Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm

Li, Sijia; Song, Kaishan; Wang, Shuai; Liu, Ge; Wen, Zhidan; Shang, Yingxin; Lyu, Lili; Chen, Fangfang; Xu, Shiqi; Tao, Hui; Du, Yu‐Cang; Fang, Chong; Mu, Guangyi

doi:10.1016/j.scitotenv.2021.146271

Cited by 110 publications

(76 citation statements)

References 56 publications

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“…For chl-a estimation, the model is based on the spectral inversion procedure, where the specific absorption spectra of phytoplankton are used for the calculation of the chl-a concentration (R 2 = 0.82). Recently, Li et al [28] proposed a machine learning method for the estimation of chl-a concentration from Sentinel-2 imagery in 45 Chinese lakes with trophy varying from oligotrophic to hypertrophic. The authors created three clusters of R rs and identified the best machine learning algorithms by testing linear regression model, support vector machine model and Catboost model for the estimation of chl-a and other water quality parameters for each cluster (R 2 = 0.50, 0.64 and 0.79 for clusters 1, 2 and 3).…”

Section: Introductionmentioning

confidence: 99%

The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

et al. 2021

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Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.

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Section: Introductionmentioning

confidence: 99%

The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

et al. 2021

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“…Selection of kernel is important with a level of impact on the model accuracy, however, might be a challenge because the work requires knowledge of expertise and practical experiences, and studies on the kernel performance are not frequently reported in the literature. With the advantages of nonlinear relationships quantification, well studied, and simplicity in model structure (Li et al, 2021), RBF is slightly better performance than the sigmoid kernel (Hong et al, 2017) and has been the selected kernel for SVM implementation in various studies with success (Li et al, 2018; Xie et al, 2012). Hence, the RBF kernel was used in this study.…”

Section: Methodsmentioning

confidence: 99%

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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“…This method uses internal implicit networks and structures to determine the complex characteristics of input data and obtain explicit relationships among the output variables [12,13]. Several approaches, including the decision tree [12,14], BP neural network [15,16], support vector machine model (SVM) [17,18], and extreme machine learning approaches [19], have strong adaptability, fault tolerance, and organization of data.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

et al. 2021

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Remote sensing for the monitoring of chlorophyll-a (Chl-a) is essential to compensate for the shortcomings of traditional water quality monitoring, strengthen red tide disaster monitoring and early warnings, and reduce marine environmental risks. In this study, a machine learning approach called the Gradient-Boosting Decision Tree (GBDT) was employed to develop an algorithm for estimating the Chl-a concentrations of the coastal waters of the Beibu Gulf in Guangxi, using Landsat 8 OLI image data as the image source in combination with field measurements of Chl-a concentrations. The GBDT model with B4, B3 + B4, B3, B1 − B4, B2 + B4, B1 + B4, and B2 − B4 as input features exhibited higher accuracy (MAE = 0.998 μg/L, MAPE = 19.413%, and RMSE = 1.626 μg/L) compared with different physics models, providing a new method for remote sensing inversion of water quality parameters. The GBDT model was used to study the spatial distribution and temporal variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi from 2013 to 2020. The results showed a spatial distribution with high concentrations in nearshore waters and low concentrations in offshore waters. The Chl-a concentration exhibited seasonal changes (concentration in summer > autumn > spring ≈ winter).

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Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm

Cited by 110 publications

References 56 publications

The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

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

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

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