Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

Blix, Katalin; Li, Juan; Massicotte, Philippe; Matsuoka, Atsushi

doi:10.3390/rs11182076

Cited by 15 publications

(12 citation statements)

References 51 publications

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“…The result confirmed that the MDN method exhibits better performance than the empirical models [27]. Other machine learning methods have been found to be successful in the study of water quality parameter inversion, including hidden Markov models, self-organizing decision trees, and Gaussian process regression [2,15,[28][29][30][31][32][33][34]. These methods can effectively resolve the nonlinear relationship between water-color parameters and remote sensing signals in ocean color retrieval.…”

Section: Introductionsupporting

confidence: 55%

“…In the coastal regions, due to the impacts of climate change and intensive human activities, such as rainfall, sewage discharge, and overfishing, eutrophic and polluted water bodies are imported into coastal waters through surface runoff, thus threatening the already-deteriorating coastal water quality [1]. Chlorophyll-a (chl-a) is the main pigment in phytoplankton for photosynthesis and is regarded as a proxy for biomass in water [2,3]. Appropriate biomass is important for maintaining the balance of a healthy aquatic ecosystem.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chen

et al. 2021

Remote Sensing

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Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

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Section: Introductionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chen

et al. 2021

Remote Sensing

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“…Smith et al suggested that an algorithm should be locally trained to learn the non-linearity of the functional dependence between the reflected water leaving radiance and Chla concentrations [52]. More recently, ML-based methods trained locally on the area under observation have attracted researchers due to the improved performance [27], [29], [32], [53].…”

Section: Related Workmentioning

confidence: 99%

“…The most widely explored ML methods include Artificial Neural Networks (ANNs) [24], Support Vector Regression (SVR) [25], Relevance Vector Regression (RVR) [26], Random Forests (RF) [27], Gaussian Process Regression (GPR) [28], [29], and Mixture Density Networks (MDN) [8]. The ANNs due to their ability to learn highly, nonlinear relationships have attracted many researchers [24], [30]- [32].…”

Section: Introductionmentioning

confidence: 99%

Improving Chlorophyll-A Estimation From Sentinel-2 (MSI) in the Barents Sea Using Machine Learning

Asim

Brekke

Mahmood

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper addresses methodologies for remote sensing of ocean Chlorophyll-a (Chl-a), with emphasis on the Barents Sea. We aim at improving the monitoring capacity by integrating in-situ Chl-a observations and optical remote sensing to locally train Machine Learning (ML) models. For this purpose, in-situ measurements of Chl-a ranging from 0.014-10.81mg/m 3 , collected for the years 2016-2018, were used to train and validate models. To accurately estimate Chl-a, we propose to use additional information on pigment content within the productive column by matching the depth-integrated Chl-a concentrations with the satellite data. Using the optical images captured by the Multi-Spectral Imager Instrument on Sentinel-2 and the in-situ measurements, a new spatial windowbased match-up dataset creation method is proposed to increase the number of match-ups and hence improve the training of the ML models. The match-ups are then filtered to eliminate erroneous samples based on the spectral distribution of the remotely sensed reflectance. In addition, we design and implement a neural network model dubbed as the Ocean Color Net (OCN), that has performed better than existing ML-based techniques, including the Gaussian Process Regression (GPR), regionally tuned empirical techniques, including the Ocean Color (OC3) algorithm and the spectral band ratios, as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks. The proposed OCN model achieved reduced Mean Absolute Error compared to the GPR by 5.2%, C2RCC by 51.7%, OC3 by 22.6%, and spectral band ratios by 29%. Moreover, the proposed spatial window and depth-integrated match-up creation techniques improved the performance of the proposed OCN by 57%, GPR by 41.9%, OC3 by 5.3%, and spectral band ratio method by 24% in terms of RMSE compared to the conventional match-up selection approach.

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“…Neural networks [26,[33][34][35] have been proven to have higher accuracies in TP concentration estimation. Their results show that the correlation between remote-sensing imagery and TP concentration can be modeled by complex neural networks.…”

Section: Introductionmentioning

confidence: 99%

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

Ding

et al. 2020

Water

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The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with R2 > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and R2 > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.

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Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

Cited by 15 publications

References 51 publications

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Improving Chlorophyll-A Estimation From Sentinel-2 (MSI) in the Barents Sea Using Machine Learning

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

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