Normalized Difference Turbidity Index Research Articles

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

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.

Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China

Bathymetric mapping is integral to maintaining marine ecosystems, managing coastal zones, and safeguarding the environment. However, achieving accurate large-scale bathymetric maps remains a challenge in China, particularly in nearshore turbid waters. To address this gap, we leveraged seasonal Gaofen-6 (GF-6) data to conduct bathymetry mapping in the Yellow River Delta-Laizhou Bay area. In our study, we found that longer wavelengths, such as those in the red-edge2 and near-infrared (NIR) bands, exhibited superior performance in determining bathymetry. Moreover, specific band ratios derived from GF-6 data—such as Blue/NIR (BN), Violet/NIR (VN), Blue/Red-edge2 (BE), Violet/Red-edge2 (VE), Green/NIR (GN), and Green/Red-edge2 (GE)—showed promising outcomes, particularly in turbid nearshore waters. When comparing models, the random forest regression (RFR) model outperformed the classification and regression trees (CART) model in turbid nearshore areas, showing higher R2 values and lower RMSE. Notably, both models demonstrated higher accuracy in March compared to May and October. Incorporating the Normalized Difference Turbidity Index (NDTI) notably improved bathymetric results, especially in turbid sea regions. Furthermore, nearshore bathymetry proved highly susceptible to natural processes, seasonal variations, and human activities. The significant discrepancies in bathymetry among coastal areas emphasize the need for tailored management strategies to enhance coastal management and foster sustainable marine economic development.

DEVELOPMENT OF GOOGLE EARTH ENGINE APPLICATION FOR SPATIOTEMPORAL ANALYSIS OF TURBIDITY IN BATAN ESTUARY, AKLAN THROUGH THE HARMONIZATION OF LANDSAT AND SENTINEL-2 IMAGERY

Abstract. Water turbidity poses a threat to marine life and the economy. To address this, regular monitoring is crucial. Field methods are commonly employed, but they can be costly and challenging to perform consistently and historically. Luckily, remote sensing satellites offer a viable solution by collecting image data from space for Earth observation. Researchers have utilized these satellite images to develop indices for analyzing water turbidity and chlorophyll-a content. Leveraging the Google Earth Engine geospatial data computing platform, an application has been created to facilitate water quality monitoring. The app harmonizes Landsat and Sentinel-2 Imagery to improve temporal resolution and increase historical data availability. These images are then subjected to indices, namely the Normalized Difference Turbidity Index (NDTI) and the Normalized Difference Chlorophyll Index (NDCI), to estimate water turbidity and chlorophyll-a content, respectively. To evaluate the effectiveness of the app, the Batan Estuary in Aklan was selected as a test site. Annual mean water turbidity data from 2000 to 2021 were processed and downloaded through the app and subsequently imported into ArcGIS Pro for further analysis. Raster statistics revealed that turbidity and chlorophyll-a content have consistently decreased since 2000, with already low levels observed. Although the river section exhibited the highest turbidity relative to the estuary, it can still be deemed nonpolluted based on US EPA standards. Moreover, ArcGIS's emerging hotspot analysis indicated that the estuary predominantly featured cold spots, indicating minimal areas with clusters of turbid water over time.

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.

Causes and effects of Shisper glacial lake outburst flood event in Karakoram in 2022

Karakoram Himalayas are vulnerable to glacial lake outburst floods (GLOFs), which cause catastrophic floods in the surrounding areas. The increasing natural and anthropogenic activities, especially in the Indo-Gangetic Plains at the southern flank of the towering Himalayas, could be the cause of climate change affecting the frequency of the natural hazards in the Himalayas. In the present study, a detailed analysis of the Shisper Lake breach of 7 May 2022 is carried out using satellite remote sensing. A decreasing trend in the glacial mass balance is observed between 2017 and 2021; in this period, frequent GLOF episodes occurred. A pronounced decline in the snow cover and glacial debris (before the cascade) and an abnormal increase in land surface temperature (LST) retrieved from satellite data could be responsible for the enhanced snow/glacial melting prior to the breach. Alteration in glacial mobility (cm/12 days) is observed at the locations where the glacier melts due to an increase in LST with increased debris cover. In the paper, we have studied changes in water quality and land cover along the stream after the GLOF. An increment in normalized difference turbidity index (NDTI) is observed before the flood showing turbulence and sediment load. Increased glacial melt contribution into the stream results in increased flow rate and decreased water quality as observed through normalized difference water index (NDWI) and normalized difference chlorophyll index (NDCI), near the Karakoram Highway Bridge. Sentinel-1 backscatter coefficient indicates increased discharge from the melting of glaciers. A significant reduction in co-flood coherence is observed, attributed to the bridge collapse and severe bank erosion.

