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Session Proposal: Advances in innovative soil moisture monitoring across hierarchical scales (AGU25)

H012
Advances in innovative soil moisture monitoring across hierarchical scales

Session ID#: 252474

Session Description:
This session aims to highlight advancements and alternative techniques in soil moisture monitoring from point-based sensors to large-area remote sensing, with a focus on proximal sensing technologies capable of bridging the scale gap. We welcome contributions that discuss the integration of novel technologies like for example Cosmic Ray Neutron Sensors (CRNS), Gamma-Ray Spectrometry (GRS), Distributed Acoustic Sensing (DAS), Distributed Temperature Sensing (DTS), Global Navigation Satellite System Reflectometry (GNSS-R), geophysical methods. Topics of interest include, but are not limited to soil moisture monitoring for applications in agriculture, hydrology, natural hazards early warning, environmental monitoring, even planetary space missions. A central objective of this session is to stimulate a discussion on the potential offered by integrated soil moisture monitoring at hierarchical space-time scales.
Index Terms:

1842 Irrigation [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1866 Soil moisture [HYDROLOGY]
Primary Convener:  Andre Daccache, University of California Davis, Davis, United States
Conveners:  Nunzio Romano, University of Naples Federico ll, Department of Agricultural Sciences - Division of Agricultural, Forest and Biosystems Engineering, Portici (Napoli), Italy, Trenton E Franz, University of Nebraska Lincoln, School of Natural Resources, Lincoln, United States and Michael H Cosh, U. S. Department of Agriculture, Beltsville, United States
Student/Early Career Convener:  Laura Almendra Martin, University of Florida, Center for Remote Sensing, Agricultural and Biological Engineering, Ft Walton Beach, FL, United States
 
Bridging the Soil Moisture Monitoring Scale Gap: An Evaluation of Cosmic Ray Neutron Detectors (CRNDs) Using Point Sensors, Neutron Probes, and Gravimetric Data (1865705)
Seyed Ali Azizi, Texas A&M University College Station, Soil and Crop Sciences, College Station, TX, United States, Briana Michele Wyatt, Oklahoma State University Main Campus, Stillwater, United States, Andres Patrignani, Kansas State University, Manhattan, United States, Michael H Cosh, U. S. Department of Agriculture, Beltsville, United States and Tyson E. Ochsner, Oklahoma State University, Plant and Soil Sciences, Stillwater, United States
 
Assessment and Forecasting of Topsoil Water Regimes Using Cosmic‑Ray Neutron Sensors (CRNS) and TDR: A Case Study in Ireland (1867606)
Konstantin Shishkin1, Owen Fenton1, Paul Murphy2, Tamara Hochstrasser2 and Klara Finkele3, (1)Teagasc, Wexford, Ireland, (2)University College Dublin, School of Agriculture, Dublin, Ireland, (3)Met Éireann - The Irish Meteorological Service, Dublin, Ireland
 
Accuracy of soil moisture estimates from gamma radiation: comparison of bulk and energy-resolved measurements (1872258)
Sonia Akter, Johan Alexander Huisman and Heye R Bogena, Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
 
