Recent Advances in Remote Sensing and Modeling of Flood Inundation

Cohen, Sagy

H122
Recent Advances in Remote Sensing and Modeling of Flood Inundation

Session ID#: 249767

Session Description:

Flood Inundation Mapping (FIM) is a key element of flood forecasting, response, and analysis frameworks. Efforts toward the development of large-scale (national to global) and high-fidelity flood prediction frameworks have highlighted key knowledge and technical gaps. Improvements in the efficiency of numerical solvers and data processing tools offer unprecedented capabilities for scaling FIM solutions over large domains at high spatial and temporal fidelity. Exciting advancements in hydraulic modeling, high-resolution terrain processing, and high-resolution remote sensing have the potential to advance FIM capabilities and enable robust flood impact analysis, especially in urban and coastal environments, which are pressing societal concerns. This multi-year running session aims to showcase the latest FIM advances, including modeling, remote sensing, and impact analysis, and explore the implementation or potential scaling of these tools within large-scale and/or operational frameworks.

Index Terms:

1821 Floods [HYDROLOGY]
1834 Human impacts [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]

Primary Convener: Sagy Cohen, University of Alabama, Tuscaloosa, AL, United States

Conveners: Lori A. Schultz, University of Alabama in Huntsville, Huntsville, AL, United States and Arjen Haag, Deltares, Delft, Netherlands

Student/Early Career Convener: Anupal Baruah, The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States

DeepSARFlood: An Automated Platform for SAR-based Flood Inundation Mapping using Vision Transformers and Development of a Comprehensive National Database (Invited) (1848715)

Manabendra Saharia, Nirdesh Sharma, Samvedya S and Mohd Saquib, Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi, India

Deep learning to improve satellite-based flood mapping: a better VIIRS algorithm and insights using Geospatial Foundation Models on Planetscope, Sentinel-1, and Sentinel-2 (Invited) (1849233)

Beth Tellman¹, Dr. Saurabh Kaushik, PhD¹, Alexander Saunders², Zhijie Zhang³, Frederick S Policelli⁴, Daniel A Slayback⁵, Jonathan Giezendanner⁶, Rohit Mukherjee² and Rui Zhang⁷, (1)University of Wisconsin Madison, Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, Madison, United States, (2)University of Arizona, School of Geography, Development and Environment, Tucson, United States, (3)Utah State University, Logan, United States, (4)NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, United States, (6)Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, United States, (7)NASA, Sciences and Exploration Directorate, Greenbelt, United States

Physics-guided super-resolution downscaling for national urban FIM (1857175)

Seth Bryant, University of Wisconsin Madison, Madison, WI, United States

A Comparative Analysis of Operational Multi-Method Flood Inundation Mapping: Quantifying Fidelity–Accuracy Tradeoffs (1865271)

Md Shadman Sakib¹, Mohamed Mowafy², Haotian Wang³, Faezeh Maghsoodifar⁴, Sagy Cohen⁵, Yinphan Tsang⁶, David F Muñoz¹, Patrick Ray², Fangni Lei³ and Hamed Moftakhari⁴, (1)Virginia Polytechnic Institute and State University, Civil and Environmental Engineering, Blacksburg, United States, (2)University of Cincinnati, Environmental Engineering, Cincinnati, United States, (3)University of Connecticut, Civil and Environmental Engineering, Storrs, United States, (4)The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, United States, (5)The University of Alabama, Tuscaloosa, AL, United States, (6)University of Hawaiʻi at Mānoa, Natural Resources and Environmental Management, Honolulu, United States

Coastal Building Height Models from High-Resolution SAR Stereo Images for Hurricane Impact Modeling and Assessment (1865572)

Steven Beninati, University of Massachusetts Amherst, Amherst, MA, United States and Stephen Frasier, University of Massachusetts Amherst, Amherst, United States

Commission Error Analysis and Correction in Remote Sensing Flood Maps Using an Object-Oriented, Hydrologically Informed Approach (1870446)

Dan Tian¹, Hongxing Liu¹, Sagy Cohen² and Lei Wang³, (1)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (2)The University of Alabama, Tuscaloosa, AL, United States, (3)Louisiana State University, Department of Geography and Anthropology, Baton Rouge, LA, United States

Bridging Return Period Gaps in Flood Inundation Mapping Using a Hybrid Deep Learning Approach (1874149)