Monitoring water spread and aquatic vegetation using earth observational data for Nani-High Altitude Lake (N-HAL) of Uttarakhand State, India

The lakes are facing anthropogenic pressure and losing its natural characteristics. Hence, the evaluation of lake water quality is a thrust area of research. The Nani-High Altitude Lake (N-HAL) was aimed to study during pre- and post-monsoon (March and October) period (2017–2022) using earth observation data based on false color composite (FCC) and spectral indices. The Sentinel-2 A/B data of 10 m spatial resolution was considered for monitoring and mapping of high-altitude lake. The FCC images were used to map and monitor changes in water spread and aquatic vegetation. Further, three spectral indices, namely, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Turbidity Index (NDTI), were applied to delineate water bodies and aquatic macrophytic vegetation (recorded are the Potamogeton pectinatus, Potamogeton crispus, Polygonum glabrum, Polygonum amphibium and Polygonum hydropiper (Water pepper). Results reveal that the water surface extent has changed from 2017 to 2022, depending on the pre- and post-monsoon rainfall pattern during the study period. During a flood year on 18-20, October 2021, in the area resulting into a drastic reduction in the aquatic vegetation and suspended particles. This lake needs immediate conservation and management policies.

Spatially Intensive Patterns of Water Clarity in Reservoirs Determined Rapidly With Sensor‐Equipped Boats and Satellites

AbstractOptical water quality affects the quantity and spectral composition of underwater light available to photoautotrophs along with light reflected off the water surface. In reservoirs, prominent gradients for optical water quality and clarity measures occur between inlets and outlets, allowing rapid surveys of ecosystem structure across diverse conditions along with tests of relationships between in situ and remotely sensed water indices. We used a sensor‐equipped boat in a clay‐rich subtropical drinking water reservoir to examine continuous spatial patterns of water clarity and the relationship between in‐lake turbidity and normalized difference turbidity index (ndti) from Sentinel 2 imagery acquired on the same date. Over the 62.3 km boat path, surface turbidity varied between 14 and 85 NTU (mean = 30), with highest values in shallower water near the main inlet. Results indicated a strong linear relationship between sensor turbidity and lab‐determined turbidity. A positive relationship between the satellite turbidity index and in‐lake turbidity, combined with a negative exponential relationship between Secchi depth and turbidity, provided a basis for predicting water clarity metrics continuously over the entire lake surface. In this turbid water body, predicted Secchi depth and turbidity over the whole lake had means of 0.22 m and 54 NTU, with 75% of the lake area <0.29 m and >26 NTU. Pursuit of general relationships involving optical properties of water from high‐speed, data‐intensive spatial surveys and remotely sensed surface reflectance facilitates further development of spatially explicit models of aquatic systems.

Retrieval of suspended sediment concentration (SSC) in the Arabian Gulf water of arid region by Sentinel-2 data

