There is a growing number of applications leveraging remote sensing technologies to monitor and manage water quality in inland waters. The use of remote sensing technologies, especially from satellites, is extremely valuable for limnological applications due to their ability to provide routine, large-scale analyses of spatio-temporal dynamics of key ecological variables and their use for time-series analysis through retrospective imagery. Because of this, remote sensing has played an important role in advancing water quality mapping by allowing the retrieval of optical and physical water properties from local to global scales. Several key factors have contributed to this increase: i) development of space science and technology which has increased the availability of Earth Observation data in the past decades; ii) development of new sensors with improved spectral resolution; iii) establishment of global networks in the field of water quality remote sensing (e.g., GEO AquaWatch, World Water Quality Alliance Earth Observations Workstream, Global Lake Ecological Observatory Network Aquatic Remote Sensing Working Group, and others), as well as open-source global datasets (e.g., GLORIA); and iv) improvements in computational power and the development of cloud computing platforms. Concurrent with these factors has been an increasing acceptance of remotely sensed water quality data worldwide; however, freshwater ecosystems are optically complex, have high biogeochemical variability, and high biological diversity, making common remote sensing products traditionally made for terrestrial systems unsuitable for freshwater. In this session, we aim to highlight efforts focused on applying satellite remote sensing data, developing methods and tools for the estimation of water quality parameters, and supporting capacity building strategies to bridge knowledge gaps in remote sensing studies of inland water quality. This session will contribute to the continuous growth and integration of remote sensing technology in limnology and contribute to the standardization of satellite-based water quality products to further improve the use of remote sensing technology for reliable inland water quality monitoring.

Lead Organizer: Igor Ogashawara, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) (igoroga@gmail.com)

Co-organizers:

Megan Coffer, National Oceanic and Atmospheric Administration (NOAA) (megan.coffer@noaa.gov)

Harriet Wilson, University of Stirling (harriet.wilson@stir.ac.uk)

Daniel Andrade, National Institute for Space Research (INPE) (damaciel_maciel@hotmail.com)

Presentations

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06:00 PM

Leveraging NASA Water Resource Program for Water Quality Management (9267)

Primary Presenter: Kelly Luis, NASA (kelly.m.luis@jpl.nasa.gov)

The need for innovative inland water quality monitoring and management strategies has never been greater. NASA’s Water Resource Program aims to foster interdisciplinary collaborations around satellite observations of water quality and support user-centered needs. This presentation highlights recent advancements in satellite instrumentation, predictive modeling, and capacity building for monitoring water quality indicators. At unprecedented accuracy and spatial and temporal scales, new science to action approaches have been developed for post wildfire response, linking environmental economic and water quality data, understanding water supply stressors, and much more. The NASA Water Resource Program is ultimately committed to addressing the complexities of the hydrologic cycle and meeting the diverse needs of communities that translate satellite observations of water quality into actionable insights for management.

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06:00 PM

Phytoplankton bloom identification in Arizona source waters through a cloud based modeling effort (9616)

Primary Presenter: Ashley Foster, Arizona State University (ashleynicolefoster16@gmail.com)

The Salt River Reservoir System is composed of four lakes (Saguaro Lake, Canyon Lake, Roosevelt Lake, and Apache Lake) that provide drinking water storage along with hydropower to the Phoenix metropolitan area. Additionally, these lakes are used for fishing and other recreational water activities. Blooms of the potentially harmful Golden Alga Prymnesium parvum and the filamentous cyanobacteria Cylindrospermopsis occur typically in spring and summer. This study adds to a continuous monitoring effort by measuring phytoplankton abundance and extracted Chlorophyll a (Chl a) concentration in the surface of  Saguaro Lake’s from 2019 to 2022. Chl a concentrations were also obtained by the Regional Drinking Water Quality Monitoring Program at ASU. The program provides water quality data sets through the Central Arizona Phoenix Long Term Environmental Research online data repository. Chl-a measurements will be compared to Landsat 8 and DoveR images to identify dates when blooms are visible from satellite overpasses. Google earth engine will be used to train machine learning models to identify blooms throughout the reservoir system based on pixel classification. Results from machine learning models such as Support Vector Machine regression and Random Forests will be compared to select the most suitable predictive model.  The purpose of this study is to aid resource managers in identifying blooms of potentially noxious phytoplankton without the associated costs and effort required by in situ sampling.

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