Turbidity Estimation Research Articles

Turbidity estimation from an acoustic backscatter signal in a tropical coral reef system.

Turbidity estimation from an acoustic backscatter signal in a tropical coral reef system.

Water quality index and health risk assessment for heavy metals in groundwater of Kashiani and Kotalipara upazila, Gopalganj, Bangladesh

The groundwater quality of the Gopalganj district in Bangladesh was not well documented. Therefore, this research was done to determine the groundwater quality in the study area. The water quality index and the human health risk for heavy metal ingestion were used to describe the water quality. The water quality was performed through the estimation of turbidity, pH, electrical conductivity, total dissolved solids, and concentration of sodium, potassium, ammonium, nitrate, iron, manganese, zinc, copper, and arsenic. The mean turbidity, pH, and total dissolved solids in the two upazila were within the permissible limit. However, the electrical conductivity in both the upazila was higher than the WHO-prescribed value in which the higher concentration was in Kotalipara. Excess concentrations of iron and arsenic were found in the Kashiani upazila, but these were below the detection limit in Kotalipara upazila. The water quality index revealed that roughly 61.0% of samples of Kashiani upazila were of poor quality. However, about 96.0% of samples of Kotalipara upazila were of excellent quality. Chronic health risks due to the revelation of drinking have also been determined by assessing the hazard quotient and hazard index. In Kashiani, almost 85.0% of samples were elevated chronic risks for adults and 100.0% of the samples were very high chronic risks for children. In Kotalipara, all the samples (almost 100%) were suggested to have a lower chronic risk for adults and children. The results suggested that the carcinogenic risk of arsenic via oral exposure was very high for both adults and children in Kashiani upazila.

Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach

This study represents the first application of Sentinel-2 remote sensing imagery and model fusion techniques to assess the chlorophyll-a (Chla) concentration and turbidity in Nansi Lake, Shandong Province, China, from 2016 to 2022. First, we innovatively employed the stacking method to fuse eight fundamentally different Machine Learning (ML) models, each utilising 20 and 17 feature bands, resulting in the development of a robust algorithm for estimating the Chla concentration and turbidity in Nansi Lake. The results demonstrate that the Stacking Model has achieved outstanding theoretical generalisation capability. Second, the sensitivity of the model to extreme value data in the sample was quantified, and we found that compared with extreme gradient boosting (XGBoost), the optimal performance of the Stacking Model improved by 12%, to some extent, it solved the problem of high-value underestimation and low-value overestimation. The SHapley Additive exPlanations (SHAP) results revealed that features such as Three Bands, Enhanced Three, Rrs492/Rrs560, Rrs705/Rrs665 play a crucial role in estimating Chla concentration. For the turbidity estimation, the Normalized Difference Turbidity Index (NDTI), Rrs705+Rrs560, Rrs865-Rrs740 made significant contributions. Finally, we utilised the Stacking Model to create spatiotemporal maps of the Chla concentration and turbidity in Nansi Lake from 2016 to 2022. We analysed the causes of the water quality changes and explored the driving factors. Compared with previous studies, this paper provides a new idea for the monitoring of lake water quality parameters by using the high resolution of Sentinel-2 image and the high precision of model fusion technology, these results can provide a reference for similar water area research and decision-making support for environment-related departments.

MAPPING AND ASSESSMENT OF THE SPATIAL AND TEMPORAL DISTRIBUTION OF TURBIDITY IN LAKE BUHI FROM SENTINEL-2 IMAGES USING GEOGRAPHICALLY WEIGHTED REGRESSION AND NORMALIZED DIFFERENCE TURBIDITY INDEX

