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dc.contributor.authorWerther, Mortimeren_UK
dc.contributor.authorOdermatt, Danielen_UK
dc.contributor.authorSimis, Stefan G Hen_UK
dc.contributor.authorGurlin, Danielaen_UK
dc.contributor.authorJorge, Daniel S Fen_UK
dc.contributor.authorLoisel, Huberten_UK
dc.contributor.authorHunter, Peter Den_UK
dc.contributor.authorTyler, Andrew Nen_UK
dc.contributor.authorSpyrakos, Evangelosen_UK
dc.description.abstractRemote sensing product uncertainties for phytoplankton chlorophyll-a (chla) concentration in oligotrophic and mesotrophic lakes and reservoirs were characterised across 13 existing algorithms using an in situ dataset of water constituent concentrations, inherent optical properties (IOPs) and remote-sensing reflectance spectra collected from 53 lakes and reservoirs (346 observations; chla concentration < 10 mg m-3, dataset median 2.5 mg m-3). Substantial shortcomings in retrieval accuracy were evident with median absolute percentage differences (MAPD) > 37% and mean absolute differences (MAD) > 1.82 mg m-3. Using the Hyperspectral Imager for the Coastal Ocean (HICO) band configuration improved the accuracies by 10–20% compared to the Ocean and Land Colour Instrument (OLCI) configuration. Retrieval uncertainties were attributed to optical and biogeochemical properties using machine learning models through SHapley Additive exPlanations (SHAP). The chla retrieval uncertainty of most semi-analytical algorithms was primarily determined by phytoplankton absorption and composition. Machine learning chla algorithms showed relatively high sensitivity to light absorption by coloured dissolved organic matter (CDOM) and non-algal pigment particulates (NAP). In contrast, the uncertainties of red/near-infrared algorithms, which aim for lower uncertainty in the presence of CDOM and NAP, were primarily explained through the total absorption by phytoplankton at 673 nm and variables related to backscatter. Based on these uncertainty characterisations we discuss the suitability of the evaluated algorithm formulations, and we make recommendations for chla estimation improvements in oligo- and mesotrophic lakes and reservoirs.en_UK
dc.publisherElsevier BVen_UK
dc.relationWerther M, Odermatt D, Simis SGH, Gurlin D, Jorge DSF, Loisel H, Hunter PD, Tyler AN & Spyrakos E (2022) Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs. ISPRS Journal of Photogrammetry and Remote Sensing, 190, pp. 279-300.
dc.rightsThis is an open access article distributed under the terms of the Creative Commons CC-BY license (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.en_UK
dc.subjectShapley additive explanationsen_UK
dc.subjectMachine learningen_UK
dc.titleCharacterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirsen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleISPRS Journal of Photogrammetry and Remote Sensingen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEuropean Commission (Horizon 2020)en_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationSwiss Federal Institute of Aquatic Science and Technology (Eawag)en_UK
dc.contributor.affiliationPlymouth Marine Laboratoryen_UK
dc.contributor.affiliationWisconsin Department of Natural Resourcesen_UK
dc.contributor.affiliationUniversity of Littoral Côte d'Opaleen_UK
dc.contributor.affiliationUniversity of Littoral Côte d'Opaleen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.relation.funderprojectMultiscale Observation Networks for Optical Monitoring of Coastal Waters, Lakes and Estuariesen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorWerther, Mortimer|en_UK
local.rioxx.authorOdermatt, Daniel|en_UK
local.rioxx.authorSimis, Stefan G H|en_UK
local.rioxx.authorGurlin, Daniela|en_UK
local.rioxx.authorJorge, Daniel S F|en_UK
local.rioxx.authorLoisel, Hubert|en_UK
local.rioxx.authorHunter, Peter D|0000-0001-7269-795Xen_UK
local.rioxx.authorTyler, Andrew N|0000-0003-0604-5827en_UK
local.rioxx.authorSpyrakos, Evangelos|en_UK
local.rioxx.project776480|European Commission (Horizon 2020)|en_UK
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