of the soil column only. Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (~top 1 m of the soil column), which is not directly measured by SMAP. The foremost objective of the SMAP L4_SM product [3] is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a soil moisture data assimilation system. The land surface model component of the assimilation system is driven with observations-based surface meteorological forcing data, including precipitation [4], which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. Finally, the model interpolates and extrapolates SMAP observations in time and in space. The L4_SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on SMAP observations and complementary information from a variety of sources. The assimilation algorithm considers the respective uncertainties of each component and yields a product that is superior to satellite or model data alone. Error estimates for the L4_SM product are generated as a by-product of the data assimilation system. The present paper will focus on the use of SMOS brightness temperature observations and soil moisture retrievals for the development of the SMAP L4_SM product. We compare SMOS brightness temperatures and SMOS soil moisture retrievals with corresponding estimates from the L4_SM modeling system and in situ observations over the U.S. Figure 1 shows a representative example of surface soil moisture from the L4_SM modeling system and SMOS retrievals over the southern United States. While the overall pattern of dry conditions in the West and wet conditions in the East is similar, there is clearly a bias between the model and SMOS estimates. We also describe the procedure for assimilating the SMOS and SMAP brightness temperature observations for the generation of the planned SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) product. [1] Kerr, Y., P., and Coauthors, The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5), 666-687, 2010. [2] Entekhabi, D., and Coauthors, The Soil Moisture Active and Passive (SMAP) Mission, Proceedings of the IEEE, 98, 704-716, doi:10.1109/JPROC.2010.2043918, 2010. [3] Reichle, R. H., W. T. Crow, R. D. Koster, and J. Kimball, The SMAP Level 4 Surface and Root-zone Soil Moisture Product, Soil Moisture Active Passive (SMAP) mission Algorithm Theoretical Basis Document, 2010. [4] Liu, Q., R. H. Reichle, R. Bindlish, M. H. Cosh, W. T. Crow, R. de Jeu, G. J. M. De Lannoy, G. J. Huffman, and T. J. Jackson, The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system, Journal of Hydrometeorology, submitted, 2010.

Last Modified: 11/26/2014
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