dc.contributor.author |
Matthews, MW
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|
dc.contributor.author |
Bernard, Stewart
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|
dc.contributor.author |
Evers-King, H
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dc.contributor.author |
Robertson Lain, L
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dc.date.accessioned |
2021-04-10T11:42:29Z |
|
dc.date.available |
2021-04-10T11:42:29Z |
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dc.date.issued |
2020-10 |
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dc.identifier.citation |
Matthews, M., Bernard, S., Evers-King, H. & Robertson Lain, L. 2020. Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. <i>Remote Sensing of Environment, 248.</i> http://hdl.handle.net/10204/11972 |
en_ZA |
dc.identifier.issn |
0034-4257 |
|
dc.identifier.issn |
1879-0704 |
|
dc.identifier.uri |
https://doi.org/10.1016/j.rse.2020.111981
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|
dc.identifier.uri |
https://www.sciencedirect.com/science/article/pii/S0034425720303515
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/11972
|
|
dc.description.abstract |
A hyperspectral inversion algorithm was used to distinguish between cyanobacteria and algal blooms in optically complex inland waters. A framework for the algorithm is presented that incorporates a bio-optical model, a solution for the radiative transfer equation using the EcoLight-S radiative transfer model, and a non-linear optimization procedure. The natural variability in the size of phytoplankton populations was simulated using a two-layered sphere model that generated size-specific inherent optical properties (IOPs). The algorithm effectively determined the type of high-biomass blooms in terms of the relative percentage species composition of cyanobacteria. It also provided statistically significant estimates of population size (as estimated by the effective diameter), chlorophyll-a (chl-a) and phycocyanin pigment concentrations, the phytoplankton absorption coefficient, and the non-algal absorption coefficient. The algorithm framework presented here can in principle be adapted for distinguishing between phytoplankton groups using satellite and in situ remotely sensed reflectance. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.source |
Remote Sensing of Environment, 248 |
en_US |
dc.subject |
Algorithms |
en_US |
dc.subject |
Bio-optics |
en_US |
dc.subject |
Cyanobacteria |
en_US |
dc.subject |
Harmful algal blooms |
en_US |
dc.subject |
Hyperspectral |
en_US |
dc.subject |
Remote sensing |
en_US |
dc.title |
Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
11pp |
en_US |
dc.description.note |
© 2020 Elsevier Inc. All rights reserved. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://www.sciencedirect.com/science/article/pii/S0034425720303515 |
en_US |
dc.description.cluster |
Smart Places |
en_US |
dc.description.impactarea |
|
en_US |
dc.identifier.apacitation |
Matthews, M., Bernard, S., Evers-King, H., & Robertson Lain, L. (2020). Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. <i>Remote Sensing of Environment, 248</i>, http://hdl.handle.net/10204/11972 |
en_ZA |
dc.identifier.chicagocitation |
Matthews, MW, Stewart Bernard, H Evers-King, and L Robertson Lain "Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm." <i>Remote Sensing of Environment, 248</i> (2020) http://hdl.handle.net/10204/11972 |
en_ZA |
dc.identifier.vancouvercitation |
Matthews M, Bernard S, Evers-King H, Robertson Lain L. Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. Remote Sensing of Environment, 248. 2020; http://hdl.handle.net/10204/11972. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Matthews, MW
AU - Bernard, Stewart
AU - Evers-King, H
AU - Robertson Lain, L
AB - A hyperspectral inversion algorithm was used to distinguish between cyanobacteria and algal blooms in optically complex inland waters. A framework for the algorithm is presented that incorporates a bio-optical model, a solution for the radiative transfer equation using the EcoLight-S radiative transfer model, and a non-linear optimization procedure. The natural variability in the size of phytoplankton populations was simulated using a two-layered sphere model that generated size-specific inherent optical properties (IOPs). The algorithm effectively determined the type of high-biomass blooms in terms of the relative percentage species composition of cyanobacteria. It also provided statistically significant estimates of population size (as estimated by the effective diameter), chlorophyll-a (chl-a) and phycocyanin pigment concentrations, the phytoplankton absorption coefficient, and the non-algal absorption coefficient. The algorithm framework presented here can in principle be adapted for distinguishing between phytoplankton groups using satellite and in situ remotely sensed reflectance.
DA - 2020-10
DB - ResearchSpace
DP - CSIR
J1 - Remote Sensing of Environment, 248
KW - Algorithms
KW - Bio-optics
KW - Cyanobacteria
KW - Harmful algal blooms
KW - Hyperspectral
KW - Remote sensing
LK - https://researchspace.csir.co.za
PY - 2020
SM - 0034-4257
SM - 1879-0704
T1 - Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm
TI - Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm
UR - http://hdl.handle.net/10204/11972
ER - |
en_ZA |
dc.identifier.worklist |
24227 |
en_US |