Suspended sediment concentration (SSC) in water increases temperature and turbidity, limits the photosynthesis of aquatic plants, and reduces biologically available oxygen. It is important to study SSC in the coastal waters of the Arabian Gulf. Thus, this study mapped the SSC of coastal water between Al Arish and Al Ghariyah in northern Qatar using the spectral bands of the MultiSpectral Imager (MSI) of Sentinel-2 by calculating the Normalized Difference Suspended Sediment Index and Normalized Suspended Material Index. The results are studied using the Normalized Difference Turbidity Index and Modified Normalized Difference Water Index. The mapping of SSC in the water using NDSSI showed the presence of a high concentration of suspended sediments between Al Arish and Al Mafjar and a low concentration between Al Mafjar and Al Ghariyah. The mapping of NSMI showed values between 0.012 (clear water) and 0.430 (more suspended material) for the occurrence of suspended materials and supported the results of NDSSI. The study of turbidity using an NDTI image showed turbidity index values ranging from −0.44 (clear water) to 0.12 (high turbidity) and confirmed the occurrence and distribution of suspended sediments and materials in the water. The MNDWI image was able to discriminate clear water with bright pixels from silty sand and mud flats. The relationships between NDSSI, NSMI, and NDTI were correlated with in-situ measurements and studied to find suitable indices to map SSC. Regression analyses showed the strongest relationship between NSMI and NDTI (R2 = 0.95) next to NDSSI and NDTI, where NDTI had the strongest effect on NDSSI (R2 = 0.86). The satellite data results were evaluated by studying the physical parameters and spatial distribution of suspended sediments in the surface and bottom waters. In addition, the grain size distributions, mineral identification, and chemical element concentrations in the bottom sediment samples were studied.

Monitoring the Water Quality of the Tigris River and Producing Maps to Determine the Locations of Turbidity in the Water for the Year 2022 in Baghdad Governorate

Abstract Interest has recently increased in the sustainable development of the natural resources of the land cover, including the water resources stored in natural catchments such as lakes and marshes and flowing from the sources to the rivers, and in increasing human activities from cultivating and fertilizing the land, to establishing factories and throwing industrial waste into rivers to throwing bore water in addition to Clay silt as a result of weathering and erosion of the soil. The purpose of the study is to monitor and determine the quality of the Tigris River water from turbidity, industrial waste and the rest of the materials that pollute the Tigris River. The section of the Tigris River passing through the city of Baghdad was selected as a sample for the study, and the data were used. Remote sensing techniques in the investigation of the polluted area in the Tigris River in Baghdad. The Landsat 8 (OLI) image of Baghdad was used as the best sensor to measure the spectral reflectivity of land features, including water, with a spatial resolution of 30 m on October 18, 2020. The Arc GIS v10.8 application software package was used to process the image data and measure the normalized turbidity index using the green band (3) and the red band (4). Turbidity was calculated using Normalized Difference Turbidity Index (NDTI), and then classified based on the high and low values of (NDTI). The results indicated that three categories of water quality succeeded in assessing and mapping the turbidity of the water that pollutes the Tigris River. The study is worthwhile. I can be deal with the rest of the pollutants using remote sensing data as the best technology and results at the fastest costs.

Identification of time-varying wetlands neglected in Pakistan through remote sensing techniques.

Aside from Ramsar Convention awareness programs, the concept of wetlands is mostly ignored in developing countries. Wetland ecosystems are essential to hydrological cycles, ecosystem diversity, climatic change, and economic activity. Under the Ramsar Convention, there are 2414 wetlands that are internationally recognized, and Pakistan is home to 19 of them. The major goal of this study is to use the satellite image technology to locate Pakistan's underutilized wetlands (Borith, Phander, Upper Kachura, Satpara, and Rama Lakes). The other goals are to understand how these wetlands are affected by climate change, ecosystem change, and water quality. We used analytical techniques including supervised classification and Tasseled Cap Wetness to identify the wetlands. To find changes caused by climate change, Quick Bird high-resolution images was used to create the change detection index. Tasseled Cap Greenness and the Normalized Difference Turbidity Index were also used to assess the water quality and changes in the ecology in these wetlands. Sentinel-2 was used to analyze data from 2010 and 2020. ASTER DEM was also used to do a watershed analysis. The land surface temperature (°C) of a few selected wetlands was calculated using Modis data. Rainfall (mm) data was taken from PERSIANN (precipitation estimation from remotely sensed information using artificial neural networks) databases. Results indicated that in 2010, the water content of Borith, Phander, Upper Kachura, Satpara, and Rama Lakes was 22.83%, 20.82%, 22.26%, 24.40%, and 22.91%. While in 2020, these lakes' water ratios are 21.33%, 20.65%, 21.76%, 23.85%, and 22.59%, respectively. Therefore, the competent authorities must take precautions to ensure that these wetlands are preserved in the future in order to improve the dynamics of the ecosystem.

