Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes

Lyu, Heng; Li, Xiaojun; Wang, Yannan; Jin, Qi; Cao, Kai; Wang, Qiao; Li, Yunmei

doi:10.1016/j.scitotenv.2015.05.115

Cited by 25 publications

(10 citation statements)

References 45 publications

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“…These results reveal that no single algorithm has the best accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. This finding is consistent with those of recently published results [ 40 , 44 ]. The four-band algorithm produced higher accuracy for high Chla and NAP concentrations, due to the fact that the four-band algorithm was proposed for highly turbid water [ 47 ].…”

Section: Resultssupporting

confidence: 94%

“…The two-band ratio [ 13 , 24 , 25 , 26 ], three-band algorithm [ 15 , 27 , 28 , 29 , 30 ], four-band algorithm [ 31 ], normalized difference chlorophyll index [ 32 , 33 ], maximum chlorophyll index [ 34 ] and synthetic chlorophyll index [ 35 ] were developed for Case 2 waters. These algorithms have been applied in numerous study areas [ 36 , 37 , 38 , 39 , 40 , 41 ]. The optimal bands that were used for each algorithm varied among studies; for instance, Le et al [ 39 ] used the 681-, 709-, and 754-nm MERIS central bands with the maximum chlorophyll index algorithm, while Matsushita et al [ 42 ] employed the 665-, 709-, and 754-nm bands.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Salem

Higa

Kim

et al. 2017

Sensors

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Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m −3 , 16.25 mg·m −3 , and 19.05 mg·m −3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m −3 ), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m −3 ).

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Section: Resultssupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Salem

Higa

Kim

et al. 2017

Sensors

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“…Regarding the feature importance, water surface reflectance at Band 3, followed by Bands 4–7, is the most important contributors to the estimation of Chl‐a (Figure 9). This result agrees well with the previous observations that spectral bands beyond 650 nm (Band 3) are usually appropriate for the development of Chl‐a retrieval models for inland waters, particularly in Case 2 waters in which the Chl‐a concentration is above 10 μg/L (Kravitz et al, 2020; Lyu et al, 2015; Nguyen et al, 2020; Pahlevan et al, 2020; Song et al, 2012). Despite a minor contribution to the retrieval of Chl‐a, it is noted that Bands 1 and 2 can still explain the variation of Chl‐a in the TAR (Figure 9), suggesting that the use of selected Sentinel‐2 MSI bands (Bands 1–7) might benefit an accurate estimation of Chl‐a in eutrophic inland lakes.…”

Section: Discussionsupporting

confidence: 91%

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam

Nguyen

Nguyen-Ngoc

et al. 2021

Water Environment Research

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Chlorophyll‐a (Chl‐a) is one of the most important indicators of the trophic status of inland waters, and its continued monitoring is essential. Recently, the operated Sentinel‐2 MSI satellite offers high spatial resolution images for remote water quality monitoring. In this study, we tested the performance of the three well‐known machine learning (ML) (random forest [RF], support vector machine [SVM], and Gaussian process [GP]) and the two novel ML (extreme gradient boost (XGB) and CatBoost [CB]) models for estimation a wide range of Chl‐a concentration (10.1–798.7 μg/L) using the Sentinel‐2 MSI data and in situ water quality measurement in the Tri An Reservoir (TAR), Vietnam. GP indicated the most reliable model for predicting Chl‐a from water quality parameters (R2 = 0.85, root‐mean‐square error [RMSE] = 56.65 μg/L, Akaike's information criterion [AIC] = 575.10, and Bayesian information criterion [BIC] = 595.24). Regarding input model as water surface reflectance, CB was the superior model for Chl‐a retrieval (R2 = 0.84, RMSE = 46.28 μg/L, AIC = 229.18, and BIC = 238.50). Our results indicated that GP and CB are the two best models for the prediction of Chl‐a in TAR. Overall, the Sentinel‐2 MSI coupled with ML algorithms is a reliable, inexpensive, and accurate instrument for monitoring Chl‐a in inland waters. Practitioner points Machine learning algorithms were used for both remote sensing data and in situ water quality measurements. The performance of five well‐known machine learning models was tested Gaussian process was the most reliable model for predicting Chl‐a from water quality parameters CatBoost was the best model for Chl‐a retrieval from water surface reflectance

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“…Such physical features limited the use of most sensors with free satellite imagery, which have spatial and temporal resolutions larger than 500 m and one week, respectively. These criteria resulted in the selection of four satellite sensors (Table 2) MODIS and MERIS sensors have been widely used to estimate chlorophyll-a in inland and coastal waters [35,40,46], which can allow time-series reconstitution of the last 20 years. The recently available sensors MSI and OLCI (successor of MERIS) have a good spatial and temporal resolution and new spectral bands, which were positioned at strategic locations to improve chlorophyll-a estimates.…”

Section: Retrieval Of Chlorophyll-a Using Models Based On Simulated Smentioning

confidence: 99%

“…Thus, major efforts have been made in the last decade to test and evaluate different algorithms to estimate chlorophyll-a in inland and coastal waters using multispectral data from datasets with different sources, such as handheld, airborne or spaceborne sensors [30][31][32][33][34][35][36][37][38][39][40][41][42][43]. In turbid and productive estuaries, the best empirical approaches for estimating chlorophyll-a have been obtained with the use of NIR-Red models [44,45].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

et al. 2017

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Remote estimation of chlorophyll-a in turbid and productive estuaries is difficult due to the optical complexity of Case 2 waters. Although recent advances have been obtained with the use of empirical approaches for estimating chlorophyll-a in these environments, the understanding of the relationship between spectral reflectance and chlorophyll-a is based mainly on temperate and subtropical estuarine systems. The potential to apply standard NIR-Red models to productive tropical estuaries remains underexplored. Therefore, the purpose of this study is to evaluate the performance of several approaches based on multispectral data to estimate chlorophyll-a in a productive tropical estuarine-lagoon system, using in situ measurements of remote sensing reflectance, R rs . The possibility of applying algorithms using simulated satellite bands of modern and recent launched sensors was also evaluated. More accurate retrievals of chlorophyll-a (r 2 > 0.80) based on field datasets were found using NIR-Red three-band models. In addition, enhanced chlorophyll-a retrievals were found using the two-band algorithm based on bands of recently launched satellites such as Sentinel-2/MSI and Sentinel-3/OLCI, indicating a promising application of these sensors to remotely estimate chlorophyll-a for coming decades in turbid inland waters. Our findings suggest that empirical models based on optical properties involving water constituents have strong potential to estimate chlorophyll-a using multispectral data from satellite, airborne or handheld sensors in productive tropical estuaries.

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Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes

Cited by 25 publications

References 45 publications

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam

Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

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