A Novel Approach for Ex Situ Water Quality Monitoring Using the Google Earth Engine and Spectral Indices in Chilika Lake, Odisha, India

Abstract

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources.

1. Introduction

Water quality monitoring is one of the core practices in protecting the integrity of water bodies and safeguarding the health of the ecosystems and populations depending on them [1,2,3]. It systematically analyzes water’s physical, chemical, and biological properties to determine water quality or its vulnerability to and impairment by contaminant exposure [4,5]. Turbidity, chlorophyll, and total suspended solids levels are monitored in water to ascertain aquatic environments’ overall health and functioning [6,7]. Remote sensing, a means to collect information on objects or phenomena without direct physical contact, has become a potent tool for water quality monitoring. In remote sensing, this is realized through the measurement of reflectance or radiance of water bodies across the spectral bands of the electromagnetic spectrum [8,9,10]. It has been discovered that remote sensing techniques are effective in detecting floating algal blooms, identifying sources of pollution, and determining changes over time in the condition of the environment. Some water quality monitoring platforms exist in the remote sensing domain, including the Google Earth Engine (GEE) [11,12,13]. Based on the cloud environment, the GEE provides users with the full library of satellite imagery and geospatial datasets and a suite of analysis and visualization tools. Resources such as this, when applied, empower the GEE as a tool through which researchers, policymakers, and environmental practitioners can carry out long-term and in-depth assessments of water quality dynamics at regional, national, and global scales [14,15,16]. The GEE’s flexibility goes beyond data access and includes deploying algorithms and models tuned for one’s water quality monitoring objectives [17,18,19]. In this sense, remote-sensing data enable one to tap into sophisticated processing capabilities so that users can find actionable insights, identify trends, and evaluate the efficacy of their strategies for managing water. Furthermore, with the aid of the GEE, dynamic water quality maps, charts, and indicators enhance effective decision-making as they encourage environmental stewardship [20,21,22].

In addition to its analytical capabilities, the GEE has a user-friendly and intuitive interface that fosters the creation of web applications and interactive platforms to disseminate water quality information. The development of intuitive dashboards and visualization tools may help stakeholders engage with data-driven insights, make them aware of the issues related to water quality, and increase community participation in conservation. In this sense, the inclusion of the GEE in this effort represents a paradigm shift on a global scale in impact assessment and monitoring [23,24,25]. In so doing, the GEE can harness the cloud remote sensing technology promptly and cost-effectively in monitoring water resources, thus enabling an individual to put into place proactive measures in the management of water resources to be assured of the integrity of the ecosystems as well as ensuring public health [26,27,28]. Water quality monitoring, therefore, requires the assistance of remote sensing and the GEE to achieve the sustainable management and protection of water resources [29,30,31]. They might be used to gain deeper insights into ecosystems and aquatic environments, spot emerging threats, and focus the interventions in such a manner so as to ensure the long-term viability of freshwater environments for generations to come [32,33].

2. Materials and Methods

2.1. Study Area

In Odisha, on the east coast of India, at the mouth of the Daya River, which drains into the Bay of Bengal, sits the Chilika Lagoon, a RAMSAR site. It extends at latitude 19°43′ N and longitude 85°19′ E (Figure 1). It lies in the three coastal districts of Odisha, Puri, Khordha, and Ganjam, and has a total area of more than 1165 square kilometers.

The lake is a complex hydrological and ecological system receiving freshwater inflows from 52 streams and rivers, with saline tidal influx from the Bay of Bengal, and with several mouths around its periphery that open and close seasonally. The corresponding gradient of the depth and salinity categorizes the northern, central, southern, and outer channel zones. It is enriched with aquatic flora and fauna in addition to rare and endangered species like the Irrawaddy dolphin, fishing cat, and Chilika buffalo. The lake also hosts millions of migratory birds who visit the lake every winter. The lake is a source of livelihood for more than 0.2 million fisher folk and other communities. Several thousands of species of fish, birds, reptiles, invertebrates, and amphibians live in the lake. The lake also offers multiple ecosystem services, including water purification, control of floods, regulation of climate, and recreation. Some of the threats and challenges the lake faces include pollution, siltation, invasive species, habitat loss, and overexploitation of resources. Additionally, it has been designated as a priority site for Indian conservation and management, which is a Ramsar Site (an intergovernmental convention was adopted in 1971 in the Iranian city of Ramsar that aims to conserve and use wetlands sustainably and recognizes wetlands).