Abstract. Water quality monitoring is vital in ensuring the suitability of Lake Buhi for aquaculture and recreational activities. However, with the limitations of traditional monitoring methods, the Bureau of Fisheries and Aquatic Resources (BFAR) cannot comprehensively characterize the spatial and temporal water quality patterns in the lake. With the development of remote sensing methods, this study aimed to describe the spatial and temporal variability of turbidity in Lake Buhi in 2020 using Geographically Weighted Regression (GWR) and the Normalized Difference Turbidity Index (NDTI) applied to Sentinel-2 images. GWR provided accurate estimates of turbidity, with the results achieving an R2 value of 0.98 for the February image and 0.93 for October. GWR-derived turbidity was 2 used as an alternative to in situ data and then regressed with NDTI values to acquire turbidity models for dry and wet seasons. Upon validation, the best turbidity model for the dry (R2 = 0.59, RMSE = 0.60, MAE = 0.15) and wet (R2 = 0.49, RMSE = 1.22, MAE = 0.25) seasons produced acceptable results, hence, used to assess the spatial and temporal variability of turbidity in the lake throughout 2020. The analysis revealed that Lake Buhi is more turbid during the dry season than the wet season. Turbidity during the dry season is governed by its natural water flow, while it is heavily influenced by precipitation during the wet season.

ASSESSMENT AND SPATIAL DISTRIBUTION OF WATER QUALITY CONSTITUENTS IN LAKE ECOSYSTEM USING SENITNEL-2 DATA AND MODELLING

An inland lake is an essential component of the freshwater environment and must be monitored to prevent the quality of the lake's water from deteriorating. Turbidity and total suspended matter (TSM) are crucial water quality assessment factors. A cutting-edge technical approach called remote sensing is utilized to keep an eye on the quality of lake water. To assess the lake water quality, the water quality constituents (WQC) model was developed using an empirical computation technique using spectral wavelengths (400-800 nm) from in-situ hyperspectral remote sensing measurements. The results show that the best appropriate models that are sensitive to the estimation of TSM (R2 = 0.8523) and Turbidity (R2 = 0.9115) were selected and incorporated into Sentinel-2MSI for WQC estimation. The spatial distribution of WQC is mapped, providing information on both the distribution and variations of WQC within the lake. The research has proven that it has the potential for regular monitoring of lake water through this technique.

Measurement of Turbidity and Total Suspended Matter in the Albufera of Valencia Lagoon (Spain) Using Sentinel-2 Images

In the context of freshwater ecosystems, turbidity and suspended solids play crucial roles, with their levels significantly influenced by anthropogenic activities. This study focuses on assessing and monitoring these parameters in Albufera de Valencia using Sentinel-2 imagery. The primary aim is to establish reliable estimation algorithms that can contribute to effective ecosystem management. This study calibrated and validated algorithms for estimating turbidity and suspended solids. The R783 × R705/R490 model proved to have the best performance for estimating turbidity and total solids in Albufera. However, R783/R490 obtained a higher coefficient of determination for the organic part, while the R705 band was selected for the inorganic part. However, to achieve better estimates of turbidity and inorganic matter, more research is needed in the future. The implications of excessive suspended solids are underscored, including the depletion of dissolved oxygen and reduced primary productivity due to limited light penetration and habitat availability. Collaboration between disciplines such as limnology, optics, and water chemistry is crucial to advance water quality estimation models in lakes and lagoons such as Albufera. By integrating expertise and approaches from these diverse fields, new knowledge can be gained, and the basis for more effective management and conservation strategies can be laid.

Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery

ABSTRACT Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale.

Monitoring the spatial distribution of water quality of an urban stormwater pond using satellite images

