Turbidity Monitoring Research Articles

Real-Time and Long-Term Monitoring of Coastal Water Turbidity Using an Ocean Buoy Equipped with an ADCP.

In this study, an acoustic Doppler current profiler (ADCP) operating at 600 kHz was installed on an ocean data buoy in the Qigu waters, Taiwan, to gather real-time sound echo intensity data. These data were then correlated with turbidity measurements obtained by a turbidimeter mounted on the buoy's mooring line at a water depth of 13 m. The data buoy operated from 6 June to 16 August 2017. During this period, turbidity measurements were recorded from 6 to 21 June 2017. This study established a calibration between the sound echo intensity measured by the ADCP and the turbidity measured using the turbidimeter; a strong linear correlation was discovered between these two variables. This correlation enabled the conversion of echo intensity data into a continuous time series of turbidity measurements, facilitating real-time and long-term monitoring of coastal water turbidity through the deployment of a buoy equipped with an ADCP. The relationships between turbidity and environmental factors such as rainfall, tides, current speeds, and wave activity over an extended period were then investigated. The results revealed that stronger tides and currents in the Qigu waters often lead to higher turbidity, suggesting that these two factors are the primary driving forces for sediment transport in the Qigu waters. Additionally, sampling of water in the Qigu area revealed sediment particles of size ranging from 2 to 120 μm.

A smart, multi-configuration, and low-cost system for water turbidity monitoring

Measuring the amount of suspended sediment in rivers is important for a better understanding of phenomena like soil erosion or floods. Since such phenomena are not spatially homogeneous, a distributed measurement approach is required to obtain fine-grained and realistic models. We designed and implemented a low-cost, flexible, and smart suspended sediment concentration monitor. The system only uses components that are easy to find and assemble. This, together with the reduced costs, enables the use of multiple instances for distributed monitoring. The system can operate according to different configurations, fixed or mobile, and using a variety of connection technologies, to possibly cover the various necessities that arise from the diverse installation sites. Each measuring unit automatically retrieves its operational parameters, easing the configuration of a possibly large distributed measurement system.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms—Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)—to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.

Intelligent classification of water bodies with different turbidity levels based on Gaofen-1 multispectral imagery

The turbidity of water is crucial for the health of river and lake ecosystems, necessitating efficient monitoring for effective water management. Existing methods for studying water turbidity's spatial and temporal distribution rely mostly on measured data. There is limited research on the classification of water bodies with different turbidity levels. The main challenge lies in determining the boundaries of liquid water bodies at various turbidity levels, making it challenging to classify them accurately using traditional remote sensing image classification methods. This paper proposes and validates an intelligent turbidity classification method based on deep learning using GaoFen-1 multispectral remote sensing imagery. An adaptive threshold water extraction method based on the Normalized Difference Water Index is proposed to capture water boundaries more accurately to improve the accuracy of extracting nearshore water bodies. A semi-automatic semantic annotation method for water turbidity is introduced to reduce manual labeling costs. The paper applies mode filtering to address edge noise issues and establishes a high-quality training sample dataset. After comparing the accuracy of various neural network models, DeepLab V3+ is selected for intelligent turbidity classification. The results show high accuracy, with mean intersection over union (MIoU), mean F1 score (MF1), and overall accuracy (OA) reaching 94.73%, 97.29%, and 97.54%, respectively. The proposed method and experiments demonstrate the feasibility of intelligent classification of water bodies with different turbidity levels using deep learning networks. This provides a new approach for large-scale and efficient remote sensing water turbidity monitoring.

Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

ABSTRACT Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed data and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 log10(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and has potential for improving input data quality for numerical models and developing predictive models from remote sensing data.

A Framework for Characterizing Spatio-Temporal Variation of Turbidity and Drivers in the Navigable and Turbid River: A Case Study of Xitiaoxi River

Turbidity, as a key indicator of water quality linked to underwater light attenuation, is crucial for evaluating water quality. Control in high-turbidity water environments plays a critical role in navigable rivers. For this purpose, our study proposed a framework for analyzing the spatio-temporal variation of turbidity and its driving factors in a navigable and turbid river using in situ measurement data, satellite data, socioeconomic data, a power index function model, and correlation analysis. The results show that the proposed model is feasible for quantitative turbidity monitoring of the Xitiaoxi River. Its upstream turbidity is lower than downstream, with seasonal averages for spring, summer, autumn, and winter of 93.9, 111.3, 113.5, and 120.9 NTU, respectively. Furthermore, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005 and began to decline after 2005 due to the policy of mining moratorium. This trend is especially noticeable at monitoring points along the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and Huxi River (S8). Mining and shipping have significantly contributed to the turbidity of the target river. This framework offers a practical approach for assessing the environmental impacts of both natural and anthropogenic factors, thereby providing valuable insights for river management practices.