2.2. Data and Indices Used

2.2.1. Satellite Imagery

For this research, Sentinel-2 Level 2A (MSI) imagery was used (Table 1). The images provide surface reflectance (SR) corrected for atmospheric conditions, and the atmospheric effects were removed, allowing for a more accurate analysis of land cover features. Sentinel-2 Level 2A products are generated by applying atmospheric correction algorithms to level-1C data.

ESRI (Environmental System Research Institute)-based ArcGIS Pro software (2023) was used to perform the pre-processing stages on the imagery. The Apparent Reflectance Function calculates the top of atmosphere reflectance and sun angle adjustment by calibrating the digital number (DN) values of data obtained from satellite sensors. Maps are presented using the software ArcGIS 10.8.

2.2.2. Google Earth Engine (GEE)

It is a cloud-based service that uses satellite imagery and geospatial datasets to analyze Earth’s surface changes, trends, and differences [34]. It offers access to a vast catalog of historical and current imagery, data, and tools for processing and visualizing them [35]. Users can interact with the platform through web-based code editors, explorer apps, or Python and JavaScript client libraries. It supports applications like remote sensing, natural resource management, disease prediction, and environmental monitoring. It is free for academic and research use.

2.2.3. Water Quality Data

Data on the water quality of Chilika Lake in Odisha were gathered from the GEE Data Catalogue [36,37]. The study’s models were created using ex situ measurements of Chilika Lake and Sentinel-2 images [38]. The research was carried out using a variety of water quality measures, including total suspended solids, chlorophyll, and turbidity, using different seasonal imagery, as shown in Table 2.

2.3. Methodology

The data acquisition stage involves gathering Sentinel-2 satellite imagery, a multispectral data collection initiative by the European Space Agency. The image interpretation stage uses the Automated Water Extraction Index (AWEI) technique to identify water bodies using visible and near-infrared wavelengths [39]. The final output is a map or image highlighting the extracted water bodies, helpful in monitoring water resources, mapping floodplains, or studying wetland ecosystems. This simplified process allows for efficient water body extraction from Sentinel-2 satellite imagery, which is portrayed in Figure 2, and the detailed process is discussed below.

2.3.1. Atmospheric Correction

Sentinel-2 is a constellation of twin polar-orbiting satellites with a high 5-day revisit period of the equator [40]. MSI data offer thirteen spectral bands, precise spatial resolution, and twelve-bit quantization. They are helpful for vegetation research, water quality applications, aerosol and coastal retrievals, and inland water research [41,42,43]. The sensor’s bottom-of-atmosphere reflectance photo underwent atmospheric effect correction, and alternative algorithms have been proposed for water quality monitoring in the GEE [44,45].

SIAC: Sensor Invariant Atmospheric Correction estimates surface reflectance of land and water pixels. Assumes spectrally smooth atmospheric scattering. Validated for Sentinel-2 land pixels. Limited accuracy for water pixels [46].

ACOLITE: The Sentinel-2 Sensor Algorithm uses dark pixel subtraction to correct atmospheric effects and estimates water quality parameters like turbidity and chlorophyll-a from surface reflectance [47]. A modified Atmospheric Correction algorithm improves accuracy and spatial resolution by adjusting Sentinel-2 reflectance using a linear regression model [48]. Both algorithms use different formulas to calculate satellite images’ surface reflectance and water quality parameters. The SIAC algorithm estimates surface reflectance at band i.

(1)${\rho }_i=\pi L_i-{\alpha }_i/{\beta }_i$

where ${\rho }_i$ is the surface reflectance, $L_i$ is the top-of-atmosphere (TOA) radiance, and αi and ${\beta }_i$ are the coefficients derived from the sensor-invariant relationship.