Stormwater ponds play a critical role in managing urban runoff and mitigating the impact of pollutants on receiving water bodies. This study investigates the potential of remote sensing (RS) techniques in retrieving water level and key water quality parameters in urban stormwater wet ponds. Specifically, the focus is on water level, chlorophyll-a (Chl-a), total suspended solids (TSS), turbidity, and Secchi depth (SD). The results indicate that RS techniques can provide reliable estimates of water level, Chl-a, turbidity, SD, and TSS in urban stormwater wet ponds. Results revealed that Chl-a concentrations are higher near the inlets and within the forebay, with a significant decrease downstream of the forebay. TSS and total dissolved solids (TDS) concentrations follow a similar pattern to Chl-a, being higher near the inlet and within the forebay, but significantly lower downstream. Notably, the highest concentrations of TSS, TDS, and Chl-a are observed in July, likely due to heavy rainfall events during that period. Through principal component analysis (PCA) and correlation analysis, the study effectively captured the interrelationships among the water quality parameters in the stormwater pond. The spatial distribution of TSS and Chl-a shows a correlation. Furthermore, the study uncovers a relationship between water temperature and Chl-a concentration, with Chl-a decreasing as the temperature exceeds 20 °C and increasing when it drops below 20 °C. This relationship is influenced by sunlight availability, which affects Chl-a growth, as evidenced by variations in Chl-a concentration between the water column's bottom layers and surface. Overall, this research highlights the potential of utilizing RS techniques to retrieve water quality parameters, providing valuable information for water managers and decision-makers. The ability to generate maps of these parameters using reflected light measurements above stormwater wet ponds also contributes to effective water quality management strategies.

Determination of broadband atmospheric turbidity from global irradiance or photovoltaic power data using deep neural nets

Photovoltaic technologies provide significant capacity to electric grids, however, resource variability and production uncertainty complicate power balancing and reserve management. A crucial step in predicting solar generation is determining clear-sky irradiance. Clear-sky attenuation can be modeled using broadband atmospheric turbidity factors, but model accuracy is dependent on the measurements used to determine the current and future state of aerosol loading and water vapor content, which requires close proximity measurements, in time and space, to account for turbidity variability. Such measurements, though, are only available in near real-time at a limited, and decreasing, number of sites. This paper proposes a new method for estimating time-varying local turbidity conditions from more readily available pyranometer or PV output data. The method employs a long short-term memory recurrent neural network to distill the turbidity-driven signal from global irradiance (or global irradiance driven) observations, despite an inherent dampening issue. The method is developed to operate in near real-time for solar forecasting applications. Validation examines the ability of the method to (1) reproduce turbidity estimates derived from historical measurements of beam irradiance under clear-sky conditions; and (2) provide input for clear-sky models in the form of persistence forecasts generated from daily mean values.

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semi-empirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events.

A transferable turbidity estimation method for estimating clear-sky solar irradiance

A transferable turbidity estimation method is proposed for estimating the turbidity and clear-sky solar irradiance. Instead of using on-site irradiance measurements (i.e., the local model), a transferable model is developed involving stations with sufficient information, and then applied at locations with limited data availability. Compared with the local method, the transferable model yields results with slightly higher discrepancies regrading normalized root mean squared error (nRMSE, 2.80% vs 2.75%). When compared with the Ineichen–Perez (PVLIB) model, the nRMSE of clear-sky global horizontal irradiance (GHIcs) estimation is reduced from 4.99% to 2.44%, and the normalized mean bias error (nMBE) is improved from -3.37% to 0.57%. The GHIcs estimation is comparable with physical models (i.e., McClear and REST2), where the McClear produces a nRMSE of 3.32% and the nMBE is 2.10%, while the REST2 generates results with an nRMSE of 2.55% and an nMBE of 1.30%. We further compare aforementioned models for day-ahead GHIcs forecasts using a day persistent way. GHIcs forecast from the transferable method has slightly lower discrepancies of nRMSE and nMBE than the physical models. Considering the complexity of physical models, the transferable turbidity estimation method with comparable performance demonstrates valuable potential for solar resourcing and forecasting applications.

Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery.

Sudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor's near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution.

