Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Abstract

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra ($R_{rs}^{\delta }$) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, $R_{rs}^{\delta }$-fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m⁻³) were produced using a coupled SVR-iCOR system.

RÉSUMÉ

L’extraction à distance de la concentration de chlorophylle-a (Chla) près de la surface dans les petites eaux intérieures est difficile en raison des interférences optiques importantes de divers constituants de l’eau et des incertitudes dans le processus de correction atmosphérique (CA). Bien que divers algorithmes aient été développés pour estimer Chla à partir de missions terrestres à résolution modérée (∼10–60 m), la production de cartes de répartition précises et de séries chronologiques de Chla s’est avérée difficile, limitant l’utilisation d’analyses à distance pour la surveillance des lacs. Ici, nous développons un modèle de régression vectorielle de support (RVS), qui utilise des spectres de réflectance dérivés de satellites ($R_{rs}^{\delta }$) utilisant des images Sentinel-2 et Landsat-8 comme entrée pour la récupération de la Chla d’un lac eutrophique des prairies représentatif, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validé à partir des données Chla in situ de sept saisons sans glace (N ∼ 200; 2014–2020), le modèle SVR a surpassé à la fois les modèles empiriques à réglage local, alimentés en ($R_{rs}^{\delta }$) (indice de chlorophylle par différence normalisée, bandes 2 et 3 et OC3) et les réseaux de densité de mélange (MDN) de 15% à 65%, tout en présentant des performances comparables à celles d’un MDN formé localement, avec une erreur de ∼35%. La comparaison des modèles de récupération de Chla, des processeurs AC (iCOR, ACOLITE) et des produits radiométriques (correction de Rayleigh, réflectance de surface et de la haute atmosphère) a montré que les meilleures cartes Chla et les séries chronologiques optimales (jusqu’à 100 mg m⁻³) ont été produites à l’aide d’un système SVR-iCOR couplé.

Acknowledgments

We thank members of University of Saskatchewan (US) Global Institute for Water Security and the University of Regina (UR) Limnology Laboratory for field data collection, as well as David Vandergucht and seasonal staff with the Saskatchewan Water Security Agency, and the Buffalo Pound Water Treatment Plant (BPWTP). Operations of the monitoring buoy were supported by Jay Bauer, Katy Nugent, Cameron Hoggarth, and staff of BPWTP. We gratefully acknowledge that the field research and UR analyses took place on Treaty 4 territory, homelands of the Cree, Saulteaux, Lakota, Dakota, and Nakota peoples, as well as the Metis/Michief nation. US is located on Treaty 6 territory, while University of Waterloo is located on the traditional territory of the Neutral, Anishinaabeg, and Haudenosaunee peoples.

Author contributions

Conceptualization: AMC; limnological in situ data: PRL, HMB, and JMD; method development: AMC, NP, and KZ; data analysis: AMC; manuscript preparation: AMC; editing and approval: All authors; funding: CRD, PRL, HMB, and NP.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Buoy operations and associated research supported by grants to HMB from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Global Water Futures (GWF)-Canada First Research Excellence Fund (FORMBLOOM project), Global Institute for Water Security, and Buffalo Pound Water Treatment Plant. Qu’Appelle Long-term Ecological Research program (QU-LTER) was supported by grants to PRL from NSERC, CFI, Canada Research Chairs, the Province of Saskatchewan, and University of Regina. NP was supported under the NASA ROSES contract #80HQTR19C0015, Remote Sensing of Water Quality element, and the USGS Landsat Science Team Award #140G0118C0011. AMC, CRD and KZ were supported by GWF TTSW (Transformative sensor Technologies and Smart Watersheds for Canadian Water Futures) project.