The ACOLITE algorithm uses the following formula to estimate the surface reflectance at band i:

(2)${\rho }_i=\pi L_i-L_{dark,i}/F_0\cos \left({\theta }_s\right)$

where ${\rho }_i$ is the surface reflectance, $L_i$ is the TOA radiance, $L_{dark, i}$ is the dark pixel radiance, $F_0$ is the solar irradiance and ${\theta }_s$ is the solar zenith angle.

2.3.2. Cloud Masking

Cloud masking is supposed to remove pixels from the Sentinel-2 images affected by clouds or their shadows. Various methods are available in this regard [49].

2.3.3. Water Masking

Water masking is a method used to identify water pixels in satellite images, with increasing applications in water quality monitoring, flood mapping, and hydrological modeling [50]. One method is the Automatic Water Extraction Index (AWEI), which combines spectral information from different bands. To apply the AWEI to Sentinel-2 images in the Google Earth Engine (GEE), load the image collection, filter by date, region, and cloud, select required bands like blue, green, red, NIR, and SWIR, and define the AWEI function to calculate the water index.

(3)$AWEI=4\times \left(Green-SWIR1\right)-(0.25\ast NIR+2.75\ast SWIR1)$

The AWEI function maps images to a binary image, assigning 1 to water pixels and 0 to non-water pixels. A threshold value is set to indicate water pixels, with values above this threshold representing water pixels and values below this threshold representing non-water pixels [51]. For instance, a zero threshold would indicate that all positive AWEI values are water pixels. Display the water mask image on the map and export the results to a vector layer or a raster file.

2.3.4. Indices Used for Monitoring Water Quality Monitoring in the GEE

For this research work, we used these indices

• (a). NDTI (Normalized Difference Turbidity Index)

An NDTI band index is described as follows:

NDTI = Red − Green/Red + Green

The NDTI, a satellite-based water turbidity measure, is crucial for assessing water quality and aquatic ecosystems, indicating pollution, erosion, and sediment transport [52].

2.3.5. JavaScript for NDTI Estimation in the GEE

The processing steps for deriving TSI maps from satellite imagery are detailed below. The NDTI uses the reflectance properties of water and vegetation to estimate vegetation abundance. It indirectly provides access to water quality parameters such as turbidity and chlorophyll-a concentration [53,54]. The formula is:

Steps in the GEE:

• (a). Import Imagery:

Choose Sentinel-2 images as they offer suitable green and NIR bands. Filter by date (Winter season, Pre-Monsoon Season, Monsoon Season, or Post Monsoon Season), cloud cover, and other criteria relevant to your study area and objectives.

• (b). Load and Process: Load the selected image collection using ee.ImageCollection(). Apply cloud masking, atmospheric correction, and other pre-processing steps as needed.

• (c). NDTI Calculation:

The function to calculate the NDTI using JavaScript is as follows (Figure 3):

• (d). Add a visualization parameter:

The function to visualize the NDTI using JavaScript is as follows (Figure 4):

2.3.6. JavaScript for NDCI Estimation in the GEE

NDCI (Normalized Difference Chlorophyll Index)

An NDCI band index is described as follows:

NDCI = Red edge 1 − Red/Red edge1 + Red

Chlorophyll, a pigment in green plants and algae, indicates water quality and eutrophication. Remote sensing and the Google Earth Engine (GEE) can help monitor water quality trends globally by estimating chlorophyll-a concentrations [55]. Sentinel-2 satellite photos and the GEE enable accurate approximation of chlorophyll concentrations [56]. However, high readings call for further investigation and management to prevent harmful algal blooms and protect water bodies.

The NDCI is calculated using specific spectral bands to estimate chlorophyll-a concentration, an indicator of phytoplankton biomass and water quality [57,58].

Steps in the GEE:

• (a). Import Satellite Imagery: Choose Sentinel-2 imagery due to its suitable green and red bands. Filter by date (Winter season, Pre-Monsoon Season, Monsoon Season, or Post-Monsoon Season), cloud cover, and other criteria relevant to your study area and objectives.