Geographically aware estimates of remotely sensed water properties for Chesapeake Bay

Remotely sensed water properties are important for a variety of applications, including validation of Earth systems models (ESMs), habitat suitability models, and sea level rise projections. For the validation of next-generation, high or multi-resolution (30 to 60 km) ESMs in particular, the usefulness of operational forecasting products and directly observing satellite-based sensors for validation is limited due to their temporal availability and spatial resolution of <1 year (in some cases) and >30 km2, respectively. To address this validation data gap, we developed a data-driven model to produce high-resolution (<1 km2) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation ESMs. Our model fits daily moderate resolution imaging spectroradiometer (MODIS) Aqua reflectance data to surface observations (<1 m depth) from 2000–2021 in the Chesapeake Bay. The resulting models have similar error statistics as prior efforts of this type for salinity [root mean square error (RMSE): 2.3] and temperature (RMSE: 1.8 C). However, unlike prior efforts, our model is designed as a pipeline meaning that it has the advantage of producing predictions of water properties in future time periods as additional MODIS data becomes available. We also include novel geographically aware predictive features insofar as they capture geographic variation in the influence of flow and surface water exchange in upstream coastal watersheds.

Laboratory Experiments to Assess the Effect of Chlorella on Turbidity Estimation

Turbidity is an important parameter in monitoring water quality, and thus attracts significant attention. Changes in the various components and constituent elements of water directly affect turbidity measurements. The turbidity of water is generally quantified by measuring the absorbance or scattering characteristics of substances suspended in it. The complex composition of environmental water bodies complicates the determination of factors influencing their turbidity. Controlled experiments that quantitatively analyze the effect of a single factor on the measurement of turbidity in the laboratory are an important means to improve the accuracy of turbidity assessment. Properties of suspended materials in a water column that may affect its measured turbidity include the concentration of algae, particle size, and the color of soluble substances, etc. The laboratory experiments reported here used Chlorella as an example to investigate the effect of algal concentration on turbidity measurement. The results are as follows. When the turbidity is low (100 NTU), the average relative error between the theoretical and practical absorbance is about 37.52%, which decreases to 19.20% at 100–200 NTU and 5.16% at 200–400 NTU. The characteristic spectral bands sensitive to turbidity (680 nm) and Chlorella (240 nm) were selected, and the theoretical and practical turbidity results were analyzed. The average relative errors of mixed liquids of less than 100, 100–200, and 200–400 NTU are 65.07%, 34.18%, and 3.95%, respectively. Therefore, the concentration of Chlorella significantly affects the measured turbidity, and results in a more complex effect at low turbidity (&lt;100 NTU). Combining the analysis of absorbance peak values and characteristic spectral bands, we can assess the turbidity changes in different components, and through calibration, information regarding the concentration and variation of different components in water bodies can be obtained. The results of this research could improve the accuracy of on-site measurement of the concentrations of different components suspended in water, and also facilitate the development of new turbidity sensors.

An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery

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.

An image-based deep learning model for water turbidity estimation in laboratory conditions

An image-based deep learning model for water turbidity estimation in laboratory conditions

MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88–0.93, mean absolute error of 0.17–0.19, and normal root mean square error of 0.05–0.09. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

Turbidity dynamics of large lakes and reservoirs in northeastern China in response to natural factors and human activities

The water environment has experienced prominent changes worldwide in recent decades, especially in inland waters. As an important lake region in China, the water turbidity of lakes/reservoirs in the Northeast Plain-Mountainous Region (NPLR) remains less understood, especially the influencing mechanisms of regional climatic conditions and human activities. To enhance the knowledge of turbidity dynamics and explanatory variables of NPLR water bodies, long-term turbidity estimates of lakes/reservoirs were derived from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua observations between 2003 and 2019. The results show that most lakes/reservoirs became less turbid in NPLR during the study period, as indicated by a decreasing turbidity trend of 0.05–2.4 nephelometric turbidity units (NTU)/year. Overall, the decreased turbidity was mainly attributed to the decreased wind speed (from 2003 to 2012, r = 0.85, p < 0.01), but also to the increased normalized difference vegetation index (NDVI) of the forest region (r = −0.80, p < 0.01), with the latter being a result of the forest protection efforts of the Chinese government. The increased snowmelt depth also contributed to a substantial turbidity decline in April. In addition, distinct seasonal patterns of turbidity were observed in the NPLR, with less turbid water in the wet season (i.e., from June to August) and turbid water in the dry season (i.e., October, November, and April in the next year). This result is attributed to the rapid seasonal dynamics of precipitation and runoff. This study provided a comprehensive analysis on the influencing mechanisms of turbidity dynamics in NPLR lakes; in particular, for the first time, revealed the impacts of the national policy-mandated forest development and the snowmelt depth on lake turbidity. These findings will provide vital information for government decision-making in water environment protection in NPLR and other similar areas with densely distributed lakes.

