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Session Proposal: Advancing Water Quality Monitoring and Management through Remote Sensing and Artificial Intelligence (AGU25)

H036
Advancing Water Quality Monitoring and Management through Remote Sensing and Artificial Intelligence

Session ID#: 248236

Session Description:
In the face of climatic and anthropogenic changes, there is an urgent need for innovative approaches to enhance our ability to monitor and manage freshwater resources. The integration of remote sensing and Artificial Intelligence (AI) is a promising approach for water quality monitoring, which is critical for ecosystem health, public safety, and economic resilience. This session invites papers that explore the innovative application of remote sensing and AI in water quality assessment, focusing on their ability to provide comprehensive information. We welcome a wide range of research that includes the development and application of remote sensing and AI models, as well as machine learning algorithms—including shallow machine learning methods for predictive analysis, trend identification, and water quality management under current and changing conditions. Interdisciplinary studies involving socio-economic impacts of water quality, policy and management implications, advancements in sensor technology, and economic analysis of water quality management are also invited.
Index Terms:

1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
1879 Watershed [HYDROLOGY]
Primary Convener:  Nasrin Alamdari, Florida State University, Civil and Environmental Engineering, Tallahassee, FL, United States
Conveners:  Elmira Hassanzadeh, Polytechnique Montreal, Civil, Geological, and Mining Engineering, Montreal, QC, Canada, Marzieh Azarderakhsh, The City University of New York, Earth and Environmental Sciences, New York, United States and Dongmei Feng, University of Cincinnati, Cincinnati, United States
 
Sustainable Wastewater Microbiome Management through Deep Learning-Driven Phenotypic Monitoring and Diagnostics (1854428)
Zijian Wang1, Yuan Yan2, IL Han3, Jangho Lee3, Guangyu Li3, Peisheng He4, Annalisa Onnis-Hayden5, Nicholas Tooker6, Dongqi Wang7, Zijun Meng3, Mark Miller8, Kester McCullough9, Stephanie Klaus9, Fabrizio Sabba10, Jose Jimenez8, Charles Bott11, Christine deBarbadillo10, Andrea Giometto3, Kilian Weinberger12 and April Z Gu3, (1)Cornell University, Biological and Environmental Engineering, Ithaca, United States, (2)Cornell University, Civil and Environmental Engineering, Ithaca, United States, (3)Cornell University, School of Civil and Environmental Engineering, Ithaca, United States, (4)Cornell University, School of Civil and Environmental Engineering, Ithaca, NY, United States, (5)Northeastern University, Department of Civil and Environmental Engineering, Boston, United States, (6)University of Massachusetts Amherst, Department of Civil and Environmental Engineering, Amherst, United States, (7)Xi'an University of Technology, Department of Municipal and Environmental Engineering, Xi'an, China, (8)Brown and Caldwell, Walnut Creek, United States, (9)Hampton Roads Sanitation District, Virginia Beach, United States, (10)Black & Veatch, Kansas City, United States, (11)Hampton Roads Sanitization District, Virginia Beach, United States, (12)Cornell University, Department of Computer Science, Ithaca, United States
 
Cloud-Based Water Quality Mapping with Landsat-8/9 and Sentinel-2 on Google Earth Engine (1861460)
Jilin Men1, Hongxing Liu2, Ekaterina Miliutina2, Lei Wang3, Yuehan Lu4, Haibin Su5, Dan Tian2 and Yitao Pu2, (1)University of Alabama, Tuscaloosa, AL, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (3)Louisiana State University, Department of Geography and Anthropology, Baton Rouge, LA, United States, (4)The University of Alabama, Geological Sciences, Tuscaloosa, AL, United States, (5)Texas A & M University - Kingsville, Department of Physics & Geosciences, Kingsville, United States
 
Deep Learning Based Prediction of Chlorophyll-a Using Environmental Variables and GRU-D in a Freshwater Ecosystem (1874330)
Eden Wasehun, North Carolina A & T State University, Greensboro, NC, United States and Leila Hashemi Beni, North Carolina A & T State University, Department of Built Environment, Greensboro, United States
 
Self-Supervised Deep Neural Networks for Predicting Cyanobacterial Bloom Events Using NASA’s PACE Satellite (1875398)
Mitra Nasr Azadani1, Syed Usama Imtiaz1 and Nasrin Alamdari2, (1)Florida State University, Tallahassee, FL, United States, (2)Florida State University, Civil and Environmental Engineering, Tallahassee, FL, United States
 
Evaluating the Generalizability and Transferability of Water Quality Remote Sensing Models Calibrated with Drone-Based Hyperspectral Imagery and Dense in situ Measurements from an Autonomous Surface Vehicle (1882129)
Amanjit Premsagar1, Hongxing Liu2, Ekaterina Miliutina2, Jilin Men2, Yuehan Lu1, Anindya Palaparthi2, Tantu Mandal2, Dan Tian2 and Yitao Pu2, (1)The University of Alabama, Geological Sciences, Tuscaloosa, AL, United States, (2)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States
 