• (b). Load and Process: Load the selected image collection using ee.ImageCollection().

Apply cloud masking, atmospheric correction, and other pre-processing steps as needed.

• (c). NDCI (Normalized Difference Chlorophyll Index) calculation: The function to calculate the NDCI using the formula above is written in JavaScript as follows (Figure 5)

• (d). Add a visualization parameter: The function to visualize the NDTI using JavaScript is as follows (Figure 6):

2.3.7. JavaScript for TSS Estimation in the GEE

TSS (Total Suspended Solids)

Suspended matter, a significant source of pollution in surface water, is crucial for water quality management [59]. Total suspended solids (TSS), a critical indicator of water quality, are monitored using remote sensing techniques like the GEE [60]. Algorithms like Doxaran and Markov models provide a synoptic view of TSS dynamics, improving water quality management and prediction [61].

• (a). The function to calculate TSS using the formula above is written in JavaScript as follows (Figure 7).

• (b). Add a visualization parameter: The function to visualize TSS using JavaScript is as follows (Figure 8):

3. Results

Chilika Lake follows the new Caledonian barrier reef; this is why it has been listed as a World Heritage Site. The salinity of the Chilika water varies by region, from freshwater to oceanic salinity due to tidal influx [62]. As it is a World Heritage Site, many tourists visit this place, which makes the surroundings more polluted. There are other polluting factors involved due to migratory birds [63]. Due to this, we studied various indices of water quality monitoring concerning seasonal and yearly data.

3.1. Seasonal Water Quality Variation

As per the Indian Metrological Department, Odisha experiences four major seasons (i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon). In the current study, a detailed analysis of the NDTI, NDCI, and TSS for all four seasons was carried out and is discussed below.

3.1.1. Winter Season

NDTI (Normalized Difference Turbidity Index)

The NDTI is a valuable tool for assessing water quality, offering insights into turbidity, sedimentation, and algal blooms [64]. High NDTI values result in reduced light penetration and impact aquatic photosynthesis, habitats, and biodiversity. A lower NDTI value may help improve the reduction in sedimentation and preserve habitats and biodiversity [65]. In the current study, we found that in 2019, the FNU maximum was recorded at 118.17, while the lowest recorded value was 26.655. The NDTI reached the highest value of 158.606 FNU and the lowest value of 19.806 FNU in 2021. In 2023, the NDTI recorded a high of 1909.83 FNU and a low of 370.775 FNU (Figure 9). Regular monitoring of turbidity using the NDTI during winter can provide valuable data for conservation efforts. It helps identify any unusual changes in water quality that might indicate pollution or other environmental issues, enabling timely intervention [66]. As the study area is a popular tourist destination, especially in winter, lower turbidity levels enhance the aesthetic appeal of the lake, attracting more tourists. This can have positive economic implications for the local communities dependent on tourism [67].

NDCI (Normalized Difference Chlorophyll Index)

A high NDCI value leads to excessive algal growth, potentially toxic to humans and aquatic life, and a low value indicates low nutrient levels, which impact aquatic productivity. During winter, the low temperature and lower nutrient inflow from the catchment area decrease the chlorophyll concentration. The NDCI can effectively monitor these changes, providing insights into the seasonal variations in phytoplankton biomass. Regular monitoring of chlorophyll levels using the NDCI during winter provides valuable data for water quality management [68].

Chlorophyll has a maximum value of 0.135 mg and a minimum value of −0.068 mg in 2019, according to the above maps and statistical data. In 2021, there was a maximum of 0.042 mg of chlorophyll-a and a minimum of −0.006 mg. In 2023, there was a high of 0.078 mg chlorophyll-a and a low of −0.028 mg (Figure 10). Monitoring chlorophyll levels using the NDCI during winter helps manage fish populations and ensure sustainable fishing practices. Lower chlorophyll levels can indicate healthier water conditions, which benefit fish growth and reproduction [69].

