H109
Ultra-high resolution near surface soil moisture products for hydrology, agriculture, and hazard applications
Session ID#: 161809
Session Description:
Existing sources of near surface soil moisture information have spatial resolutions of km to tens of km from remote sensing and hydrological models. Recent advances in algorithms and sensing technologies allowed the products with tens to hundreds meter resolution. While not feasible in the past, the advances enable the applications to stream-flow analysis in watersheds, arctic hydrology, field-scale agriculture and food security, irrigation monitoring, and hazards to infrastructure such as landslides and leaks from water conveyance canals. This session reports the advances in remote-sensing and modeling methods to achieve the ultra-high resolution products, and in applications to hydrology, earth surface processes, and hazard studies. We invite contributions utilizing airborne (e.g., UAVSAR, ASAR, F-SAR), spaceborne (e.g., SMAP, Sentinel1, NISAR), and numerical model resources, at IR, TIR, microwave frequencies, in novel applications benefiting from the ultra-high spatial resolution.
Index Terms:
1860 Streamflow [HYDROLOGY]
1866 Soil moisture [HYDROLOGY]
4303 Hydrological [NATURAL HAZARDS]
Primary Convener: Seungbum Kim, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Conveners: Eric Jameson Fielding, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States and Narendra N Das, Michigan State University, Biosystem and Agricultural Engineering, East Lansing, United States
Evaluation of Satellite-Based Soil Moisture Products using the Landslide Hazard Assessment for Situational Awareness (LHASA) Model for Landslide Prediction and Hazard Analysis in the Lower Mekong River Basin (1063270)
Chelsea Dandridge, University of Virginia, Engineering Systems and Environment, Charlottesville, VA, United States, Thomas Stanley, University of Maryland Baltimore County, GESTAR-II, Baltimore, United States, Nishan Kumar Biswas, NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, MD, United States, Venkataraman (Venkat) Lakshmi, University of Virginia, Engineering Systems and Environment, Charlottesville, United States and Dalia Kirschbaum, NASA Goddard Space Flight Center, Earth Sciences Division, Greenbelt, United States
A data-driven snapshot algorithm for high-resolution soil moisture retrievals for the upcoming NISAR mission (1065915)
Preet LAL1, Gurjeet Singh1, Narendra N Das1 and Dara Entekhabi2, (1)Michigan State University, Civil and Environmental Engineering, East Lansing, United States, (2)Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, United States
Low-Altitude Airborne SAR Radiometric Calibration for Soil Moisture Retrieval in Forest Flows (1092502)
Yuhuan Zhao1, Xiaoyou Lin2, Delwyn Moller2, Mahta Moghaddam1 and Dean Meason3, (1)University of Southern California, Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, CA, United States, (2)Department of Electrical Computer & Software Engineering, The University of Auckland, Auckland, New Zealand, (3)Scion Research, Rotorua, Bay of Plenty, NEW ZEALAND
Impact of Spatial Heterogeneity in Downscaling Microwave Sensor Based Coarse Soil Moisture to Ultra-high Resolution (1102957)
Juby Thomas1, Manika Gupta1, Damini Sarma2, Prashant K Srivastava3 and Dharmendra Kumar Pandey4, (1)University of Delhi, Department of Geology, Delhi, India, (2)Birla Institute of Technology, Mesra, India, (3)Banaras Hindu University, Varanasi, India, (4)Space Applications Centre - ISRO, Ahmedabad, India
High-resolution soil moisture: downscaling of physics-based modeling estimates (1131385)
Elena Leonarduzzi, Princeton University, High Meadows Environmental Institute, Princeton, NJ, United States and Reed M Maxwell, Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States
Combining UAVSAR Observations from Multiple Incidence Angles for Retrieval of Surface Soil Moisture Over Corn and Soybean Fields (1132357)
Ponnurangam Gramani Ganesan, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadna, CA, UNITED STATES, Seungbum Kim, Jet Propulsion Lab,California Institute of Technology, Pasadena, CA, United States, Michele L. Reba, USDA, Agricultural Research Service, Jonesboro, AR, United States, Yang Zheng, RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, Tien-Hao Liao, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Michael H Cosh, U. S. Dept. of Agriculture, Beltsville, MD, United States
High resolution soil moisture estimation considering complex vegetated areas with modified Water Cloud Model (1144972)