Remote sensing techniques for monitoring algal blooms in the area between Jeddah and Rabigh on the Red Sea Coast

The Red Sea is known to host and facilitate the growth and reproduction of several species of phytoplankton and algae. The degree of density and distribution of these organisms differ with region. However, very few studies have been conducted on algal blooms in this region. Hence, this study aimed to identify the effectiveness of remote sensing techniques in monitoring and controlling algal blooms in the area extending between the cities of Jeddah and Rabigh on the Red Sea coast. A series of satellite images were used to study algae from January 2020 to March 2021. All study data were subjected to processing and analysis using spectral indices [chlorophyll a (Chl-a); Concentration and Sea Surface Temperature (SST); Normalized Difference Turbidity Index (NDTI); Surface Scum Index (SSI); Surface Algal Bloom Index (SABI); Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)], and the classification, overlay, and buffer methods. We found that the rate of chlorophyll a (Chl-a) increased in February 2021 and decreased in May 2020, with the algal bloom dwindling with increasing distance from the shores of the Red Sea. Furthermore, despite the varying effectiveness of the spectral indices in estimating the distribution and density of algae in the Red Sea, Landsat SSI was the most effective. One of the primary results of the study was that algal blooms more frequently occur in the winter (February). The study produced a digital map of algal blooms in the study area. Algal blooms exhibited medium inverse correlations with temperature and specific humidity, and strong direct correlation with wind speed. Valley estuaries, close to the beaches, were found to be the most suitable area for the concentration and growth of algae. The study's findings emphasize the need for close monitoring and controlling changes in the components of the marine environment using remote sensing techniques, in order to track the changes that marine plants (algae) are exposed to in the Red Sea.

Lake Water Quality and Dynamics Assessment during 1990–2020 (A Case Study: Chao Lake, China)

Settlements along the coastlines of oceans and lakes, which are among the world’s most densely populated areas, are in immediate danger due to stressors brought on by climate change and dangers posed by human activities. This study investigates the water changes of Chao Lake during the last 30 years by using Landsat 5, 7 and 8 time-series images and water indices, including Normalized Difference Water Index (NDWI), Normalized Difference Turbidity Index (NDTI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Sea Surface Temperature (SST). The gathered data demonstrates that each estimated indicator’s value has increased with time. Thus, over the course of the 30-year research period, the NDWI, NDTI, GNDVI and SST annual average values show increases of 112.10%, 242.42%, 112.82% and 119.42%, respectively. The NDWI index underwent these fluctuations, evidenced with the biggest amount (681.8%) in the winter and the lowest amount (28.13%) in the fall. The most NDTI changes (480%) and the least (only 50%) occurred in summer and fall, respectively. The largest increases in GNDVI (180%) and SST values (537.86%) were observed in winter; the smallest changes in GNDVI (43.48%) and GNDVI (68.76%) in fall. The outcomes also demonstrated a strong link between all four estimated factors. In the majority of the analyzed months, the correlation between the 2 measures, GNDVI and NDTI, was considerably greater and near to 1. The findings of this study may be utilized by managers, decision-makers and local planners for the purpose of environmental planning and reducing water pollution in Chao Lake (and other water regions), as well as reducing the risk of environmental hazards due to water pollution.

Index-Based Spatiotemporal Assesment Of Water Quality In Tarbela Reservoir, Pakistan (1990−2020)

Anthropogenic activities can greatly influence the lake ecosystems across the globe. Within these ecosystems, the impacts of human activities are most evident on sedimentation, light and nutrient availability, and disturbance frequency. There have been times of natural environmental healing of reservoirs and the present research aims to explore the variations in the water quality of Tarbela reservoir, Pakistan the largest rock-filled dam of the world, from 1990 to 2020. Landsat imagery (Landsat 4-5, 5, 7 and 8) was used to monitor Land Use Land Cover (LULC), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Turbidity Index (NDTI) and Normalized Difference Water Index (NDWI) in Tarbela reservoir, and its surrounding area from 1990−2020, on decadal interval. The results indicated a significant increase in built-up area, of about 630 km2 , in the western and eastern parts of the reservoir, whereas turbidity level, revealed a substantial decline with 4% decrease observed in the last decade, 2010-2020 thus confirming improved water quality. The study also presented expanse in the spatial coverage of chlorophyll index and water index, indicating increase in residence time of the water. It is concluded that the water quality continued to deteriorate with time, however, 2020 was a year of environmental healing and there was an overall water quality improvement of the reservoir observed. The study recommends policies to be formulated for sediment flushing and turbidity reduction for longer time duration to enhance the life of this mega reservoir.

