Kevin Wagner

Dr. Kevin Wagner

Oklahoma State University
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 May 2021 in Remote Sensing
Verified
Journal Article
Open access
PDF file
ABUBAKARR Sidique MANSARAYABUBAKARR Sidique MANSARAY
ABUBAKARR Sidique MANSARAY
ADAndy Dzialowski
Andy Dzialowski
MMMeghan Martin
Meghan Martin
Kevin WagnerKevin Wagner
Kevin Wagner
HGHamed Gholizadeh
Hamed Gholizadeh
SSScott Stoodley
Scott Stoodley
Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

1 March 2016 in Journal of Hydrology
Verified
Journal Article
Closed access
RHR.D. Harmel
R.D. Harmel
JHJ.M. Hathaway
J.M. Hathaway
Kevin WagnerKevin Wagner
Kevin Wagner
JWJ.E. Wolfe
J.E. Wolfe
RKR. Karthikeyan
R. Karthikeyan
David McCarthyDavid McCarthy
David McCarthy

11 February 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
Priya KayasthaPriya Kayastha
Priya Kayastha
ADAndy Dzialowski
Andy Dzialowski
SSScott Stoodley
Scott Stoodley
Kevin WagnerKevin Wagner
Kevin Wagner
ABUBAKARR Sidique MANSARAYABUBAKARR Sidique MANSARAY
ABUBAKARR Sidique MANSARAY
Algal blooms in freshwater ecosystems can negatively impact aquatic and human health. Satellite remote sensing of chlorophyll a (Chl-a) is often used to help determine the severity of algal blooms. However, satellite revisit flyover schedules may not match the erratic nature of algal blooms. Studies have paired satellite and ground-based data that were not collected on the same day, assuming Chl-a concentrations did not change significantly by the flyover date. We determined the effects of an increasing time window between satellite overpass dates and field-based collection of Chl-a on algorithms for Landsat 5, Landsat 8, and Sentinel-2, using 14 years (2006–2020) of Chl-a data from 10 Oklahoma reservoirs. Multiple regression models were built, and selected statistics were used to rank the time windows. The Sentinel-2 results showed strong relationships between Chl-a and satellite data collected up to a ±5-day window. The strength of these relationships decreased beyond a ±3-day time window for Landsat 8 and a ±1-day time window for Landsat 5. Our results suggest that the time window between field sampling and satellite overpass can impact the use of satellite data for Chl-a monitoring in reservoirs. Furthermore, longer time windows can be used with higher resolution (spatial, spectral) satellites.

23 January 2020 in Water
Verified
Journal Article
Open access
PDF file
AGAnna Gitter
Anna Gitter
Kristina MenaKristina Mena
Kristina Mena
Kevin WagnerKevin Wagner
Kevin Wagner
DBDiane Boellstorff
Diane Boellstorff
KBKyna Borel
Kyna Borel
RKRaghupathy Karthikeyan
Raghupathy Karthikeyan
Gastrointestinal (GI) illness risks associated with exposure to waters impacted by human and nonhuman fecal sources were estimated using quantitative microbial risk assessment (QMRA). Microbial source tracking (MST) results had identified Escherichia coli (E. coli) contributors to the waterbody as human and unidentified (10%), cattle and domestic animals (25%), and wildlife (65%) in a rural watershed. The illness risks associated with ingestion during recreation were calculated by assigning reference pathogens for each contributing source and using pathogen dose–response relationships. The risk of GI illness was calculated for a specific sampling site with a geometric mean of E. coli of 163 colony forming units (cfu) 100 mL−1, and the recreational standard of E. coli, 126 cfu 100 mL−1. While the most frequent sources of fecal indicator bacteria at the sampling site were nonhuman, the risk of illness from norovirus, the reference pathogen representing human waste, contributed the greatest risk to human health. This study serves as a preliminary review regarding the potential for incorporating results from library-dependent MST to inform a QMRA for recreational waters. The simulations indicated that identifying the sources contributing to the bacterial impairment is critical to estimate the human health risk associated with recreation in a waterbody.

8 March 2025 in Agricultural Water Management
Verified
Journal Article
Open access
MAMuhammad Umar Akbar
Muhammad Umar Akbar
Ali MirchiAli Mirchi
Ali Mirchi
Arfan ArshadArfan Arshad
Arfan Arshad
SASara Alian
Sara Alian
MMMukesh Mehata
Mukesh Mehata
Kevin WagnerKevin Wagner
Kevin Wagner

1 January 2019 in Science of The Total Environment
Verified
Journal Article
Closed access
Jaehak JeongJaehak Jeong
Jaehak Jeong
Kevin WagnerKevin Wagner
Kevin Wagner
JFJaime J. Flores
Jaime J. Flores
TCTim Cawthon
Tim Cawthon
YHYoung Gu Her
Young Gu Her
Haw YenHaw Yen
Haw Yen