Design and Implementation of Water PH Level and Turbidity Monitoring System

Abstract: Building collapses have become a distressing and recurring issue in Nigeria, necessitating urgent action to mitigate their occurrence. This paper introduces an innovative approach to address this challenge by developing an Automated Structural Health Monitoring System (ASHMS) utilizing a mobile app. Focusing on major cities like Lagos and Abuja, the proposed system employs strategically placed sensors to gather data from various structural elements of buildings. The collected information, encompassing seismic activity, vibrations, bending moments, shear forces, and other key parameters, is wirelessly transmitted to the cloud where it can then be accessed and visualized using mobile app. The system offers real-time insights into the structural integrity of buildings and provides early warnings, thereby contributing significantly to reducing the risk of building collapses. Key findings show that when incorporated into an automated Structural Health Monitoring (SHM) system, the user's mobile app offers essential structural insights, capturing values such as a 0.5-unit vibration level, 20 units of shear force, 50 units of bending moment, and an 89-degree vertical wall angle. The app's userfriendly interface improves the effectiveness of monitoring and overseeing structural health, in line with the overarching objective of promoting safety in construction practices

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMGs), two piezoelectric nanogenerators (PENGs), and two multilayer-structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm−3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

A Comparative Study of Multi-Rotor Unmanned Aerial Vehicles (UAVs) with Spectral Sensors for Real-Time Turbidity Monitoring in the Coastal Environment

Complex coastal environments pose unique logistical challenges when deploying unmanned aerial vehicles (UAVs) for real-time image acquisition during monitoring operations of marine water quality. One of the key challenges is the difficulty in synchronizing the images acquired by UAV spectral sensors and ground-truth in situ water quality measurements for calibration, due to a typical time delay between these two modes of data acquisition. This study investigates the logistics for the concurrent deployment of the UAV-borne spectral sensors and a sampling vessel for water quality measurements and the effects on the turbidity predictions due to the time delay between these two operations. The results show that minimizing the time delay can significantly enhance the efficiency of data acquisition and consequently improve the calibration process. In particular, the outcomes highlight notable improvements in the model’s predictive accuracy for turbidity distribution derived from UAV-borne spectral images. Furthermore, a comparative analysis based on a pilot study is conducted between two multirotor UAV configurations: the DJI M600 Pro with a hyperspectral camera and the DJI M300 RTK with a multispectral camera. The performance evaluation includes the deployment complexity, image processing productivity, and sensitivity to environmental noises. The DJI M300 RTK, equipped with a multispectral camera, is found to offer higher cost-effectiveness, faster setup times, and better endurance while yielding good image quality at the same time. It is therefore a more compelling choice for widespread industry adoption. Overall, the results from this study contribute to advancement in the deployment of UAVs for marine water quality monitoring.

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

An Approach Based on Video Image Intelligent Recognition for Water Turbidity Monitoring in River

Development and evaluation of a rapid visual loop-mediated isothermal amplification assay for the tcdA gene in Clostridioides difficile detection.

The tcdA gene codes for an important toxin produced by Clostridioides difficile (C. difficile), but there is currently no simple and cost-effective method of detecting it. This article establishes and validates a rapid and visual loop-mediated isothermal amplification (LAMP) assay for the detection of the tcdA gene. Three sets of primers were designed and optimized to amplify the tcdA gene in C. difficile using a LAMP assay. To evaluate the specificity of the LAMP assay, C. difficile VPI10463 was used as a positive control, while 26 pathogenic bacterial strains lacking the tcdA gene and distilled water were utilized as negative controls. For sensitivity analysis, the LAMP assay was compared to PCR using ten-fold serial dilutions of DNA from C. difficile VPI10463, ranging from 207 ng/µl to 0.000207 pg/µl. The tcdA gene of C.difficile was detected in 164 stool specimens using both LAMP and polymerase chain reaction (PCR). Positive and negative results were distinguished using real-time monitoring of turbidity and chromogenic reaction. At a temperature of 66 °C, the target DNA was successfully amplified with a set of primers designated, and visualized within 60 min. Under the same conditions, the target DNA was not amplified with the tcdA12 primers for 26 pathogenic bacterial strains that do not carry the tcdA gene. The detection limit of LAMP was 20.700 pg/µl, which was 10 times more sensitive than that of conventional PCR. The detection rate of tcdA in 164 stool specimens using the LAMP method was 17% (28/164), significantly higher than the 10% (16/164) detection rate of the PCR method (X2 = 47, p < 0.01). LAMP method is an effective technique for the rapid and visual detection of the tcdA gene of C. difficile, and shows potential advantages over PCR in terms of speed, simplicity, and sensitivity. The tcdA-LAMP assay is particularly suitable for medical diagnostic environments with limited resources and is a promising diagnostic strategy for the screening and detection of C. difficile infection in populations at high risk.

Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment

Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

The Potential of Multibeam Sonars as 3D Turbidity and SPM Monitoring Tool in the North Sea

Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In the Belgian part of the North Sea, turbidity and suspended particulate matter (SPM) concentrations have been monitored for decades by satellite remote sensing, but this technique only focuses on the surface layer of the water column. Within the water column, turbidity and SPM concentrations are measured in stations or transects with a suite of optical and acoustic sensors. However, the dynamic nature of SPM variability in coastal areas and the recent construction of offshore windmill parks and dredging and dumping activities justifies the need to monitor natural and human-induced SPM variability in 3D instead. A possible solution lies in modern multibeam echosounders (MBES), which, in addition to seafloor bathymetry data, are also able to deliver acoustic backscatter data from the water column. This study investigates the potential of MBES as a 3D turbidity and SPM monitoring tool. For this purpose, a novel empirical approach is developed, in which 3D MBES water column and in-situ optical sensor datasets were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. Our results show that these converted mean mass concentrations at the Kwinte and Westdiep swale areas are within the limits of the reported yearly averages. Moreover, they are in the same order of magnitude as the measured mass concentrations from Niskin water samples during each campaign. While there is still need for further improvement of acquisition and processing workflows, this study presents a promising approach for converting MBES water column data to turbidity and SPM measurements. This opens possibilities for improving future monitoring tools, both in scientific and industrial sectors.

A-290 Evaluation of a Commercial Turbidity Monitoring System Followed by Mass Spectrometry to Rapid Identification of Microorganisms from Urine Samples

Abstract Background Urinary tract infections (UTIs) are one of the most common bacterial infections in worldwide and represent a worried public health problem, especially due to a significant rise in global antibiotic use in recent years. The purpose of the study was to evaluate the performance of Uro-Quick (UQ) system (Alifax) and the Vitek-MS system (bioMérieux) to identify bacteria isolates directly from urine samples. Methods We analyzed 352 clinical samples of urine. From automated screening by UQ system was used 500 µL of homogenized urine sample manually pipetted into a UQ vial containing 4 mL of broth. The inoculated vials were manually introduced into the Uro-Quick reading unit and incubated for 3 h. The signals are processed by software which monitors the growth curves and calculates the microbial count as colony forming units (CFU/mL). Sample preparation to the mass spectrometry (Vitek-MS) was performed using an in-house protocol based on a centrifugation-wash (CW) method. In parallel with the Vitek-MS direct identification, all the urine samples were submitted to standard culture (SC) method on ChromID™CPS® plate by semi-quantitative technique using 1 µL disposable loop, incubated at 35–37°C and the colony counting were read after 24 and 48h of incubation. Bacterial isolates from positive cultures were identified by Vitek®2 System, according to the manufacture’s recommendations. Cultures with the presence of three or more microorganisms were considered negative. Results Out of 334 clinical samples, 261 (78.1%) were negative for the growth by UQ system and for the presence of microorganisms by SC, 78 (21.9%) samples were positive with growth as ≥104 CFU/mL by UQ system, of which 32 samples were also positive with microorganism isolated by SC. Among the 78 microorganisms detected on the samples with growth curve by UQ system, 74 (94.8%) were identified to the species level by the mass spectrometry and 4 (5.2%) were not identified. The microorganisms were Escherichia coli (n = 40), Enterococcus faecalis (n = 12), Klebsiella pneumoniae (n = 6), Proteus mirabilis (n = 4), Citrobacter koseri (n = 4), Streptococcus agalactiae (n = 3), Morganella morganii (n = 2), Enterobacter cloacae (n = 1), Citrobacter freundii (n = 1) and Gardnerella vaginalis (n = 1). Among the 32 microorganisms detected by SC, 28 showed the same identification at species level when was compared with direct identification by Vitek-MS. Conclusion This study showed that both UQ and CW methods by Vitek-MS technology used together were able to provides a rapid result (&amp;lt;4 h) for bacterial identification directly from urine samples, that can be useful in clinical practices, especially in severe infectious cases like pyelonephritis. So, future studies should address with a larger number of samples to improve the sensibility of this test for different groups of microorganisms.