Методика оценки однородности створов мониторинга рек по оптической характеристике водной поверхности — индексу мутности

Preservation and improvement of aquatic recourses ecological state is an important state goal. The goal demands monitoring of river discharge and hydrochemical parameters of the river. Only the data allow evaluating of ecologically reasonable anthropogenic impact on an aquatic ecosystem. It is evident that homogeneity of river water at a monitoring river station is the requirement of the monitoring data representativity. Of course, regular control of the homogeneity by each of the identifiable hydrochemical parameters is extremely time-consuming task. The paper presents methodology for check of homogeneity of a monitoring river station by means of Earth remote sensing data analysis. The methodology includes calculation of the Normalized Difference Turbidity Index (NDTI) on the base of Sentinel-2 satellite images in geographical information systems ArcGIS and QGIS within buffer zone of a river section, calculation of distances between cell centers of the NDTI matrix and the initial vertex of the river section (the riverside), the results export in Microsoft Excel files, statistical analysis of the NDTI values homogeneity along the river section. The homogeneity analysis is made by means of the Student and Fisher tests when the NDTI distributions belong the normal law of distribution and by the tests of Wilcoxon-Mann-Whitney and Siegel-Tukey otherwise. The data processing is performed by means a number of workflow models and Python-language programs, a Lazarus-language program, and a number of Mathcad-language programs. The tools are elaborated by Victor Tretyakov. An example of the methodology application is provided in the paper. There is considered evaluation of NDTI homogeneity at the Neva River section located downstream from the Slavyanka River outlet into the Neva River. Of course, the NDTI homogeneity cannot ensure homogeneity of the river section for all the hydrochemical parameters. However, absence of the NDTI homogeneity may serve as a mark of the stream flow non-homogeneity at the river section.

Turbidity Changes in the Tigris River Using Satellite Data During the COVID-19 Related Lockdown

In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity during lockdown associated with COVID-19. Iraq announced the longest-ever lockdown on 25 March 2020 due to COVID-19 pandemic. In the absence of ground observations, remote sensing data was adopted, especially during this period. The change in the visible region's spectral reflectance of water in part of the river has been analyzed using the Landsat 8 OLI multispectral remote sensing data at Tigris River, Salah al-Din province (Bayji / near the refinery), Iraq. It was found that the green and red bands are most sensitive and can be used to estimate turbidity. Furthermore, the temporal variation in turbidity was also analyzed through a normalized difference turbidity index NDTI at each location. The NDTI decreased by 7.14% in April 2020 (lockdown period) as compared to November 2020 (6 months after lockdown), when it was observed that the turbidity in the river (near Bayji oil refinery) had drastically decreased. The study confirmed that the remote sensing approach can be used to make qualitative estimates on turbidity, even in the absence of field observations. It was concluded that the water quality of Tigris River has improved and has shown signs of restoration.

Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring 2021

Global warming together with environmental pollution threatens marine habitats and causes an increasing number of environmental disasters. Periodic monitoring of coastal water quality is of critical importance for the effective management of water resources and the sustainability of marine ecosystems. The use of remote sensing technologies provides significant benefits for detecting, monitoring, and analyzing rapidly occurring and displaced natural phenomena, including mucilage events. In this study, five water indices estimated from cloud-free and partly cloudy Sentinel-2 images acquired from May to July 2021 were employed to effectively map mucilage aggregates on the sea surface in the Izmit Bay using the cloud-based Google Earth Engine (GEE) platform. Results showed that mucilage aggregates started with the coverage of about 6 km² sea surface on 14 May, reached the highest level on 24 May and diminished at the end of July. Among the applied indices, the Adjusted Floating Algae Index (AFAI) was superior for producing the mucilage maps even for the partly cloudy image, followed by Normalized Difference Turbidity Index (NDTI) and Mucilage Index (MI). To be more specific, indices using green channel were found to be inferior for extracting mucilage information from the satellite images.

