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Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters.
Abstract
This short communication describes several previously undocumented processing steps, pixel flagging procedures, and improvements made to the maximum peak height (MPH) algorithm, which is aimed at the operational detection of chlorophyll a (chl-a) and cyanobacteria blooms in inland and near-coastal waters. The improvements reduce false-positive cyanobacteria detection in oligotrophic waters; enable pixels in clear waters affected by environmental radiances (stray light) to be identified; and enhance detection of floating aquatic vegetation. Several case studies from around the globe are used to illustrate these improvements, and demonstrate new pixel flagging procedures and algorithm operation. The algorithm was validated using a large in situ dataset for chl-a from a wide range of inland water types. The MPH chl-a estimates were more stable than the fluorescence line height (FLH) and maximum chlorophyll index (MCI) values. The results demonstrate the potential for using the MPH algorithm, now released as a plug-in for the Basic ENVISAT A(ATSR) and MERIS (BEAM) processing toolbox, as a standard product for estimating chl-a (and trophic status) in inland and near-coastal waters using MERIS and the forthcoming Sentinel-3 Ocean and Land Colour Instrument.
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“Our cursory examination of other algorithms in the rededge portion of the electromagnetic spectrum, such as the Algal Bloom Index (ABI; Hu and Feng, 2016), which is another algorithm that includes fluorescence, and Maximum Peak Height (MPH) chlorophyll (Matthews and Odermatt, 2015) indicates that RBD is more useful at delineating these blooms when they are fluorescing.”
From Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry (2020) · cites this paper as Matthews and Odermatt, 2015
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Research works printed but not linked: 11
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- Matthews, 2014, Eutrophication and cyanobacterial blooms in South African inland waters: 10 years of MERIS observations, Remote Sensing of Environment, 155, 161, 10.1016/j.rse.2014.08.010 · DOI
- Matthews, 2012, An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters, Remote Sensing of Environment, 124, 637, 10.1016/j.rse.2012.05.032 · DOI
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Cited by
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Wolny, J. L., Tomlinson, M. C., Schollaert Uz, S., Egerton, T. A., McKay, J. R., Meredith, A., Reece, K. S., Scott, G. P., & Stumpf, R. P. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00337 · Cited by 80
Zeng, C., & Binding, C. (2019) · Remote Sensing · doi:10.3390/rs11192306 · Cited by 28
Metadata
- Authors
- Matthews, M. W., & Odermatt, D.
- Affiliations
- University of Cape Town (ZA)
- Brockmann Consult (Germany) (DE)
- Corresponding author
- Matthews, Mark William
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 156
- Pages
- 374-382
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2043870328
- Cited by
- 174
- References
- 0
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 12 · 2023: 22 · 2022: 34 · 2021: 17 · 2020: 11 · 2019: 15 · 2018: 15 · 2017: 12 · 2016: 14 · 2015: 4
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Remote Sensing of Environment (publishedVersion)
- Funders
- EU-FP-7 Marie Curie PEOPLE/IAPP project ‘WaterS’ (WaterS - EU REA Grant Agreement No 251527)
- ESA's Diversity II project (Diversity II - ESRIN/AO/1-7025/12/I-LG)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Plankton and Biogeochemistry
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- cyanobacterial blooms, eutrophication, inland waters, fluorescence line height, stray light, MERIS, Sentinel-3 OLCI, trophic state, false positive detection
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