H43H
Remote Sensing and Modeling of the Terrestrial Water Cycle II Posters

Thursday, 17 December 2015: 13:40-18:00
Poster Hall (Moscone South)
Primary Conveners:  Iliana E Mladenova, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Conveners:  Venkataraman Lakshmi, Univ South Carolina, Columbia, SC, United States
Chairs:  Iliana E Mladenova, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Venkataraman Lakshmi, Univ South Carolina, Columbia, SC, United States
OSPA Liaisons:  Iliana E Mladenova, NASA Goddard Space Flight Center, Greenbelt, MD, United States
 
Field-scale land surface modeling over continental extents: Applications in satellite remote sensing of soil moisture (85792)
Nathaniel Chaney, Princeton University, Princeton, NJ, United States
 
Intercomparison of AMSR2 and AMSR-E Soil Moisture Retrievals with MERRA-L data set over Australia (62330)
Eunsang Cho1, Minha Choi2, Chun-Hsu Su3, Dongryeol Ryu3, Hyunglok Kim4 and Jennifer M Jacobs5, (1)Hanyang University, Seoul, South Korea, (2)Organization Not Listed, Washington, DC, United States, (3)University of Melbourne, Parkville, VIC, Australia, (4)Sungkyunkwan University, Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Suwon, Gyeonggi-do, South Korea, (5)Univ New Hampshire, Durham, NH, United States
 
Remote Sensing of Soil Moisture based on Dynamic Vegetation Scattering Properties for AMSR sensors (71482)
Jinyang Du1, John S Kimball2 and Lucas A Jones2, (1)University of Montana, Numerical Terradynamic Simulation Group, Missoula, MT, United States, (2)University of Montana, Numerical Terradynamic Simulation Group, College of Forestry & Conservation, Missoula, MT, United States
 
Comparing and Combining Surface Soil Moisture Products from AMSR2 (75907)
Robert Parinussa1, Seokhyeon Kim1, Yi Liu1, Fiona Johnson1 and Ashish Sharma2, (1)University of New South Wales, Sydney, NSW, Australia, (2)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia
 
SMAP L2/L3 Soil Moisture Product Validation using In Situ Based Core Validation Sites (62061)
Andreas Colliander1, Thomas J Jackson2, Steven Chan1, Narendra N Das1, Seungbum Kim1, R. Scott Dunbar1, Rajat Bindlish2, Lan B Dang1, Aaron A Berg3, Tracy L Rowlandson3, Kelly K Caylor4, Michael H Cosh2, Hala K AlJassar5, Ernesto Lopez-baeza6, Jose Martínez-Fernández7, Angel Gonzales-Zamora7, Heather McNairn8, Anna M Pacheco9, Mahta Moghaddam10, Carsten Montzka11, Claudia Notarnicola12, Georg Niedrist12, Thierry Pellarin13, Jouni Pulliainen14, Kimmo Rautiainen14, Judith Ramos15, Mark S Seyfried16, Zhongbo Su17, Yijian Zeng17, Rogier Van der Velde17, Marouane Temimi18, Marc Thibeault19, Wouter Dorigo20, Mariette Vreugdenhil20, Jeffrey Walker21, Xiaoling Wu21, Todd G Caldwell22, Michael Spencer1, Peggy E O'Neill23, Dara Entekhabi24, Simon H Yueh1 and Eni G Njoku1, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (2)USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States, (3)University of Guelph, Guelph, ON, Canada, (4)Princeton University, Princeton, NJ, United States, (5)Kuwait University, Physics Department, Kuwait, Kuwait, (6)University of Valencia, Valencia, Spain, (7)University of Salamanca, Salamanca, Spain, (8)Agriculture and Agri-Food Canada, Science and Technology Branch, Ottawa, ON, Canada, (9)Agriculture and Agri-Food Canada (AAFC), Ottawa, Canada, (10)University of Southern California, Electrical Engineering, Los Angeles, CA, United States, (11)Forschungszentrum Jülich, Jülich, Germany, (12)EUR.AC, Institute for Applied Remote Sensing, Bozen/Bolzano, Italy, (13)LTHE Laboratoire d'étude des Transferts en Hydrologie et Environnement, Saint Martin d'Hères, France, (14)Finnish Meteorological Institute, Helsinki, Finland, (15)Universidad Nacional Autónoma de México, Mexico City, Mexico, (16)USDA - ARS, Northwest Watershed Research Center, Boise, ID, United States, (17)University of Twente, Enschede, Netherlands, (18)NOAA-CREST/City College, CUNY, New York, NY, United States, (19)CONAE, Buenos Aires, Argentina, (20)Vienna University of Technology, Vienna, Austria, (21)Monash University, Melbourne, Australia, (22)University of Texas at Austin, Austin, TX, United States, (23)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (24)Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States
 
Land Surface Modeling at Hyper-Resolution in the Context of SMAP Cal/Val (60334)
Camille Garnaud, Environment Canada Dorval, Dorval, QC, Canada and Stéphane Bélair, Environment Canada, Dorval, QC, Canada
 
Global Evaporation Estimates from SMAP Passive Microwave Soil Moisture Retrievals Using Conditional Sampling.   (81660)
Mariette Vreugdenhil1, Dara Entekhabi2, Alexandra G Konings3,4, Guido Salvucci5 and Patrick Hogan1, (1)Vienna University of Technology, Vienna, Austria, (2)Massachusetts Institute of Technology, CEE, Cambridge, MA, United States, (3)Stanford University, Stanford, CA, United States, (4)Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States, (5)Boston University, Earth and Environment, Boston, MA, United States
 