Performance characteristics of a prototype dialysate turbidity monitoring system to detect peritonitis in patients receiving peritoneal dialysis.

The risk of peritonitis has limited wider adoption of peritoneal dialysis (PD) in the United States. We developed a prototype bedside dialysate turbidity monitoring system, aiming to improve diagnostic accuracy relative to conventional approaches which depend on visual inspection and reporting of insensitive and non-specific symptoms. The prototype system was tested in a single-centre, proof-of-principle clinical study in patients receiving intermittent PD. We obtained multiple effluent dialysate samples from each consenting participant. We compared turbidity measurements with diagnostic criteria endorsed by the International Society of Peritoneal Dialysis (ISPD). Overall, we analysed 983 specimens from 65 patients, including 105 samples from patients with peritonitis and 878 samples from patients without peritonitis. An operating point derived from a previous in vitro study yielded an unadjusted sensitivity and specificity of 95.2% and 91.5%, respectively. The majority of samples that did not meet ISPD diagnostic criteria were either cases detected before criteria were met or were related to active peritonitis treatment and resolution. This proof-of-principle study demonstrates the feasibility and diagnostic accuracy of a prototype dialysate turbidity monitoring system for peritonitis surveillance.

NEW STANDARDS FOR MITIGATING DREDGING IMPACTS IN SENSITIVE HABITATS IN SAUDI ARABIA

We propose a systematic approach to monitoring and managing dredge operations based on industry best practice advice, tailored to the specific conditions in the Red Sea, including the following key elements: 1) Relocation of corals from the direct dredging footprint, 2) Development of a site and project-specific dredging environmental management plan, 3) Undertaking turbidity and coral monitoring, 4) Turbidity plume hindcast and forecast, 5) Adaptive management involving all key project stakeholders. The paper lists the above key elements and presents a case study from Sindalah Island where the system was successfully applied – the first time such an approach has been implemented in the Middle East region.

A Glass-Fiber-Optic Turbidity Sensor for Real-Time In Situ Water Quality Monitoring.

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level of drinking water distribution systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve the manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large-scale distributed fiber-optic turbidity monitoring networks.

Microbial estimation using real-time monitoring of vegetable wash water quality parameters

The real-time effectiveness of washing fresh-cut produce is hard to evaluate because it is impossible to rapidly measure the bacteria levels in the produce and wash water during a continuous washing step. This study aimed to investigate the correlation between microbial levels (total aerobic bacteria [TAB] and coliforms) and water quality parameters (turbidity, color, salinity, and temperature) during the processing of salted Chinese cabbages. Results showed that wash water and cabbage samples were contaminated with TAB and coliforms but not Escherichia coli. Turbidity, as a measure of the wash water quality, had a wide measurement range (0.48–43.48 NTU). There was a very high positive correlation between the TAB levels in all four washing tanks and turbidity (r = 0.98). The error between actual and estimated TAB levels, based on turbidity, was within 8 %. These results suggest that real-time monitoring of turbidity can be applied to the water supply control system to maintain the cleaning effect of salted Chinese cabbages.

Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine

Time series ocean color data product facilitates the study of spatio-temporal variability of various physical parameters of water, which allows the monitoring of water quality, interannual variability in phytoplankton biomass, and seasonal representation at different latitudes.Its role in marine biogeochemistry, water quality distribution, and climate fluctuation is very crucial., However, the lack of in situ measurement induces poor management and knowledge of the dynamics of Tunisian coastal waters. Therefore, this study aims to develop a workflow to monitor Tunisian waters based on long-term spatial observations of sea surface temperature, chlorophyll, and turbidity observations.Long-term sea surface observations were automatically obtained by processing the daily Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua data via the Google Earth Engine (GEE) platform from 2005 to 2020. The recorded average monthly and yearly trends are validated by point-based measurements from the Gulf of Gabes and a qualitative analysis based on the bibliographic synthesis of offshore measurement campaigns in Tunisian waters. The hottest years were 2006, 2007, and 2017 while the coldest ones (12–28 °C) were 2011, 2012, and 2016. The highest chlorophyll content (10 and 5 μg L-1) was observed in 2006, 2007, 2011, 2012, 2015, 2016, and 2019. In addition, Turbidity peaks ranging between 11 nephelometric turbidity units (NTU) and 5 NTU were identified during December and January of 2005 2008, and 2011. Moreover, seasonal cyclicity and high correlations between estimated parameters were observed. Overall, combining the Google Earth Engine tool with daily MODIS data was effective for the routine monitoring of water quality parameters that is fast, accurate, and important for Tunisian coast management.