Seongkeun Cho, Sungkyunkwan University, Environment and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Suwon, Gyeonggi-do, Korea, Republic of (South), Seulchan Lee, Sungkyunkwan University, Department of water resources, Suwon, Gyeonggi-do, Korea, Republic of (South), Seungcheol Oh, Sungkyunkwan University, Suwon, South Korea and Minha Choi, Sungkyunkwan University, School of Civil, Architecture Engineering & Landscape Architecture, Suwon, Gyeonggi-do, South Korea
Machine Learning Based High Resolution Soil Moisture (ML-HRSM) for Identifying Prevent Planting Fields (1165354)
Zhengwei Yang, USDA National Agricultural Statistics Service, Research and Development Division, Washington, United States, Patrick Willis, USDA National Agricultural Statistics Service, Research and Development Division, Washington, VA, United States, Jingyi Huang, University of Wisconsin Madison, Soil Science, Madison, WI, United States and Zhou Zhang, University of Wisconsin-Madison, Biological Systems Engineering, Madison, United States
HIGH SPATIAL RESOLUTION SOIL MOISTURE DATA USINGL-BAND RADIOMETER AND SMALL UNMANNED AERIAL SYSTEMS (1168144)
Eryan Dai, Orbital Micro Systems, Inc., Boulder, CO, United States, Albin John Gasiewski, Univ of Colorado, Boulder, CO, United States, Jack Steward Elston, Black Swift Technologies, Boulder, CO, United States, Maciej Stachura, Black Swift Technologies, Boulder, United States and Michael Hurowitz, Orbital Micro Systems, Inc, Boulder, CO, United States
Monitoring Soil Moisture Changes Using Multi-band SAR Over Landslide Regions in California (1170985)
Eric Jameson Fielding, Jet Propulsion Laboratory, California Institute of Technology, Earth Science, Pasadena, CA, United States, Tien-Hao Liao, California Institute of Technology, Pasadena, United States, Seungbum Kim, Jet Propulsion Lab,California Institute of Technology, Pasadena, CA, United States, Alexander L Handwerger, JIFRESSE UCLA and Jet Propulsion Lab, Los Angeles, United States, Michael Cosh, USDA ARS, Baltimore, United States, Richard H Chen, NASA Jet Propulsion Laboratory, Pasadena, CA, United States, Noah J Finnegan, University of California - Santa Cruz, Department of Earth and Planetary Sciences, Santa Cruz, CA, United States and Yunling Lou, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
A Multi-scale Soil Moisture and Temperature Observation Network over Cropland in Central Illinois, U.S. (1173767)
Bin Peng, University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, Yi Yang, University of Illinois at Urbana Champaign, Urbana, IL, United States, Kaiyu Guan, University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States, Ming Pan, Center for Western Weather and Water Extremes, San Diego, United States and Michael Cosh, USDA ARS, Baltimore, United States
Global NISAR Soil Moisture Retrievals using a Time-Series Ratio Algorithm (1177566)
Jeonghwan Park1,2, Rajat Bindlish3, Dustin Horton4 and Joel Johnson4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Global Science and Technology Inc, Greenbelt, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)Ohio State University, Department of Electrical and Computer Engineering, Columbus, United States
Landcover Classification Using Radar Observations from the SMEX02 Campaign (1179575)
Dustin Horton1,2, Joel Johnson2, Adithya Sriram Damodaran2, Jeonghwan Park3,4 and Rajat Bindlish5, (1)ElectroScience Laboratory, The Ohio State University, Columbus, United States, (2)Ohio State University, Department of Electrical and Computer Engineering, Columbus, United States, (3)Global Science and Technology Inc, Greenbelt, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, United States
Estimating Soil Moisture Content in Antarctic Soils Using High-Resolution Remote Sensing Data (1180070)
Mark R Salvatore1, John E Barrett2, Laura Fackrell3, Eric Sokol4, Joseph Levy5, Lily Kuentz5, Michael N Gooseff6, Byron Adams7, Haley Matul8, Brian Szutu8, Paul Knightly3 and Peter T Doran9, (1)Northern Arizona University, Astronomy and Planetary Science, Flagstaff, AZ, United States, (2)Virginia Tech, Biological Sciences Department, Blacksburg, VA, United States, (3)Northern Arizona University, Flagstaff, United States, (4)Battelle, National Ecological Observatory Network, Boulder, CO, United States, (5)Colgate University, Hamilton, NY, United States, (6)University of Colorado Boulder, Civil, Environmental, and Architectural Engineering, Boulder, United States, (7)Brigham Young University, Provo, UT, United States, (8)Northern Arizona University, Flagstaff, AZ, United States, (9)Boulder, CO, United States