TSS (Total Suspended Solids)

During winter, less rainfall in the catchment area leads to lower sediment inflow, which results in lower TSS levels, which enhances water quality and impacts TSS, with it having a maximum value of 6701.34 mg/L and a minimum value of 1914.76 mg/L in 2021. The TSS in 2023 had a minimum value of 315.965 mg/L and a maximum value of 1113.42 mg/L (Figure 11). Lower TSS levels can improve fish health and growth by providing a more transparent and oxygen-rich environment. This can enhance fish populations and support sustainable fishing practices. Regular monitoring of TSS levels during winter offers valuable data for conservation efforts [70,71,72].

3.1.2. Pre-Monsoon Season

NDTI (Normalized Difference Turbidity Index)

In the pre-monsoon period, sediment load increases due to agricultural activities and soil erosion in the catchment area. Elevated turbidity levels affect aquatic life by reducing light penetration, essential for photosynthesis in marine plants [73]. Higher turbidity levels can affect fish health and breeding conditions. Higher turbidity levels can reduce the aesthetic appeal of the lake, affecting tourism [74]. The NDTI for the year 2019 had a maximum value of 702.304 FNU and a minimum value of 35.038 FNU, according to the mapping and statistical data above. The NDTI had a maximum value of 356.244 FNU and a minimum value of 39.683 FNU in 2021. In 2023, the NDTI reached a high of 1326.35 FNU and a low of −77.251 FNU (Figure 12). Monitoring turbidity using the NDTI during the pre-monsoon period is crucial for water quality management and also for attracting tourists [75].

NDCI (Normalized Difference Chlorophyll Index)

During the pre-monsoon period, there is an increase in agricultural activity and soil erosion, leading to higher nutrient runoff into Chilika Lake and resulting in a hike in chlorophyll levels [76]. Higher chlorophyll levels indicate increased phytoplankton biomass, which can affect the lake’s ecological balance. Elevated nutrient levels can lead to eutrophication, causing algal blooms that deplete oxygen in the water and harm aquatic life [77]. Higher chlorophyll levels can affect the food web dynamics in Chilika Lake. Monitoring chlorophyll levels using the NDCI during the pre-monsoon period can help manage fish populations and ensure sustainable fishing practices. Understanding phytoplankton dynamics is crucial for predicting fish stock health and availability [78].

According to the mapping mentioned above and statistical data, the highest and minimum values of chlorophyll-a in 2019 were 0.113 mg and −0.001 mg, respectively. In 2021, there was a maximum of 0.046 mg chlorophyll-a and a minimum of −0.016 mg. Chlorophyll-a had a maximum value of 0.083 mg and a minimum value of −0.013 mg in 2023 (Figure 13). Regular monitoring using the NDCI helps maintain water quality, essential for attracting tourists and supporting the local economy [79]. Regularly tracking chlorophyll levels using the NDCI during the pre-monsoon period provides valuable data for water quality management. It helps identify any unusual changes in phytoplankton biomass that might indicate pollution or other environmental issues, enabling timely intervention [80].

TSS (Total Suspended Solids)

In the pre-monsoon period, higher sediment runoff into Chilika Lake resulted in elevated TSS levels. Elevated TSS levels can reduce water clarity and quality, affecting the health of the aquatic ecosystem [81]. High TSS can carry pollutants and nutrients, leading to eutrophication, algal blooms, and oxygen depletion. Regular monitoring helps identify pollution sources and implement management strategies. High TSS levels can negatively impact aquatic life by reducing light penetration, essential for photosynthesis in aquatic plants [82]. The fishing community around Chilika Lake relies heavily on its resources. Elevated TSS levels can affect fish health and breeding conditions, reducing fish populations [83]. Monitoring TSS helps manage fish stocks and ensure sustainable fishing practices. Monitoring these levels is crucial for understanding sediment dynamics and their impact on the lake’s ecosystem [84].

According to the mapping and statistical data above, the TSS for the year 2019 had a maximum value of 3339.34 mg/L and a minimum value of 208.886 mg/L. The TSS in 2021 had a minimum value of 188.934 mg/L and a maximum value of 3176.90 mg/L. The minimum value of 175.206 mg/L and the maximum value of 817.706 mg/L for TSS were found in 2023 (Figure 14).