Establishing the Irish Soil Moisture Observation Network (ISMON) through Inter-Institutional Collaboration (1874047)
Klara Finkele1, Michael P Bruen2, Kenneth A Byrne3, Thomas Cummins4, Eve Daly5, Declan T. Delaney6, Rowan Fealy7, Owen Fenton8, Padraig Flattery1, Stuart Green8, Suzanne Higgins9, Natalya Hunter Williams10, Caren Jarmain11, Paul M. Johnston12, Haleh Karbala Ali1, Gary Lanigan13, Tim McCarthy14, Ted McCormack10, Per-Erik Mellander8, Paul Murphy15, Oliver Nicholson16, Ciaran Nugent17, Karl Richards8, Matthew John Saunders18, Brian Tobin4, Patrick Tuohy19, Saji Varghese1 and Tamara Hochstrasser15, (1)Met Éireann - The Irish Meteorological Service, Dublin, Ireland, (2)Dooge Centre for Water Resources Research, School of Civil Engineering, University College Dublin, Dublin, Ireland, (3)University of Limerick, Department of Biological Sciences & Bernal Institute, Faculty of Science and Engineering, Limerick, Ireland, (4)University College Dublin, School of Agriculture and Food Science, Dublin, Ireland, (5)National University of Ireland, Galway, Galway, Ireland, (6)University College Dublin, School of Electrical and Electronic Engineering, Dublin, Ireland, (7)Maynooth University, Department of Geography, Maynooth, Ireland, (8)Teagasc, Wexford, Ireland, (9)Agri-Food and Biosciences Institute, Belfast, United Kingdom, (10)Geological Survey Ireland, Dublin, Ireland, (11)Stellenbosch University, Department of Geography and Environmental Studies, Stellenbosch, South Africa, (12)University of Dublin, Trinity College, Department of Civil, Structural and Environmental Engineering, Dublin, Ireland, (13)Teagasc, Environment, Soils and Land-Use Research, Wexford, Ireland, (14)Maynooth University, Department of Computer Science, Maynooth, Ireland, (15)University College Dublin, School of Agriculture, Dublin, Ireland, (16)Office of Public Works, Trim, Ireland, (17)Department of Agriculture, Food and Marine, Forestry Division, Wexford, Ireland, (18)Trinity College Dublin, School of Natural Sciences, Discipline of Botany, Dublin, Ireland, (19)Vista Milk SFI Research Centre, Teagasc, Cork, Ireland
 
Connecting Scales of Soil-Moisture Measurements by Cosmic Rays Neutron Sensing (1877304)
Enrico Gazzola1, Stefano A Ferraris2, Davide Gisolo2, Alessio Gentile2, Stefano Bechis2, Marcella Biddoccu3, Giorgio Capello3, Francesca Ragazzi4, Silvia Obber4, Christian Ronchi5, Roberto Cremonini5 and Stefano Gianessi1, (1)Finapp, Montegrotto Terme, Italy, (2)University of Turin, Interuniversity Department of Regional and Urban Studies and Planning, Torino, Italy, (3)CNR, STEMS (Institute of Sciences and Technologies for Sustainable Energy and Mobility), Torino, Italy, (4)ARPA Veneto, Unità Organizzativa Qualità del Suolo, Padova, Italy, (5)ARPA Piemonte, Torino, Italy
 
Quality assuring soil moisture data records with machine learning approaches on in situ data (1879134)
Michael H Cosh, USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, United States and Victoria A Walker, Oak Ridge Institute for Science and Education, Oak Ridge, United States
 
On the value of machine learning for addressing spatial-temporal soil moisture network inconsistencies (1880483)
John D Bolten, NASA Goddard Space Flight Center, Greenbelt, United States, ManhHung Le, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Michael H Cosh, U. S. Department of Agriculture, Beltsville, United States
 
Using point scale soil moisture sensors to estimate regional groundwater recharge (1883933)
Bismark Osei, Texas A&M University College Station, Soil and Crop Science, College Station, United States and Briana Michele Wyatt, Oklahoma State University Main Campus, Stillwater, United States
 
Evaluating the CYGNSS Version 3.2 Soil Moisture Product Across Scales Using Remote Sensing and In-Situ Observations (1900497)
Dinan Bai, University of Michigan Ann Arbor, Ann Arbor, MI, United States and Christopher S Ruf, University of Michigan Ann Arbor, Climate and Space, Ann Arbor, MI, United States
 
Fusing time-lapse geophysics and in-situ sensing to create a 4D model of hillslope hydrology in a Mediterranean catchment. (1906182)
Nunzio Romano1, Giorgio Cassiani2, Paolo Nasta3, Caterina Mazzitelli4 and Matteo Censini2, (1)The University of Naples Federico II, Department of Agricultural Sciences, AFBE Division, Naples, Italy, (2)University of Padova, Department of Geosciences, Padova, Italy, (3)University of Naples Federico II, Department of Agricultural Sciences - Division of Agricultural, Forest and Biosystems Engineering, Portici, Italy, (4)The University of Naples Federico II, Department of Agricultural Sciences, AFBE Division, Portici, Italy
 