SMOS Instrument Performance and Calibration After 6 Years in Orbit (74595)
Roger Oliva, European Space Agency, Villanueva De La Can, Spain
 
Comparison of Passive and Active Remotely Sensed Microwave Soil Moisture Retrievals using Soil Moisture Simulations (GLDAS) over Different Land Covers in East Asia: using SMOS, ASCAT, AMSR2, and FY-3B (66776)
Hyunglok Kim, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea and Minha Choi, Sungkyunkwan University, Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Suwon, South Korea
 
Enhancement of Passive Microwave Soil Moisture Retrievals using Visible/Infrared Imager (70376)
David Truesdale1, Li Li1, Jeffrey H Bowles2, Bo-Cai Gao2 and Gia Lamela2, (1)US Naval Research Laboratory, Washington, DC, United States, (2)Naval Research Lab DC, Remote Sensing, Washington, DC, United States
 
Spatiotemporal analysis of soil moisture in using active and passive remotely sensed data and ground observations (59355)
Huixuan Li, University of South Carolina Columbia, Columbia, SC, United States, Bin Fang, Columbia University of New York, Palisades, NY, United States and Venkataraman Lakshmi, Professor, Earth and Ocean Sciences, Columbia, SC, United States
 
Towards constraining hydrologic models using satellite retrieved soil moisture (67066)
Matthias Zink, Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany
 
Use of a Rainfall Runoff Model and Satellite Data Sets for Hydrological Studies of the Upper Contas Watershed, Brazil (59698)
Tainá Martins Cunha1, Vitor Rebello1, Otto Corrêa Rotunno Filho1, Maria Claudia Barbosa1, Mariza Ramalho Franklin2,3 and Venkataraman Lakshmi4,5, (1)UFRJ Federal University of Rio de Janeiro, Rio De Janeiro, Brazil, (2)CNEN National Nuclear Energy Commission, Rio de Janeiro, Brazil, (3)Institute for Radiation Protection and Dosimetry, Rio de Janeiro, Brazil, (4)University of South Carolina Columbia, Columbia, SC, United States, (5)Professor, Earth and Ocean Sciences, Columbia, SC, United States
 
Downscaling Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-based Sensors and an Ensemble Kalman Filter (86664)
Douglas C Baldwin, Pennsylvania State University Main Campus, University Park, PA, United States
 
Three dimensional prediction of soil moisture at any support using multiple datasets with different spatial supports (78584)
Thomas Bishop, Niranjan Wimalathunge and Thomas G Orton, University of Sydney, Sydney, Australia
 
The Evaluation of an Integrated Land Surface – Groundwater Model Through Remote Sensing (76341)
Yi Liu1, Robert Parinussa1, Hoori Ajami2, Jason Peter Evans3, Matthew F McCabe4 and Ashish Sharma2, (1)University of New South Wales, Sydney, NSW, Australia, (2)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (3)University of New South Wales, Sydney, Australia, (4)King Abdullah University of Science and Technology, Environmental Science and Engineering, Thuwal, Saudi Arabia
 
Prediction of Root Zone Soil Moisture using Remote Sensing Products and In-Situ Observation under Climate Change Scenario (72472)
Gurjeet Singh, Ph.D. Scholar, School of Earth, Ocean and Climate Sciences, Bhubaneswar, India, Rabindra Kumar Panda, Proffessor, School of Earth, Ocean and Climate Sciences, Bhubaneswar, India and Binayak Mohanty, Texas A&M University, College Station, TX, United States
 
Calibration of Noah soil hydraulic property parameters using surface soil moisture from SMOS and basin-wide in situ observations (66644)
Peter J Shellito, University of Colorado at Boulder, Boulder, CO, United States, Eric E Small, Univ of Colorado Boulder, Boulder, CO, United States and Michael H Cosh, USDA Agricultural Research Service New England Plant, Soil and Water Research Laboratory, East Wareham, MA, United States
 
Groundwater Management using Remotely Sensed Data in High Plains Aquifer (64797)
Davood Ghasemian, C Larrabee Winter and David P Guertin, University of Arizona, Tucson, AZ, United States
 
Hydraulic head levels and aquifer parameters inferred from a joint analysis of InSAR and well data in the San Luis Valley, Colorado (62907)
Jingyi Chen1, Rosemary J Knight1 and Howard A Zebker2, (1)Stanford University, Stanford, CA, United States, (2)Stanford University, Geophysics, Stanford, CA, United States
 
Diurnal emissivity dynamics in bare versus biocrusted sand dunes (62804)
Offer Rozenstein, McGill University, Bioresource Engineering, Montreal, QC, Canada
 
Characterizing hydrologic changes of Great Dismal Swamp using SAR/InSAR technology (79706)
Jin Woo Kim1, Zhong Lu1 and Zhiliang Zhu2, (1)Southern Methodist University, Dallas, TX, United States, (2)USGS, Reston, VA, United States
 
Validation of Large-Scale Geophysical Estimates Using In Situ Measurements with Representativeness Error (76449)
Alexandra G Konings1,2, Alexander Gruber3, Kaighin A Mccoll1, Seyed Hamed Alemohammad1 and Dara Entekhabi4, (1)Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States, (2)Stanford University, Stanford, CA, United States, (3)Vienna University of Technology, Vienna, Austria, (4)Massachusetts Institute of Technology, CEE, Cambridge, MA, United States
 
Back to the Future: Have Remotely Sensed Digital Elevation Models Improved Hydrological Parameter Extraction? (86772)
Ben (Abdollah) Jarihani, University of Queensland, St Lucia, QLD, Australia
 
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