Mar Menor lagoon (SE Spain) chlorophyll-a and turbidity estimation with Sentinel-2

Mar Menor is a Mediterranean Coastal lagoon with high environmental and social value, but has suffered important episodes of contamination in recent years due to heavy rains, sediment dragging and polluting substances mainly from agriculture as well as the entry of mining waste, causing an increase in eutrophication. Water quality variables such as chlorophyll-a concentration [Chl-a] and turbidity, can be studied through its optical properties by remote sensing techniques. In this work, a methodology is proposed for monitoring [Chl-a] and the turbidity of the Mar Menor using Sentinel-2 images. For this purpose, an extensive database of both variables was used consisting of data taken on different dates between 2016 and 2019 at 12 points of Mar Menor. The images were atmospherically corrected using Case 2 Regional Coast Color Processor (C2RCC) version for turbid waters (C2X) to obtain the water surface reflectance. Then several arithmetic relations between database and reflectance bands used in the bibliography for [Chl-a] and turbidity were analyzed. Comparing the results of each one of these relations with the in situ data, decided that the best index for [Chl-a] estimation is the relation (R560 + R705)/ (R560 + R665) with an RMSE = 2.6 mg/m3 and a NRMSE = 9.1 % and for turbidity R705*R705/R490 with an RMSE = 1.5 NTU and a NRMSE= 10.9 %. Finally, by applying these relationships on different dates, thematic maps of [Chl-a] and turbidity of Mar Menor were obtained. One of these images was some days after September 2019 torrential rains, in which a considerable [Chl-a] and turbidity increase was observed

HOW EFFICIENT CAN SENTINEL-2 DATA HELP SPATIAL MAPPING OF MUCILAGE EVENT IN THE MARMARA SEA?

Abstract. With the repetition of mucilage event, which is triggered by many different anthropogenic, climatic and microbiological factors, in the Marmara Sea in 2021, the importance of water quality in the seas has come to the fore again. To present the spatial distribution of the mucilage, a feasibility study has been carried out with point-based water quality measurements and remote sensing data. In-situ measurements are collected routinely within the scope of the Integrated Marine Pollution Monitoring Program (DEN-IZ) which was conducted in cooperation with the Ministry of Environment, Urbanisation and Climate Change and the Scientific and Technological Research Council of Turkey - Marmara Research Center (TUBITAK-MAM). In this preliminary study, 16 in-situ measurements, 5 of which were taken from water containing mucilage, on 29 April 2021 in the Gulf of Gemlik were used. Then, univariate regression analyzes were performed in two different scenarios (i.e. 5 mucilage points and all in-situ points) with Sentinel-2 satellite imagery and in-situ water quality measurements for 2 different parameters (i.e. chlorophyll-a – Chl-a) and turbidity). According to R2 and accuracy assessment measures (f- and t- statistics etc.), the most suitable models were determined for two scenarios and two parameters. Finally, the performances of the selected models were tested with 2 different in-situ measurements and satellite images (dated 22 and 27 April) taken from dates close to the data set used; and it was concluded that the models created with 16 points were successful for both Chl-a and turbidity estimation for this preliminary study.