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Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs
Author(s): Werther, Mortimer
Odermatt, Daniel
Simis, Stefan G H
Gurlin, Daniela
Jorge, Daniel S F
Loisel, Hubert
Hunter, Peter D
Tyler, Andrew N
Spyrakos, Evangelos
Keywords: Chlorophyll-a
Shapley additive explanations
Machine learning
Issue Date: Aug-2022
Date Deposited: 20-Jul-2022
Citation: Werther 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.
Abstract: Remote 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.
DOI Link: 10.1016/j.isprsjprs.2022.06.015
Rights: This 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.
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