Faezeh Maghsoodifar^1,2, Md Shadman Sakib³, Mohamed Mowafy⁴, Haotian Wang⁵, Sagy Cohen⁶, Yinphan Tsang⁷, Hamed Moftakhari^1,2, David F Muñoz⁸, Patrick Ray⁴ and Fangni Lei⁵, (1)The University of Alabama, Department of Civil, Construction, and Environmental Engineering, Tuscaloosa, United States, (2)The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, United States, (3)Virginia Polytechnic Institute and State University, Civil and Environmental Engineering, Blacksburg, United States, (4)University of Cincinnati, Environmental Engineering, Cincinnati, United States, (5)University of Connecticut, Civil and Environmental Engineering, Storrs, United States, (6)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (7)University of Hawaiʻi at Mānoa, Natural Resources and Environmental Management, Honolulu, United States, (8)Virginia Polytechnic Institute and State University, Civil & Environmental Engineering, Blacksburg, United States

Enhancing River Channel Geometry Estimation for Bankfull Conditions in Iowa Using Machine Learning (1875760)

Marcela Rojas¹, Daniel Gilles¹ and Nathan Young², (1)University of Iowa, Iowa City, IA, United States, (2)University of Iowa, Iowa City, United States

Operational flood inundation detection from Sentinel-1 GRD time series using Bayesian change point analysis (1876583)

Narumasa Tsutsumida, Japan Geoscience Union, Tokyo, Japan; Saitama University, Graduate school of Science & Engineering, Saitama, Japan

Improving Flood Inundation Mapping Using SAR Imagery and Deep Learning Models (1877335)

Min Ki Hong¹, Jungho Seo², Hocheol Seo² and Yeonjoo Kim², (1)Yonsei University, Department of Civil and Environmental Engineering, Seoul, South Korea, (2)Yonsei University, Department of Civil and Environmental Engineering, Seoul, Korea, Republic of (South)

FSM-GEE: An Open-Source Flood Susceptibility Mapping Toolkit (1887634)

Linh Nguyen, Kyungpook National University, School of Advanced Science and Technology Convergence, Daegu, Korea, Republic of (South) and Giha Lee, Kyungpook National University, School of Advanced Science and Technology Convergence, Sangju, South Korea

Enhanced Flood Mapping Through Coherent and Incoherent Change Detection: A Sentinel-1 SAR Time-Series Analysis (1892978)

Sulaiman Hotaki, Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, Hannover, Germany, Mahdi Motagh, GFZ Helmholtz Center for Geosciences, Potsdam, Germany and Mahmud Haghighi, Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, Potsdam, Germany

Satellite-Based Flood Inundation Mapping Using Sentinel-1 and Land Cover Data: A Case Study of Hurricane Florence (1893968)

Yun-Jae Choung, The University of Alabama, Alabama Water Institute, Tuscaloosa, United States, Steven J Burian, Cooperative Institute for Research to Operations in Hydrology (CIROH), Alabama Water Institute, The University of Alabama, Tuscaloosa, AL, Alabama Water Institute, Tuscaloosa, United States and David R. Vallee, National Weather Service Northeast River Forecast Center, Taunton, MA, United States

Urban Flood Mapping: Are Satellite- and Process-Based Methods Aligned? (1894564)

Rebecca Composto¹, Mirela G Tulbure², Mollie Gaines³, Varun Tiwari⁴, Júlio Caineta³ and Emma Vail⁵, (1)North Carolina State University Raleigh, Center for Geospatial Analytics, Raleigh, NC, United States, (2)North Carolina State University, Center for Geospatial Analytics, Raleigh, United States, (3)North Carolina State University, Forestry and Environmental Resources, Raleigh, United States, (4)North Carolina State University Raleigh, Center for Geospatial Analytics, Raleigh, United States, (5)NASA DEVELOP, Athens, United States

GEE-FMF: A Google Earth Engine-Based Machine Learning Framework for Efficient Regional Flood Mapping (1894751)

Saide Zand^1,2, Hamed Moftakhari^1,2, Ebrahim Ebrahim Hamidi^1,3 and Hamid Moradkhani^1,2, (1)The University of Alabama, Department of Civil, Construction, and Environmental Engineering, Tuscaloosa, United States, (2)The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, United States, (3)North Carolina State University, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States

A Framework for Large-scale Flood Inundation Mapping and Evaluation over an Extensive Benchmark Database. (1898079)

Dipsikha Devi¹, Anupal Baruah², Supath Dhital², Dinuke Munasinghe³, Dan Tian², Sagy Cohen² and Yixian Chen², (1)University of Alabama, Department of Geography and the Environment, Tuscaloosa, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (3)Department of Geography and Environment, University of Alabama, Tuscaloosa, United States

Flood Hazard Mapping in A Selected Reach of Teesta River in India Using 2-D Hydrodynamic Model BRAHMA-FM (1910806)