Vertical Variability of Soil Moisture Observed by Watermark Sensors (1909054)
Jianhua Chen, National Yunlin University of Science and Technology, Taipei, TAIWAN and Jet-Chau Wen, NYUSUT National Yunlin University of Science and Technology, Yulin, Taiwan
 
Spatiotemporal Variability of Soil Moisture: Multi-Sensor Observations at the Eoseom Korea Hydrometeorological Observatory (1909668)
Doyoung Kim, Sungkyunkwan University, Department of Global Smart City, Suwon, Gyeonggi-do, Korea, Republic of (South), 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, South Korea and Minha Choi, Sungkyunkwan University, Department of Water Resources, Graduate School of Water Resources, Suwon, Gyeonggi-do, South Korea
 
Comparative Analysis of Soil Moisture Sampling Strategies Using an Automated UAV Payload System in Agricultural Fields (1924903)
Hemanth Narayan Dakshinamurthy, South Carolina State University, Orangeburg, SC, United States
 
High-Resolution Multi Depth Soil Moisture Mapping Using CNN-LSTM: A Fusion of Satellite Observations and Soil Survey Data (1928558)
Saman Rabiei, University of Florida, Ft Walton Beach, FL, United States; Department of Soil, Water and Ecosystem Sciences, University of Florida, Gainesville, United States, Ebrahim Babaeian, University of Florida, Soil and Water Sciences Department, Gainesville, United States and Sabine Grunwald, University of Florida, Soil, Water and Ecosystem Sciences Department, Gainesville, United States
 
GSSM-10 (Global 10-m Surface Soil Moisture) Derived from Multi-Sensor Data and Ensemble Machine Learning (1932323)
Nuo Xu, University of California Davis, Davis, CA, United States, Andre Daccache, University of California Davis, Davis, United States and Arman Ahmadi, University of California, Davis, Biological and Agricultural Engineering, Davis, United States
 
UAS-based GNSS-R, LiDAR, and Multispectral Data Fusion for Field Scale Soil Moisture Estimation (1933069)
Volkan Senyurek1, Md Ebtidaul Karim1, Mohammad Abdus Shahid Rafi1, John Ball1 and Ali Gurbuz2, (1)Mississippi State University, Starkville, United States, (2)North Carolina State University, Electrical and Computer Engineering, Raleigh, United States
 
Exploring the Sensitivity of UAV-Based Gamma-Ray Signals to Soil Moisture in Semi-Arid Rangelands, Texas (1935894)
Jipeng Liu, Texas State University San Marcos, San Marcos, TX, United States and Dr. Eunsang Cho, Ph.D., Texas State University, Department of Civil Engineering, San Marcos, United States
 
Comparative Evaluation of Environmental Influences on Cosmic Ray Neutron Measurements Across Diverse Landscapes (1936898)
Jaehwan Jeong, Plant Genomics & Breeding Institute, Research Institute of Agriculture and Life Science, College of Agriculture & Life Sciences, Seoul National University, Seoul, South Korea, Shinhyuk Kong, Seoul National University, Department of Agriculture, Forestry and Bioresources, Seoul, Korea, Republic of (South) and Hyungsuk Kimm, Seoul National University, Department of Agriculture, Forestry and Bioresources, Seoul, South Korea
 
