A remote sensing approach to ascertain spatial and temporal variations of seawater quality parameters in the coastal area of Bay of Bengal, Bangladesh

Ashikur, M.R.; Rupom, R. S.; Sazzad, M.H.

doi:10.1016/j.rsase.2021.100593

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…As for the salinity sensor, the MAE does not allow the direct use of this sensor for deep water or seawater characterisation. The typical TSS values in these waters are below 100 mg/L [ 48 , 49 , 50 ], and even below 10 mg/L in some areas [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

Parra

Viciano-Tudela

Carrasco

et al. 2023

Sensors

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The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

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Section: Resultsmentioning

confidence: 99%

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

Parra

Viciano-Tudela

Carrasco

et al. 2023

Sensors

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“…A total of 4320 samples were collected from 22 July to 20 October 2021, with an interval of 30 min. After consulting the data, eight factors affecting the marine water quality were selected, including dissolved oxygen (Do), chlorophyll a (Chl), turbidity (Turb), blue-green algae (Bga), total dissolved solids (Tds), dissolved oxygen saturation (DoP), water temperature (Temp), and pH [ 20 , 21 , 22 , 23 , 24 , 25 ]. In order to explore the spatio-temporal characteristics of the marine water quality factors, the water quality factors were gathered from three adjacent stations.…”

Section: Resultsmentioning

confidence: 99%

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Sun

Wang

et al. 2023

Entropy

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In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

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“…Medium-resolution remote sensing sensors, such as SEAWIFS, MODIS, and VIRRS, have been used to understand and monitor water biogeochemical parameters worldwide [10][11][12][13][14][15][16][17][18][19][20]. However, due to the significant spatial variability, mainly in coastal waters, higher spatial resolution remote sensing sensors such as the Landsat Family, i.e., Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), and particularly the Operational Land Image (OLI) temporal series have been recently used to track water quality changes [21][22][23][24][25][26]. Since 2015, multispectral instruments (MSIs) on board the Sentinel-2 satellites have significantly improved spatial and temporal capabilities to study the behavior of biogeochemical parameters in several bays, including San Francisco Bay [27], Kastela Bay [28], the Bengal Bay [29], and Zhanjiang Bay [30].…”

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Turbidity in Cartagena Bay, Colombia, Using Sentinel-2 Imagery

Eljaiek-Urzola,

Sander de Carvalho,

Betancur-Turizo

et al. 2023

Remote Sensing

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The Cartagena Bay in Colombia has vital economic and environmental importance, playing a fundamental role in both the port and tourism sectors. Unfortunately, the water quality of the bay is undergoing a deterioration process due to the significant influx of sediment from the artificial channel known as Canal del Dique. Although field campaigns are carried out semiannually with 12 monitoring stations to evaluate these impacts, understanding the spatial dynamics of suspended solids in the bay remains a challenge. This article presents a spatial analysis of water turbidity in the Cartagena Bay during the years 2018 to 2022, using Sentinel-2 images. To achieve this objective, an empirical algorithm was developed through the Monte Carlo simulation. The validation of the algorithm demonstrated an R-squared value of 0.83, with an RMSE of 2.72 and a MAPE of 24.93%. The results showed the seasonal variability, with higher turbidity levels during the rainy season, reaching up to 35 FNU, and lower turbidities during the dry season, dropping to 1 FNU. Furthermore, these findings indicated that the southern area of the bay presents the most significant turbidity variations. This research enhances our understanding of the bay’s turbidity dynamics and suggests an additional tool for its monitoring.

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A remote sensing approach to ascertain spatial and temporal variations of seawater quality parameters in the coastal area of Bay of Bengal, Bangladesh

Cited by 5 publications

References 36 publications

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Spatial Patterns of Turbidity in Cartagena Bay, Colombia, Using Sentinel-2 Imagery

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