Environmental monitoring of water quality in coastal sea area using remote sensing and modeling

Kõuts, Tarmo; Sipelgas, Liis; Savinits, Natalja; Raudsepp, Urmas

doi:10.1109/baltic.2006.7266166

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…Miller et al (2005) used MODIS 250 red band reflectance data to calibrate and validate the output of sediment transport model (ECOMSED) through a comparison of the model predicted SPM concentrations with SPM images derived from cloud-free MODIS images in the Lake Pontchartrain, LA, USA. Kouts et al (2007) combined satellite remote sensing with numerical modeling, consisting of a hydrodynamic model, a particle transport model and a benthic macroalgae growth model, to calculate distributions of SPM. Then comparison of SPM distributions from remote sensing images and numerical model results showed qualitatively similar patterns.…”

mentioning

confidence: 99%

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Chen

Cui

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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mentioning

confidence: 99%

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Chen

Cui

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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“…For example, Jensen et al (1989) mapped the SPM concentration distribution to help understand and manage complex physical processes in coastal lagoons by integrating numerical simulation and remote sensing retrieval results. Likewise, Kõuts et al (2007) evaluated the water quality of Pakri Bay, the southern Gulf of Finland, using satellite RS and three numerical simulation models, including the hydrodynamic model, particle transport model, and benthic macro algae growth model. Together with physical and causal models, exploring novel applications of data assimilation provides more opportunities for water environment monitoring.…”

Section: Estimation Models Combined With Data Assimilationmentioning

confidence: 99%

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Chen

et al. 2022

Earth's Future

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Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state-of-the-art methods for water extraction, including threshold-based methods, water indices, and machine learning-based methods; (c) state-of-the-art models for quantitative estimation of water quality, including empirical models, semi-empirical/semi-analytical models, and machine learning-based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to "low spatiotemporal resolution," and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.

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“…This is because modeling living organisms, such as algae, involves many uncertainties. Therefore, remote sensing data from ungauged areas can be utilized to confirm and validate modeling results [ 22 , 23 ]. However, remote sensing data have not yet been directly applied to model future algal blooms.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

Ahn

Kim

Jong

et al. 2021

Sensors

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Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.

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Environmental monitoring of water quality in coastal sea area using remote sensing and modeling

Cited by 8 publications

References 11 publications

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

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