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Adhikary S, Tiwari SP, Banerjee S, Dwivedi AD, Rahman SM. Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing. PeerJ 2024; 12:e17361. [PMID: 38737741 PMCID: PMC11088370 DOI: 10.7717/peerj.17361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.
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Affiliation(s)
| | | | | | | | - Syed Masiur Rahman
- King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
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2
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Zhang X, Li C, Zheng Y, Liu C, Zhou W, Xu Z, Yang Z, Yang Y, Cao W. Approach for estimating the vertical distribution of the diffuse attenuation coefficient in the South China Sea. OPTICS EXPRESS 2023; 31:43771-43789. [PMID: 38178466 DOI: 10.1364/oe.503850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
The vertical distribution of the diffuse attenuation coefficient K(z, λ) is critical for studies in bio-optics, ocean color remote sensing, underwater photovoltaic power, etc. It is a key apparent optical property (AOP) and is sensitive to the volume scattering function β(ψ, z, λ). Here, using three machine learning algorithms (MLAs) (categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF)), we developed a new approach for estimating the vertical distribution of Kd(z, 650), KLu(z, 650), and Ku(z, 650) and applied it to the South China Sea (SCS). In this approach, based on in situ β(ψ, z, 650), the absorption coefficient a(z, 650), the profile depths z, and Kd(z, 650), KLu(z, 650), and Ku(z, 650) calculated by Hydrolight 6.0 (HL6.0), three machine learning models (MLMs) without or with boundary conditions for estimating Kd(z, 650), KLu(z, 650), and Ku(z, 650) were established, evaluated, compared, and applied. It was found that (1) CatBoost models have superior performance with R2 ≥ 0.92, RMSE≤ 0.021 m-1, and MAPE≤ 4.3% and most significantly agree with HL6.0 simulations; (2) there is a more satisfactory consistency between HL6.0 simulations and MLMs estimations while incorporating the boundary conditions; (3) the estimations of Kd(z, 650), KLu(z, 650), and Ku(z, 650) derived from CatBoost models with and without boundary conditions have a good agreement with R2 ≥0.992, RMSE ≤0.007 m-1, and MAPE≤0.8%, respectively; (4) there is an overall decreasing trend with increasing depth and increasing offshore distance of Kd(z, 650), KLu(z, 650), and Ku(z, 650) in the SCS. The MLMs for estimating K(z, λ) could provide more accurate information for the study of underwater light field distribution, water quality assessment and the validation of remote sensing data products.
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Otálora S, Segatto MEV, Monteiro ME, Múnera M, Díaz CAR, Cifuentes CA. Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:9291. [PMID: 38005677 PMCID: PMC10674769 DOI: 10.3390/s23229291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/08/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023]
Abstract
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.
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Affiliation(s)
- Sophia Otálora
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | - Marcelo E. V. Segatto
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | | | - Marcela Múnera
- Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK;
| | - Camilo A. R. Díaz
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | - Carlos A. Cifuentes
- Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK;
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Lapucci C, Antonini A, Böhm E, Organelli E, Massi L, Ortolani A, Brandini C, Maselli F. Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters. SENSORS (BASEL, SWITZERLAND) 2023; 23:9258. [PMID: 38005644 PMCID: PMC10675379 DOI: 10.3390/s23229258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023]
Abstract
Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model's performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.
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Affiliation(s)
- Chiara Lapucci
- National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (E.B.); (C.B.)
- LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (A.A.); (A.O.)
| | - Andrea Antonini
- LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (A.A.); (A.O.)
| | - Emanuele Böhm
- National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (E.B.); (C.B.)
| | - Emanuele Organelli
- National Research Council (CNR), Institute of Marine Science (ISMAR), Via Fosso del Cavaliere 100, 00133 Rome, Italy;
| | - Luca Massi
- Dipartimento di Biologia, Università Degli Studi di Firenze, Via Micheli 1, 50121 Florence, Italy;
| | - Alberto Ortolani
- LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (A.A.); (A.O.)
- National Research Council (CNR), Institute for BioEconomy (IBE), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Carlo Brandini
- National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (E.B.); (C.B.)
- LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy; (A.A.); (A.O.)
| | - Fabio Maselli
- National Research Council (CNR), Institute for BioEconomy (IBE), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
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Hu H, Fu X, Li H, Wang F, Duan W, Zhang L, Liu M. Prediction of lake chlorophyll concentration using the BP neural network and Sentinel-2 images based on time features. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 87:539-554. [PMID: 36789702 DOI: 10.2166/wst.2023.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
One of the most important indicators of lake eutrophication is chlorophyll-a (Chl-a) concentration, which is also an essential component of lake water quality monitoring. It is an efficient, economical and convenient method to monitor the Chl-a concentration through remote sensing images. Taking the Wuliangsuhai Lake as an example, the relevant bands of Sentinel-2 images were used as the input and the Chl-a concentration as the output to build neural network models. In the process of building the model, we mainly studied and tested the impact of adding time features to the model input on the model accuracy. Through the experiment, it was found that the month and day difference features of remote sensing images and Chl-a measurement could significantly improve the prediction accuracy of Chl-a concentration in varying degrees. Finally, it was determined that the neural network prediction model with 12 bands of Sentinel-2 images combined month features as inputs and one hidden layer, eight neurons and Chl-a concentration as outputs was the best. Then, the accuracy of the model was validated when the test set accounts for 20 and 30%, and good results were obtained.
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Affiliation(s)
- Hua Hu
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China ; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, China
| | - Xueliang Fu
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China ; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, China
| | - Honghui Li
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Fang Wang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Weijun Duan
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Liqian Zhang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Min Liu
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
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Zhu X, Guo H, Huang JJ, Tian S, Xu W, Mai Y. An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 323:116187. [PMID: 36261960 DOI: 10.1016/j.jenvman.2022.116187] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.
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Affiliation(s)
- Xiaotong Zhu
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Hongwei Guo
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Jinhui Jeanne Huang
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China.
| | - Shang Tian
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Wang Xu
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
| | - Youquan Mai
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
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Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method. REMOTE SENSING 2022. [DOI: 10.3390/rs14143494] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.
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Gao B, Balyan V. Construction of a financial default risk prediction model based on the LightGBM algorithm. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer (P2P) companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM (Light Gradient Boosting Machine) algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms.
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Affiliation(s)
- Bo Gao
- School of Management Engineering, Henan University of Engineering , Zhengzhou , Henan 451191 , China
| | - Vipin Balyan
- Department of Electrical, Electronics and Computer Engineering, Cape Peninsula University of Technology , Cape Town , South Africa
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Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors. REMOTE SENSING 2022. [DOI: 10.3390/rs14010229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.
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Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea). WATER 2021. [DOI: 10.3390/w13141903] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.
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