Remote Sensing-Based Mapping and Monitoring Salinity and Dissolved Oxygen in Mobile Bay and Coastal Alabama Using Deep Learning and Sentinel‑3 OLCI Data (1882807)
Anindya Palaparthi1, Hongxing Liu2, Dan Tian2, Yuehan Lu3, Sagy Cohen2, Naveenkumar Purushothaman4 and Lei Wang5, (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)The University of Alabama, Geological Sciences, Tuscaloosa, AL, United States, (4)Organization Not Listed, Washington, DC, United States, (5)Louisiana State University, Department of Geography and Anthropology, Baton Rouge, LA, United States
 
Advancing Virtual Water Analytics through Remote Sensing and Artificial Intelligence for Sustainable Agricultural Water Management (1889611)
Mannan Aleem, University of Agriculture Faisalabad, Structures and Environmental Engineering, Faisalabad, Pakistan, Dr. Shahbaz Khan, PhD , University of Agriculture Faisalabad, Department of Structures and Environmental Engineering, Faisalabad, Pakistan, Hailong He, University of Manitoba (Fort Garry Campus), Faculty of Agricultural and Food Sciences, Winnipeg, Manitoba, Canada, Ana Javaid, Government College University Faisalabad, Pakistan, Department of Food Science, Faisalabad, Pakistan and Muqadas Aleem Dr., University of Agriculture Faisalabad, Department of Plant Breeding and Genetics, Faisalabad, Pakistan
 
Integrated Assessment of Water Quality Dynamics in the Western Mississippi Sound Using Field Observations, Remote Sensing, and Coupled Hydrodynamic–Watershed Modeling (1895608)
Mr. Hafez Ahmad, Mississippi State University, Department of Geosciences, Mississippi State University, Starkville, MS, Mississippi State, United States, Padmanava Dash, Mississippi State University, Department of Geosciences, Mississippi State, United States and Padmanava Dash,Department of Geosciences, Mississippi State University, Starkville, MS
 
AI-Enabled Remote Sensing for Continuous Monitoring of Groundwater Quality Dynamics (1910236)
Imran Khan, University of Agriculture Faisalabad, Department of Irrigation and Drainage, Faisalabad, Pakistan, Naeem Saddique, University of Agriculture Faisalabad, Dept. of Irrigation and Drainage, Faisalabad, Pakistan, Sheraz Maqbool, International Water Management Institute, Islamabad, Pakistan, Islamabad, Pakistan, Aamir Raza, university of Minnesota, soil, water and climate, Heiloo, United States and Syed Muhammad Ahmad Fridi, University of Agriculture, Faisalabad, Faisalabad, Pakistan
 
From Space to Shore: Tracking Microbial Contamination Using PACE Hyperspectral Imagery (Invited) (1913451)
Dr. Antarpreet Singh Jutla, PhD, University of Florida, Ft Walton Beach, FL, United States, Sunil Kumar, University of Florida, Environmental Engineering Sciences, Ft Walton Beach, FL, United States, Bailey Magers, University of Florida, Gainesville, United States, Katherine Deliz Quinones, University of Florida, Environmental Engineering Sciences, Gainesville, United States and Rita R Colwell, University of Maryland College Park, Institute of Advanced Computer Studies, College Park, United States
 
Remote sensing applications for riverine water quality monitoring and management: Opportunities and lessons learned (Invited) (1918014)
John Gardner, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
 
Modeling Harmful Algal Blooms in Surface Water Bodies Using Sentinel-2 Images and Machine Learning (1923246)
Sumit Mahato, University of Cincinnati Main Campus, Cincinnati, United States and Dongmei Feng, University of Cincinnati Main Campus, Chemical and Environmental Engineering, Cincinnati, United States
 
Space-Based Tools for California’s Water Quality Challenges: Linking Satellites to Solutions (Invited) (1937643)
Erin Lee Hestir, University of California Merced, Department of Civil & Environmental Engineering, Merced, CA, United States, Christine M Lee, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Amanda Lopez, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Dulcinea Avouris, Kent State University Kent Campus, Kent, United States, Brittany Lopez Barreto, University of California, Merced, Environmental Systems, Merced, United States, Lori Aznive Berberian, Occidental College, Geology, Los Angeles, CA, United States, Shobha Khanna, University of California Merced, Merced, CA, United States, Fabjola Kasaj, Organization Not Listed, Livermore, United States and Marc Beutel, University of California, Merced, Merced, CA, United States
 
Reducing bias of ensemble tree-based regression algorithms in environmental modeling (1946595)
Sumon Hossain Rabby, Florida State University, Department of Civil and Environmental Engineering, Tallahassee, FL, United States, Ebrahim Ahmadisharaf, Florida A&M University-Florida State University College of Engineering, Tallahassee, United States and Ming Ye, Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, United States
 