Arup Kumar Sarma¹, Jusmita Kashyap¹, Anurag Handique¹, Anupal Baruah², Shakti Kalyani³, Kasturi Kalita¹, Amit Kalita¹ and Rohan Choudhury¹, (1)Indian Institute of Technology Guwahati, Department of Civil Engineering, Guwahati, India, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (3)Indian Institute of Technology Guwahati, Centre for Environment, Guwahati, India

Probabilistic High-Resolution Flood Maps from Ensembles of Downscaled Compound Flood Inundation in Southeast Texas (1914776)

Mark K Wang¹, Paola Passalacqua¹, Ethan Coon², Saubhagya S Rathore³ and Gabriel Perez⁴, (1)The University of Texas at Austin, Maseeh Department of Civil, Architectural and Environmental Engineering, Austin, United States, (2)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, United States, (4)Oklahoma State University, School of Civil & Environmental Engineering, Stillwater, United States

Sensitivity of Terrain-based Flood Inundation Model (OWP HAND-FIM) Predictions to Channel Geometry: Insights from Bathymetric Adjustments of Rating Curves (1922081)

Reihaneh Zarrabi¹, Sagy Cohen², Carson Pruitt³, Anupal Baruah², Gregory Petrochenkov³ and Riley McDermott⁴, (1)University of Alabama, Department of Geography and the Environment, Tuscaloosa, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (3)NOAA Office of Water Prediction, National Water Center, Geo-Intelligence Division, Tuscaloosa, United States, (4)NOAA, Office of Water Prediction, Silver Spring, United States

Predicting the Accuracy of Flood Inundation Forecasts from Terrain Variability (1925808)

Jose Castejon Villalobos¹, Anzy Lee¹, Noelle Patterson², Rebecca Manners Diehl³, Belize Arela Albin Lane¹ and Colin B Phillips⁴, (1)Utah State University, Civil and Environmental Engineering, Logan, UT, United States, (2)Utah State University, Civil and Environmental Engineering, Logan, United States, (3)University of Vermont, Department of Geography and Geosciences, Burlington, VT, United States, (4)Utah State University, Civil & Environmental Engineering and Utah Water Research Laboratory, Logan, United States

Evidence of chronic flooding from a global 10-meter flood‑occurrence dataset (1927264)

Rohit Mukherjee, Pacific Northwest National Laboratory, Atmospheric, Climate, and Earth Sciences Division, Richland, United States and TC Chakraborty, Pacific Northwest National Laboratory, Richland, WA, United States

A Next-generation Flood Mapping System (RAPID v2.0): the 1-dimentional PDF Matching Outperforms 0-dimensional Threshold Comparison (1927783)

Maopu Xu¹, Xinyi Shen², Qingyuan Zhang³, William C. Straka⁴, Jessie Torres³ and Marilyn Murphy⁵, (1)University of Wisconsin Milwaukee, Milwaukee, WI, United States, (2)University of Wisconsin-Milwaukee, School of Freshwater Sciences, Milwaukee, United States, (3)National Oceanic and Atmospheric Administration, Center for Satellite Applications and Research, College Park, United States, (4)University of Wisconsin Madison, Cooperative Institute for Meteorological Satellite Studies, Madison, United States, (5)National Oceanic and Atmospheric Administration, College Park, United States

Evaluating the Impact of Streamflow Nonstationarity on Flood Inundation Using Remote Sensing and Deep Learning (1930301)

Jibin Joseph, Purdue University, Lyles School of Civil and Construction Engineering, West Lafayette, IN, United States, Nagesh Kumar D, Indian Institute of Science, Bangalore, India and Venkatesh Merwade, Purdue University, Lyles School of Civil and Construction Engineering, West Lafayette, United States

Advanced Flood Detection Using Dual-PolSAR Decomposition and AI-Based Classification Models (1931182)

Soohyun Kim¹, Sayed M. Bateni², Jinwook Lee³, Saeid Janizadeh³, Dongkyun Kim⁴ and Changhyun Jun⁵, (1)University of Hawaii at Manoa, Department of Civil and Environmental Engineering, Honolulu, HI, United States, (2)University of Hawaii at Manoa, Department of Civil and Environmental Engineering and Water Resources Research Center, Honolulu, HI, United States, (3)University of Hawaii at Manoa, Department of Civil and Environmental Engineering, Honolulu, United States, (4)Hongik University, Department of Civil and Environmental Engineering, Seoul, Korea, Republic of (South), (5)Korea University, School of Civil, Environmental and Architectural Engineering, Seoul, South Korea

Integrating High-Temporal X-band SAR Imagery with HEC-RAS 2D Simulations for Enhanced Flood Analysis (1941584)