3.1.3. Monsoon Season

NDTI (Normalized Difference Turbidity Index)

The monsoon season brings heavy rainfall, increasing runoff from the surrounding catchment areas. This runoff carries a high load of sediments into Chilika Lake, resulting in elevated turbidity levels [85]. The NDTI can effectively monitor these changes, providing valuable data on sediment dynamics. High turbidity levels during the monsoon season can affect water quality by reducing light penetration and increasing the concentration of suspended particles [86]. Monitoring turbidity using the NDTI helps identify areas with poor water quality, enabling timely interventions to mitigate pollution and maintain the lake’s health [54]. Elevated turbidity levels can negatively impact aquatic life by reducing the availability of light for photosynthesis in aquatic plants [87]. The NDTI helps to assess these impacts and plan conservation measures. Monitoring these levels using the NDTI helps to manage fish populations and ensure sustainable fishing practices. Monitoring turbidity levels using the NDTI can help predict and manage flood events by providing data on sediment load and water flow patterns [88]. The NDTI for the year 2019 had a maximum value of 644.353 FNU and a minimum value of −118.078 FNU, according to the mapping and statistical data above. The NDTI had a minimum value of 75.631 FNU and a maximum value of 342.902 FNU in 2021. In 2023, the highest value of the NDTI was 84.481 FNU, while the lowest value was 319.128 FNU (Figure 15).

NDCI (Normalized Difference Chlorophyll Index)

The monsoon season brings heavy rainfall, increasing runoff from agricultural lands and surrounding areas. This runoff carries a high load of nutrients into Chilika Lake, resulting in elevated chlorophyll levels [89]. The elevated nutrient levels can lead to eutrophication, causing algal blooms that deplete oxygen in the water and harm aquatic life. Monitoring chlorophyll levels using the NDCI helps in the early detection of eutrophication, allowing for timely management interventions to prevent adverse effects on the lake’s ecosystem. Higher chlorophyll levels can affect the food web dynamics in Chilika Lake [90]. Monitoring chlorophyll levels using the NDCI during the monsoon period can help to manage fish populations and ensure sustainable fishing practices. Understanding phytoplankton dynamics is crucial for predicting fish stock health and availability. Regular monitoring of chlorophyll levels using the NDCI during the monsoon season provides valuable data for water quality management. It helps identify any unusual changes in phytoplankton biomass that might indicate pollution or other environmental issues, enabling timely intervention [91].

According to the given mapping and statistical data, the maximum and minimum values of chlorophyll-a in 2019 were 0.054 mg and −0.039 mg, respectively. The maximum and minimum amounts of chlorophyll-a in 2021 were 0.057 mg and 0.005 mg, respectively. In 2023, there was a maximum of 0.039 mg chlorophyll-a and a minimum of −0.007 mg (Figure 16).

TSS (Total Suspended Solids)

The monsoon season brings heavy rainfall, leading to substantial runoff from the surrounding catchment areas. This runoff carries a high load of sediments into Chilika Lake, resulting in elevated TSS levels [92]. Higher TSS levels have implications like reduced water clarity, impacting aquatic photosynthesis and recreational use; increased sedimentation, affecting habitats and biodiversity; higher risk of waterborne diseases and pathogens and decreased dissolved oxygen levels, harming aquatic life; and increased turbidity, interfering with ultraviolet (UV) disinfection [93]. Moderate TSS levels result in algal growth promotion, potentially leading to eutrophication, nutrient transport, and cycling impacts, changes in aquatic food webs, and sedimentation impacts on aquatic habitats. Low TSS levels result in improved water clarity, enhancing aquatic photosynthesis, reducing sedimentation, preserving habitats and biodiversity, and lowering the risk of waterborne diseases and pathogens [94].

