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dc.contributor.authorHadjal, Madjid
dc.contributor.authorPaterson, Ross
dc.contributor.authorMcKee, David
dc.date.accessioned2024-02-15T12:32:53Z
dc.date.available2024-02-15T12:32:53Z
dc.date.issued2023-02-16
dc.description.abstractEstimation of chlorophyll (CHL) using ocean colour remote sensing (OCRS) signals in coastal waters is difficult due to the presence of two other constituents altering the light signal: coloured dissolved organic material (CDOM) and mineral suspended sediments (MSS). Artificial neural networks (NNs) have the capacity to deal with signal complexity and are a potential solution to the problem. Here NNs are developed to operate on two datasets replicating MODIS Aqua bands simulated using Hydrolight 5.2. Artificial noise is added to the simulated signal to improve realism. Both datasets use the same ranges of in water constituent concentrations, and differ by the type of logarithmic concentration distributions. The first uses a Gaussian distribution to simulate samples from natural water conditions. The second uses a flat distribution and is intended to allow exploration of the impact of undersampling extremes at both high and low concentrations in the Gaussian distribution. The impact of the concentration distribution structure is assessed and no benefits were found by switching to a flat distribution. The normal distribution performs better because it reduces the number of low concentration samples that are relatively difficult to resolve against varying concentrations of other constituents. In this simulated environment NNs have the capacity to estimate CHL with outstanding performance compared to real in situ algorithms, except for low values when other constituents dominate the light signal in coastal waters. CDOM and MSS can also be predicted with very high accuracies using NNs. It is found that simultaneous retrieval of all three constituents using multitask learning (MTL) does not provide any advantage over single parameter retrievals. Finally it is found that increasing the number of wavebands generally improves NN performance, though there appear to be diminishing returns beyond ~8 bands. It is also shown that a smaller number of carefully selected bands performs better than a uniformly distributed band set of the same size. These results provide useful insight into future performance for NNs using hyperspectral satellite sensors and highlight specific wavebands benefits.en_US
dc.identifier.citationHadjal, Paterson, McKee. Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions. Frontiers in Remote Sensing. 2023;4en_US
dc.identifier.cristinIDFRIDAID 2244688
dc.identifier.doi10.3389/frsen.2023.973944
dc.identifier.issn2673-6187
dc.identifier.urihttps://hdl.handle.net/10037/32943
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.relation.journalFrontiers in Remote Sensing
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleNeural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditionsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)