Dr. Younghyun Cho, PhD, Korea Water Resources Corporation, Water Resources & Environmental Research Center, K-water Research Institute, Daejeon, Korea, Republic of (South), Rex Tan, Capella Space, Singapore, Singapore and Joonwoo Noh, Korea Water Resources Corporation, Daejeon, South Korea

Flood Mapping from GNSS Reflectometry: A CYGNSS-Based Deep Learning Product for Monitoring Inundation Events (1942635)

Roman Alarcia Perez¹, Paulo T. Setti Jr.² and Sajad Tabibi¹, (1)University of Luxembourg, Department of Engineering, Esch-sur-Alzette, Luxembourg, (2)INPE National Institute for Space Research, Cachoeira Paulista, Brazil

Enhanced Framework for Urban Flood Mapping: Fusion of Synthetic Aperture Radar Flood Extents with Auxiliary Data (1943281)

Ambika Khadka, ICEYE, Espoo, Finland

Capturing Peak Flood Conditions with Satellite Imagery (1943723)

Annett Anders¹, Dafni Sidiropoulou-Velidou¹, Juan Ardila¹, Archana Joshi¹ and Kat Jensen², (1)ICEYE Ltd., Analytics, Espoo, Finland, (2)Muon Space, Mountain View, United States

Enhancing Large-Scale Flood Inundation Mapping through Process-Based Classification (1949577)

Rebecca Manners Diehl, University of Vermont, Department of Geography and Geosciences, Burlington, VT, United States, David Baude, University of Vermont, Geography & Geosciences; Water Resources Institute, Burlington, United States, Ijaz Ul Haq, University of Vermont, Computer Science, Burlington, United States, Kristen Underwood, University of Vermont, Civil & Environmental Engineering; Water Resources Institute, Burlington, United States, Alexander B Prescott, University of Vermont, Department of Geography & Geosciences, Burlington, United States, Beverley Wemple, University of Vermont, Department of Geography and Geosciences, Burlington, United States, Belize Arela Albin Lane, Utah State University, Civil and Environmental Engineering, Logan, UT, United States and Colin B Phillips, Utah State University, Civil & Environmental Engineering and Utah Water Research Laboratory, Logan, United States

Generation of High-Resolution Flood Inundation Maps from Airborne Imagery Using Supervised Machine Learning (1949588)

Dinuke Munasinghe¹, Sagy Cohen², Dan Tian², Hongxing Liu², Anupal Baruah² and Dipsikha Devi², (1)University of Alabama, Tuscaloosa, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States

Seeing the Flood: Sensor Accuracy across Landscapes (1954354)

Emma Vail¹, Mirela G Tulbure¹, Rebecca Composto¹, Varun Tiwari², Júlio Caineta¹ and Mollie Gaines¹, (1)North Carolina State University, Center for Geospatial Analytics, Raleigh, United States, (2)Clark University, Center for Geospatial Analytics, Worcester, United States

Integrating Diverse Flood Inundation Datasets into a Harmonized Database and Visualization Framework for Enhanced Decision Support (1957497)

Pitamber Wagle, Brigham Young University, Provo, UT, United States, Jim Nelson, Brigham Young University, Civil and Environmental Engineering, Provo, UT, United States, Riley Hales, Brigham Young University, Civil and Environmental Engineering, Provo, United States, Louis Rosas, Brigham Young University, Provo, United States and Ibrahim Demir, Tulane University, New Orleans, United States

Deep learning for flood mapping using spatial, spectral, and temporal features (1958792)

Júlio Caineta, North Carolina State University Raleigh, Raleigh, NC, United States and Mirela G Tulbure, North Carolina State University, Center for Geospatial Analytics, Raleigh, United States

Multimodal Post‑Flood Water Extent Mapping With SAR and Incomplete Multispectral Data Using a Spatially Masked Adaptive Gated Network (1961803)

Hyunho Lee and Wenwen Li, Arizona State University, School of Geographical Science and Urban Planning, Tempe, AZ, United States

FastPLN: High-resolution GPU-accelerated Rapid Flood Mapping at Scale (1965765)

Brodie Alexander, The University of Alabama, Department of Electrical and Computer Engineering, Tuscaloosa, AL, United States, Junho Song, University of Alabama, Alabama Water Institute, Tuscaloosa, United States and James Halgren, The University of Alabama, Alabama Water Institute, Tuscaloosa, United States

Benchmark Flood Inundation Mapping Using RGB Aerial Imagery and a Sub-Matrix Convolutional Neural Network (1968375)