Monitoring these levels is crucial for understanding sediment dynamics and their impact on the lake’s ecosystem. Elevated TSS levels can reduce water clarity and quality, affecting the health of the aquatic ecosystem. High TSS levels can carry pollutants and nutrients, contributing to eutrophication, and leading to algal blooms and oxygen depletion [95]. Regular monitoring helps to identify pollution sources and implement management strategies. High TSS levels can negatively impact aquatic life by reducing light penetration, essential for photosynthesis in aquatic plants [96]. This can disrupt the food web and affect the overall biodiversity of the lake. Fish and other organisms may also experience stress due to increased sedimentation [97]. High TSS levels can reduce the aesthetic appeal of the lake, affecting tourism. Clearwater is important for recreational activities such as boating and bird watching. Regular monitoring helps maintain water clarity and supports the local tourism industry [98]. The local community is crucial in maintaining water clarity and supporting the local tourism industry. The monsoon season often brings the risk of flooding. Monitoring TSS levels helps to predict and manage flood events by providing data on sediment load and water flow patterns. This information is crucial for designing effective flood control measures [99].

The TSS for the year 2019 had a maximum value of 5252.09 mg/L and a minimum value of 388.392 mg/L, according to the mapping and statistical data above. TSS has a maximum value of 2453.94 mg/L and a minimum value of 438.239 mg/L in 2021. With a high value of 518.07 mg/L and a low value of 110.72 mg/L, the TSS in 2023 (Figure 17).

3.1.4. Monsoon Season

NDTI (Normalized Difference Turbidity Index)

In the post-monsoon season, the lagoon receives significant river freshwater inflow carrying sediments and nutrients. The NDTI helps to monitor the increased turbidity levels due to sediment resuspension and runoff [100]. High turbidity can reduce water clarity, impacting aquatic life and the overall health of the lagoon. The post-monsoon period is characterized by high sediment deposition from riverine inputs. The NDTI can track these changes, providing insights into sediment dynamics and their impact on water quality [101]. The influx of nutrients from agricultural runoff and other sources during the post-monsoon season can lead to eutrophication. Turbidity influences light penetration in the water column, affecting phytoplankton growth [102]. Post-monsoon, increased turbidity can limit light availability, potentially reducing phytoplankton growth. The NDTI can detect changes in turbidity due to pollution events, such as industrial discharge or urban runoff [103]. Monitoring these changes during the post-monsoon season helps in the early detection and mitigation of pollution sources, ensuring that the lagoon’s water quality remains within safe limits [104]. By understanding turbidity patterns and their implications, stakeholders can make informed decisions to protect and sustain Chilika Lagoon’s biodiversity and ecological balance.

According to the mapping and statistical data above, the NDTI for the year 2019 had a maximum value of 262.823 FNU and a minimum value of 23.312 FNU. In 2021, it had a minimum value of 31.013 FNU and a maximum value of 75.244 FNU. In 2023, it had a maximum value of 504.286 FNU and a minimum value of 81.351 FNU (Figure 18).

NDCI (Normalized Difference Chlorophyll Index)

Post monsoon, the influx of nutrients from riverine runoff can lead to phytoplankton blooms. The NDCI helps detect these blooms by measuring chlorophyll-a concentrations [105]. High NDCI values indicate increased phytoplankton biomass, which can affect the lagoon’s primary productivity and food web dynamics [106]. The post-monsoon season often brings nutrient-rich runoff from agricultural fields and urban areas. The NDCI can monitor the extent of eutrophication by tracking chlorophyll-a levels [107]. Elevated chlorophyll-a concentrations can lead to hypoxic conditions, affecting aquatic life and water quality [108]. High phytoplankton biomass can reduce light penetration in the water column, affecting submerged aquatic vegetation and other photosynthetic organisms. NDCI data can help understand these changes and their implications for the lagoon’s ecological health.

According to the preceding mapping and statistical data, the maximum and minimum values of chlorophyll-a in 2019 were 0.043 mg and −0.061 mg, respectively. In 2021, there was a maximum of 0.101 mg of chlorophyll-a and a minimum of −0.017 mg. In 2023, the highest concentration of chlorophyll-a was found to be 0.027 mg, while the lowest concentration was found to be 0.019 mg (Figure 19).