Minimizing the impact of atmospheric correction processes on satellite-derived estimates of Chlorophyll-a using domain adaptation (1948568)
Arun Saranathan1, Ryan O'Shea1, Mortimer Werther2, Akash Ashapure3 and Nima Pahlevan4, (1)Science Systems and Applications, Inc., Lanham, MD, United States, (2)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, (3)Science Systems and Applications Inc., Lanham, United States, (4)Science Systems and Applications, Inc., Lanham, United States
 
Synthesizing Cross-System Algal Bloom Research and Long-Term Trends in River Algal Concentrations Using Satellites (1964655)
Gabriella Zuccolotto1, Samuel Sillen2, Emily M. Elliott2 and John Gardner3, (1)University of Pittsburgh, Pittsburgh, PA, United States, (2)University of Pittsburgh, Department of Geology and Environmental Science, Pittsburgh, United States, (3)University of North Carolina at Chapel Hill, Earth, Marine, and Environmental Sciences, Chapel Hill, United States
 
Long-Term Modeling of Physiochemical and Nutrient Parameters in a Regulated Coastal Basin Using a Spatially-Distributed Long Short-Term Memory Model (1965161)
Xiuming Sun, Florida State University, Tallahassee, FL, United States, Ebrahim Ahmadisharaf, Florida A&M University-Florida State University College of Engineering, Tallahassee, United States and Ming Ye, Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, United States
 
Beyond Proxies: A Data-Driven Framework for Mapping Nitrogen and Phosphorus in Surface Waters (1980212)
Jobin Thomas, University of Mississippi Main Campus, Geology and Geological Engineering, University, MS, United States, Thomas Oommen, University of Mississippi, Geology and Geological Engineering, Oxford, United States, Jason Taylor, USDA-ARS, Water Quality & Ecology Research Unit, Oxford, United States, Matthew T Moore, USDA-ARS National Sedimentation Laboratory, Oxford, United States, Gregg R Davidson, University of Mississippi, Geology & Geological Engineering, University, United States and Lance D Yarbrough, University of Mississippi, Geology & Geological Engineering, Oxford, MS, United States
 
Integrating Sentinel-1 and Sentinel-2 Data for TDS Monitoring in Arid Regions: A Case Study of the Dez River (1991989)
Meysam Atakhorami, Shahid Beheshti University, Tehran, Iran, Mojtaba Shourian, Department of Water Faculty of Civil Water, and Environmental Engineering, Shahid Beheshti University, Tehran, Iran, Tehran, Iran, Alireza Sharifi, Department of Geoinformation and Geomatics Engineering Faculty of Civil Water, and Environmental Engineering, Shahid Beheshti University, Tehran, Iran, Tehran, Iran and Mojtaba Shourian, Alireza Sharifi
 
Multi Modal Learning for Forecasting Maximum Chlorophyll Index and Peak Height in Lake Champlain (1997293)
Muhammad Adil1, Patrick J Clemins2, Andrew W. Schroth3, Donna M Rizzo4, Panagiotis D. Oikonomou4,5, Peter DF Isles6, Noah Beckage7, Safwan Wshah8 and Asim Zia9, (1)University of Vermont, Vermont Artificial Intelligence Lab (VaiL), Department of Computer Science, Burlington, VT, United States, (2)University of Vermont, Department of Computer Science, Burlington, VT, United States, (3)University of Vermont, Geography and Geosciences, Burlington, United States, (4)University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, (5)Agricultural University of Athens, Department of Natural Resources Management and Agricultural Engineering, Athens, Greece, (6)Vermont Department of Environmental Conservation, Watershed Management Division, Montpelier, United States, (7)University of Vermont, Burlington, United States, (8)University of Vermont, Vermont Artificial Intelligence Lab (VaiL), Department of Computer Science, Burlington, United States, (9)University of Vermont, Department of Community Development and Applied Economics, Department of Computer Science, Burlington, VT, United States
 
Integrating GLORIA Hyperspectral Data with Mixture Density Networks for Chlorophyll-a Prediction in Chesapeake Bay (2000389)
Nivedita Priyadarshini Kamaraj1, Sundarabalan V. Balasubramanian2, Susanna Werth1 and Manoochehr Shirzaei1, (1)Virginia Tech, Department of Geosciences, Blacksburg, United States, (2)University of Massachusetts Boston, Department of Biology, Boston, United States
 
Physics-informed Gaussian Process models for spatiotemporal groundwater contaminant characterization (2004662)
Richy Zheng, Miramonte High School, Moraga, United States and Haruko M Wainwright, Massachusetts Institute of Technology, Department of Nuclear Science and Engineering; Department of Civil and Environmental Engineering, Cambridge, United States
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