@article{Chegoonian-2023-Comparative,
title = "Comparative Analysis of Empirical and Machine Learning Models for Chl{\textless}i{\textgreater}a{\textless}/i{\textgreater} Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges",
author = "Chegoonian, Amir M. and
Pahlevan, Nima and
Zolfaghari, Kiana and
Leavitt, Peter R. and
Davies, John-Mark and
Baulch, Helen M. and
Duguay, Claude and
Chegoonian, Amir M. and
Pahlevan, Nima and
Zolfaghari, Kiana and
Leavitt, Peter R. and
Davies, John-Mark and
Baulch, Helen M. and
Duguay, Claude",
journal = "Canadian Journal of Remote Sensing, Volume 49, Issue 1",
volume = "49",
number = "1",
year = "2023",
publisher = "Informa UK Limited",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G23-18001",
doi = "10.1080/07038992.2023.2215333",
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 (Rrsδ) 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, Rrsδ-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.",
}
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<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 (Rrsδ) 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, Rrsδ-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.</abstract>
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%0 Journal Article
%T Comparative Analysis of Empirical and Machine Learning Models for Chl\textlessi\textgreatera\textless/i\textgreater Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges
%A Chegoonian, Amir M.
%A Pahlevan, Nima
%A Zolfaghari, Kiana
%A Leavitt, Peter R.
%A Davies, John-Mark
%A Baulch, Helen M.
%A Duguay, Claude
%J Canadian Journal of Remote Sensing, Volume 49, Issue 1
%D 2023
%V 49
%N 1
%I Informa UK Limited
%F Chegoonian-2023-Comparative
%X 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 (Rrsδ) 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, Rrsδ-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.
%R 10.1080/07038992.2023.2215333
%U https://gwf-uwaterloo.github.io/gwf-publications/G23-18001
%U https://doi.org/10.1080/07038992.2023.2215333
Markdown (Informal)
[Comparative Analysis of Empirical and Machine Learning Models for Chl<i>a</i> Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges](https://gwf-uwaterloo.github.io/gwf-publications/G23-18001) (Chegoonian et al., GWF 2023)
ACL
- Amir M. Chegoonian, Nima Pahlevan, Kiana Zolfaghari, Peter R. Leavitt, John-Mark Davies, Helen M. Baulch, Claude Duguay, Amir M. Chegoonian, Nima Pahlevan, Kiana Zolfaghari, Peter R. Leavitt, John-Mark Davies, Helen M. Baulch, and Claude Duguay. 2023. Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges. Canadian Journal of Remote Sensing, Volume 49, Issue 1, 49(1).
