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Husayn El Sharif, Jingfeng Wang, and Aris P. Georgakakos

algorithm by which daily SMAP-type top soil moisture data can be assimilated into the DSSAT-CSM for modeling of crop yield and irrigation amount at the ~10-km spatial scale and 2) reduce the uncertainty in the forecast of crop yield and irrigation demand by combining SMAP-type remotely sensed soil moisture data with other weather measurement data products. d. Outline The article is organized as follows. Section 1 introduces the value of precision agriculture models—models designed to explore site

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Sujay V. Kumar, Kenneth W. Harrison, Christa D. Peters-Lidard, Joseph A. Santanello Jr., and Dalia Kirschbaum

algorithms that are focused on generating improved estimates of hydrologic model states ( Kumar et al. 2008 ). More recently, the LIS-DA subsystem was enhanced through the incorporation of a suite of radiative transfer models (LIS-RTM) that enables the direct use of raw satellite observations for data assimilation. The optimization (LIS-OPT; Kumar et al. 2012 ) and uncertainty estimation (LIS-UE; Harrison et al. 2012 ) subsystems help in improving the representation of model parameters and for

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M. Susan Moran, Bradley Doorn, Vanessa Escobar, and Molly E. Brown

), so the SMAP hardware, algorithms, and products were clearly defined. The algorithm theoretical basis documents (ATBDs) were drafted, and the SMAP Science Data System (SDS) test bed was in place to make high-fidelity simulated data products available (listed in Table 1 ). The Alaska Satellite Facility (ASF) and the National Snow and Ice Data Center (NSIDC) DAACs had been selected for the SMAP mission and were preparing for SMAP product visualization and access. Some SMAP-sponsored field campaigns

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Fan Chen, Wade T. Crow, and Dongryeol Ryu

overpass) and 2130 (ascending overpass) local solar time. ASCAT surface soil moisture retrievals on 0.25° spatial grid were obtained from Naeimi et al. (2009) by applying the Water Retrieval Package 5 (WARP5) algorithm. For details regarding retrieval of ASCAT surface soil moisture data, refer to Wagner et al. (1999) and Naeimi et al. (2009) . Retrievals of ascending and descending overpasses are combined into a daily dataset. In cases where both ascending and descending retrievals for a grid cell

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Catherine Champagne, Andrew Davidson, Patrick Cherneski, Jessika L’Heureux, and Trevor Hadwen

moisture extremes has been done through in situ monitoring networks ( Keyantash and Dracup 2004 ; Nandintsetseg and Shinoda 2013 ; Sridhar et al. 2008 ; Vicente-Serrano 2006 ) and land surface models ( Sheffield and Wood 2007 ). Progress in the development of satellite technology and retrieval algorithms for quantifying soil moisture from active and passive microwave satellite platforms has provided a new suite of tools to the risk assessment community that are now able to provide near

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Susan Frankenstein, Maria Stevens, and Constance Scott

first calculates the maximum speed across a terrain unit for each factor (slope, vegetation, surface roughness, soil strength, etc.) and then takes the minimum of these as the allowable speed. These speeds can then be linked using secondary algorithms ( Haley et al. 1979 ). b. Soil strength One of the many terrain factors needed to determine vehicle speed is soil strength. For vehicle mobility modeling, the soil strength is quantified using either the cone index (CI) or rating cone index (RCI), both

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C. Albergel, W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner

vegetation, (ii) frozen soils, (iii) snow cover, (iv) nonconvergence of the algorithm, and (v) complex topography. Only significant trends ( p = 0.05) are shown, and 34% and 52% of ERA-Land and MERRA-Land grid cells present significant trends, respectively. Both wet (positive) and dry (negative) trends occur. ERA-Land is dominated by decreasing soil moisture content over time; 72% of all significant trends ( Fig. 6a ) are drying trends (negative values). However, MERRA-Land behaves differently: only 41

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Amy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, and James P. Verdin

West Africa . Agric. For. Meteor. , 180 , 118 – 131 , doi: 10.1016/j.agrformet.2013.05.010 . Reichle, R. , Crow W. , Koster R. , Kimball J. , and Lannoy G. D. , 2012 : SMAP level 4 surface and root zone soil moisture (L4_SM) data product, initial release v.1. Algorithm Theoretical Basis Doc., Jet Propulsion Laboratory, Pasadena, CA, 77 pp. [Available online at .] Reynolds, C. A. , Jackson T. J. , and Rawls W. J. , 2000

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

assimilated within CaLDAS for an improved specification of the soil moisture state. e. Assimilation algorithm As part of the early development of CaLDAS, a simplified two-dimensional variational assimilation scheme for the assimilation of passive microwave L-band data was developed and tested ( Balsamo et al. 2006 , 2007 ). For consistency with the model development directions at EC ( Lavaysse et al. 2013 ; Houtekamer et al. 2014 ), the ensemble Kalman filter (EnKF) assimilation technique was chosen for

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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

sensors. In the second analysis, we examined how streamflow forecast skill is connected to the information content of subgrid precipitation distributions, as might be established with a downscaling algorithm applied to the output of global seasonal forecast systems. Here we found that this information content would have little impact on streamflow prediction in the eastern half of CONUS, apparently because of the fact that this region is not characterized by soil moisture–limited evaporation. In

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