Pushing the Limits of Cosmic-Ray Neutron Sensors for Monitoring and Exploration of Near-Surface Water (1945461)
Martin Schrön1, Daniel Altdorff2, Markus Köhli3, Lasse Hertle4, Solveig Landmark1, Peter Dietrich1, Sascha E Oswald5, Sabine Attinger2 and Steffen Zacharias1, (1)Helmholtz Centre for Environmental Research UFZ Leipzig, Department of Monitoring and Exploration Technologies, Leipzig, Germany, (2)Helmholtz Centre for Environmental Research UFZ Leipzig, Department of Computational Hydrosystems, Leipzig, Germany, (3)University of Heidelberg, Heidelberg, Germany, (4)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (5)University of Potsdam, Institute of Earth and Environmental Science, Potsdam, Germany
 
Soil moisture measurements based on Cosmic-Ray Neutron Sensing – current status and open questions (1945674)
Markus Köhli, University of Heidelberg, Heidelberg, Germany
 
Time-Series Ratio Algorithm for NISAR Soil Moisture Retrieval: Preliminary Results and Initial Validation (1955278)
Joel T Johnson1, Jeonghwan Park2, Rajat Bindlish3, Dustin Horton4 and Mohammad Al-Khaldi4, (1)Ohio State University Main Campus, Columbus, OH, United States, (2)Ohio State University, Electroscience Lab, Columbus, OH, United States, (3)NASA GSFC, Greenbelt, United States, (4)Ohio State University, Department of Electrical and Computer Engineering, Columbus, United States
 
Revealing Principles of Hydrological Response –The Schäfertal Approach (1965419)
Frido Reinstorf, Magdeburg-Stendal University of Applied Sciences, Magdeburg, Germany
 
Magnetic Induction Localization for UAV-based Radar Swarms via a Hybrid LSTM-Optimization Method (1966887)
Alireza Farhadian Boroujerdi1, Sepehr Eskandari2 and Mahta Moghaddam2, (1)University of Southern California, Los Angeles, CA, United States, (2)University of Southern California, Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, CA, United States
 
UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization (1971315)
Ishfaq Aziz1, Runze Zhang2, Trent Ford3, Derek Houtz4, Yuxiang Zhao2, Adam Watts5 and Mohamad Alipour1, (1)University of Illinois Urbana Champaign, Department of Civil and Environmental Engineering, Urbana, United States, (2)University of Illinois Urbana Champaign, Civil and Environmental Engineering, Urbana, United States, (3)University of Illinois Urbana Champaign, Illinois State Water Survey, Champaign, United States, (4)Swiss Federal Institute for Forest, Snow, and Landscape Research, Zurich, Switzerland, (5)Pacific Northwest Research Station, Fire and Environmental Research Applications, Seattle, United States
 
Surface soil moisture estimates at 200m resolution using the PALSAR-2 and post-launch NISAR data (1985431)
Seungbum Kim, Univ California Los Angeles, Los Angeles, United States, Xiaodong Huang, Mitti Labs Limited, Mumbai, India; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Rowena B Lohman, Cornell University, Earth and Atmospheric Sciences, Ithaca, NY, United States and Michael H Cosh, U. S. Department of Agriculture, Beltsville, United States
 
NISMEX-FL: Calibration/Validation experiment for High-Resolution Soil Moisture from NISAR for Agricultural Fields in Florida (1987563)
Laura Almendra Martin1, Jasmeet Judge2, Sarah Daly2, Michelle Prieto-Sanchez2 and Mawiyah Abdelkarim2, (1)University of Florida, Center for Remote Sensing, Agricultural and Biological Engineering, Ft Walton Beach, FL, United States, (2)Center for Remote Sensing, Agricultural and Biological Engineering, University of Florida, Gainesville, United States
 
Assessing the GeoWATCH System's Accuracy in Forecasting Soil Conditions Across Seasonal and Environmental Variabilities (1999698)
Shannon Hibbard1, Izac Torres2,3, Spencer E. Staley2, Sally Houseman2, Brad Sion2 and John B Eylander4, (1)Desert Research Institute Reno, Reno, NV, United States, (2)Desert Research Institute Reno, Reno, United States, (3)University of Nevada Reno, Reno, United States, (4)USACE/ERDC/CRREL, Hanover, United States
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