Spatio-temporal variability of Turbidity over Ukai Reservoir, by using RS2/R2A LISS III Satellite datasets

Turbidity is an optical determination of water clarity. It is one of the most important optically active water parameter to assess the water quality through the remote sensing observations. Turbidity measurements come from suspension of sediment such as silt or clay, inorganic materials, or organic matter such as algae, plankton and decaying material. Turbidity and total suspended matter often overlap each other. However, it is not a direct measurement of the total suspended materials in water. Instead, as a measure of relative clarity, turbidity is often used to indicate changes in the total suspended solids concentration in water without providing an exact measurement of solids. Through remote sensing we can monitor the turbidity in large water bodies, rives, coastal areas etc. An algorithm has been developed to estimate the turbidity (in NTU: Nephelometric Turbidity Unit) over inland waters (Ukai reservoir) using empirical relationship between normalized Green and Red bands (NDTI : Normalized Difference Turbidity Index) of Resourcesat-2 and Resourcsat-2A Linear Imaging Self Scanning-III (RS2 and R2A LISS-III) dataset. Derived algorithm shows a strong coefficient of determination (R2 = 0.97) with the in-situ turbidity measurements. The field measurements were carried out over Ukai reservoir on 27-28th March 2018, where synchronous in situ water leaving reflectance and turbidity were measured. Model was derived between in situ measured turbidity and NDTI derived from spectral reflectance of band 2 (Green) and band 3 (Red) of RS2 and R2A LISS-III. The model was applied to derive the turbidity maps of Ukai reservoir for pre-monsoon (March, April and May months) season during the period 2012 to 2018. Overall turbidity ranges from 1.47-20 NTU during the field data collection of pre-monsoon season and overall scene derived turbidity ranges are between 2 – 33 NTU. The highest observed turbidity value was more than fourteen times greater than the lowest value that shows the natural variability within the reservoir for the same season. Remotely sensed data sets can increase the abilities of water resources researchers and decision making persons to monitor waterbodies more effectively and frequently.

Assessment of Palm Jumeirah Island’s Construction Effects on the Surrounding Water Quality and Surface Temperatures during 2001–2020

Climate change stressors like rising and warmer seas, increased storms and droughts, and acidifying oceans are rapidly threatening coastal zones, which are the world’s most densely inhabited places. This research assesses the effects of Palm Jumeirah Island (PJI) construction on its surrounding water quality and temperature, using Landsat-7 and 8 spectral and thermal bands for the years 2001, 2014, 2016, 2019, and 2020. To aid in this goal, the changes in water spectral reflectance was observed and interpreted, based on previous research and measurements, to discover the correlation between water quality and its spectral reflectance. Then, the sea surface temperature (SST) was calculated for the years under review and changes in water temperature were evaluated. Finally, the Green Normalized Difference Vegetation Index (GNDVI) and the Normalized Difference Turbidity Index (NDTI) were calculated to estimate water chlorophyll levels and water turbidity, respectively, and changes were observed and interpreted for the time period under review. The present study showed that the PJI construction not only increased the water reflectance in the 0.5–0.8 µm of wavelength, which can be considered to be the increase of suspended sediments and chlorophyll but the water temperature also increased by 7.5 °C during the 19 years. In addition, a gradual increase in the values of GNDVI (by 0.097–0.129) and NDTI (by 0.118~0.172) were observed. A drop in chlorophyll and suspended sediment spectral reflectance and GNDVI and NDTI values were also observed in 2020 compared to 2019 which can be attributed to the 63 to 82% decrease in tourists in Dubai in 2020 as a result of the COVID-19 pandemic. This study aims to draw attention to environmental issues by clarifying the effect of creating artificial islands in the sea and our analysis and results are a suitable reference for specialized hydrological and environmental studies based on spectral information and distance measurements, as presented in this paper.