Sadra Seyvani¹, Dinuke Munasinghe¹, Parvaneh Nikrou² and Sagy Cohen³, (1)University of Alabama, Tuscaloosa, United States, (2)University of Alabama, Tuscaloosa, AL, United States, (3)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States

Stage-Specific Intercomparison of Five Flood Inundation Models Across the Rising and Falling Limbs of a Flood Event (1969582)

Parvaneh Nikrou¹, Sadra Seyvani², Sudershan Gangrade³, Joseph Lee Gutenson⁴, Dan Tian⁵, Anupal Baruah⁵, Michael L Follum⁴, Shih-Chieh Kao⁶, Sagy Cohen⁵ and Sadra Seyvani, (1)University of Alabama, Tuscaloosa, AL, United States, (2)University of Alabama, Tuscaloosa, United States, (3)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States, (4)Follum Hydrologic Solutions, Casper, United States, (5)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (6)Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, United States

Examining Spatial Consistency of the Synthetic Rating Curves and Improvement in the Calibration Procedure (1972276)

Reza Jamshidi, Northeastern University, Boston, United States, Edward Beighley, Northeastern University, Department of Civil and Environmental Engineering, Boston, United States, Kelsey Pieper, University of North Carolina at Charlotte, Department of Civil and Environmental Engineering, Charlotte, North Carolina, United States and C. Nathan Jones, University of Alabama, Biological Sciences, Tuscaloosa, Alabama, United States

Flood extent enhancement using terrain information for the OPERA Harmonized Landsat Sentinel-2 Dynamic Surface Water eXtent (DSWx-HLS) products (1973988)

Son K. Do, University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, VA, United States, Sagy Cohen, The University of Alabama, Tuscaloosa, AL, United States, John B Eylander, USACE/ERDC/CRREL, Hanover, United States, Sydney Crisanti, U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States and Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States

SAR-Based Flood Inundation and Crop Damage Mapping in the 2024 Feni River Basin: Overcoming Optical and Ground Truthing Challenges with DEM Integration and Remote Sensing Proxies (1989666)

MD. Mahdi Hasan Saikot¹, Md. Imtiaz Islam², Syed Musfekus Salehin Sian², Biddyut Roy² and Abu Sufian Forhadun Nabi², (1)Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh, (2)Bangladesh University of Engineering and Technology, BUET-Japan Institute of Disaster Prevention and Urban Safety, Dhaka, Bangladesh

Integrating Hydraulic Models and Sentinel-1 Observation for Flood Mapping in North Carolina (1991358)

Yuyang Li¹, Edward Beighley¹, Kelsey Pieper² and C. Nathan Jones³, (1)Northeastern University, Department of Civil and Environmental Engineering, Boston, United States, (2)University of North Carolina at Charlotte, Department of Civil and Environmental Engineering, Charlotte, North Carolina, United States, (3)University of Alabama, Biological Sciences, Tuscaloosa, Alabama, United States

Improving NOAA-OWP HAND Flood Depth Estimation with Machine Learning-Based Surrogate Modeling (2001275)

Parneet Sidhu¹, Dipsikha Devi², Anupal Baruah² and Sagy Cohen², (1)California State University Sacramento, Sacramento, CA, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States

Improving HAND-Based Water Depth Estimates with Machine Learning-Derived SAR Water Extent Maps (2002747)

Minjeong Jo^1,2, Arif Albayrak^2,3, Batuhan Osmanoglu⁴, Franz Josef Meyer⁵ and Lori A. Schultz⁶, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)University of Maryland Baltimore County, Goddard Earth Sciences Technology and Research (GESTAR) II, Baltimore, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)Ursa Space Systems, Ithaca, United States, (5)University of Alaska Fairbanks, Geophysical Institute, Fairbanks, AK, United States, (6)University of Alabama in Huntsville, Huntsville, AL, United States

Investigating the Usefulness of Terrain-Based Flood Mapping for Probabilistic Flood Inundation (2003743)

Santiago Henao Gomez, University of Iowa, IIHR—Hydroscience & Engineering, Iowa City, IA, United States, Humberto J Vergara, PhD, University of Iowa, IIHR-Hydroscience & Engineering, Iowa City, United States, Mohamed Abdelkader, University of Iowa, IIHR—Hydroscience & Engineering, Iowa City, United States, Christine Szpilka, Oklahoma University, Norman, United States and Kendra M Dresback, University of Oklahoma Norman Campus, School of Civil Engineering and Environmental Science, Norman, United States

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H122 Recent Advances in Remote Sensing and Modeling of Flood Inundation

Session Description:

Index Terms:

H122
Recent Advances in Remote Sensing and Modeling of Flood Inundation