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

Chegoonian, Amir M.; Pahlevan, Nima; Zolfaghari, Kiana; Leavitt, Peter R.; Davies, John-Mark; Baulch, Helen M.; Duguay, Claude R.

doi:10.1080/07038992.2023.2215333

Cited by 11 publications

(8 citation statements)

References 105 publications

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“…References [157] and [158] applied SVR to determine Chla. It is noticeable that although the models are the same, their methodology was disparate.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Oliveira Santos,

Guimarães,

Lima Neto

et al. 2024

Preprint

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Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

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“…References [157] and [158] applied SVR to determine Chla. It is noticeable that although the models are the same, their methodology was disparate.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Oliveira Santos,

Guimarães,

Lima Neto

et al. 2024

Preprint

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“…The authors of [143,144] applied SVR to determine the Chla levels. It is noticeable that although the models were the same, their methodology was different.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

“…Regarding reference [143], the RMSE values were within the same order of magnitude and the R 2 values were relatively close to each other. In contrast, the authors of [144] performed a much broader study on several lakes spread across the Chinese territory. As previously mentioned, including a greater number of lakes allows the forecasting model to better generalize its results, thus providing more robust outcomes.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Oliveira Santos,

Guimarães,

Neto

et al. 2024

Remote Sensing

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It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

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“…The Sentinel-2 MSI is an advanced sensor offering a short revisit time and higher spatial (10-60 m) and radiometric resolutions (13 bands with 12 bits) [15]. Chegoonian et al [16] applied Sentinel-2 and Landsat OLI to retrieve Chl-a in Buffalo Pound Lake, which is a small inland reservoir located in the south-central part of Saskatchewan with 30 km 2 of water surface area. However, except for Chegoonian et al [16], there have been very few studies on the application of the Sentinel-2 MSI sensor for estimating Chl-a in a small-scale reservoir with intensive monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Chegoonian et al [16] applied Sentinel-2 and Landsat OLI to retrieve Chl-a in Buffalo Pound Lake, which is a small inland reservoir located in the south-central part of Saskatchewan with 30 km 2 of water surface area. However, except for Chegoonian et al [16], there have been very few studies on the application of the Sentinel-2 MSI sensor for estimating Chl-a in a small-scale reservoir with intensive monitoring. When considering the study area and Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Jang,

Kim,

Kim

et al. 2024

Remote Sensing

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Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

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

Cited by 11 publications

References 105 publications

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

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