TSS (Total Suspended Solids)

In the post-monsoon season, the lagoon receives substantial freshwater inflow from rivers, carrying sediments and organic matter, leading to increased TSS levels and higher turbidity [109]. Elevated turbidity can reduce light penetration, affecting photosynthesis and the growth of submerged aquatic vegetation [110]. The post-monsoon period is characterized by significant sediment deposition from riverine inputs. High TSS levels indicate increased sediment load, which can alter the lagoon’s geomorphology and impact habitats for aquatic organisms [111]. High TSS levels can also indicate the presence of pollutants, such as heavy metals and pesticides, attached to sediment particles. Monitoring TSS helps identify pollution sources and assess their impact on water quality [112]. Increased TSS can clog fish gills, reduce feeding efficiency, and smother benthic habitats. This can lead to a decline in fish populations and affect the overall biodiversity of the lagoon [113].

According to the mapping above and statistical data, the TSS in 2019 had a high value of 4479.57 mg/L and a low value of 1450.71 mg/L. The year 2021 saw a maximum TSS value of 6309.97 mg/L and a minimum value of 708.087 mg/L. The TSS had a maximum value of 794.806 mg/L and a minimum value of 90.01 mg/L in 2023 (Figure 20).

4. Discussion

Based on our observations, we found that the NDTI value of the water in Chilika Lake climbed gradually from 2019 to 2021 before sharply increasing in 2023. While the water NDTI increased explicitly in the pre-monsoon and monsoon seasons in 2019 and 2021, it increased in the winter and post-monsoon seasons in 2023. In the monsoon season, however, it decreased to a modest value, indicating that the water is somewhat clearer and more obvious than in previous years [114,115]. There was an increase in the amount of chlorophyll-a, as observed when using the NDCI. By year, it grew progressively between 2019 and 2023. Additionally, it was noted that chlorophyll-a rose quickly throughout the pre-monsoon and monsoon seasons; in contrast, there was a significant increase in chlorophyll-a during the winter and pre-monsoon seasons of 2021 [116]. With the help of all of these observations, we can see that the amount of chlorophyll-a in Chilika Lake has been rising annually.

Based on our most recent TSS indices, it was concluded that the TSS value increased mainly during the winter and monsoon seasons in 2019. By 2021, we noted that the growing value shifted to the winter and post-monsoon seasons [117]. Conversely, we observe that the winter and pre-monsoon seasons significantly increased the TSS value. As we can see from the above, the TSS value primarily increased during the winter months each year. These days, the quality of the water is improving. The value of TSS generally increases over the years, indicating an improvement in the water quality these days [118,119,120].

Upon creating a unified map with all of the monthly data for a given year, we noticed that the NDTI, NDCI, and TSS values climbed from 2019 to 2021 to 2023 [121,122]. Additionally, one can see that while the NDTI and NDCI values increased in 2023, the TSS value decreased compared to the year before [123,124]. This is likely because the composite data we used to create the value were primarily an average or mean value, which is what caused the TSS value to decrease in 2023 (Table 3).

4.1. Interpretation

This study using the GEE coupled with spectral indices provides a cost-effective and efficient method for monitoring water quality in Chilika Lake, which can be replicated in other aquatic ecosystems [125]. The findings can inform decision-makers and stakeholders about the lake’s water quality status to develop targeted management strategies to mitigate pollution and employ conservative approaches [126]. The findings can contribute to conservation efforts by identifying areas with poor water quality [127]. The GEE and spectral indices can help monitor the impacts of climate change on water quality, enabling researchers to understand the complex relationships between climate variables and water quality parameters [128]. The integrated approaches of classical monitoring along with Remote sensing-based studies have always proved to be an accurate and precise means to study different water quality parameters, and both approaches suffice each other [129,130,131,132,133,134]. The identified areas with poor water quality in our research can be addressed by the concerned authorities. As the water is affected by different industries neighboring the lake, which contribute to water deterioration [135], decision-makers have employed initiatives to address these issues, and over the course of time, some prominent positive outcomes can be noticed like improved salinity levels, fish landings, a decrease in invasive species, recovery of lost habitats for important species, etc. Further rigid and spatial targets can be planned by decision-makers based on the findings for the sustainable water quality management of the Chilika lagoon [136]. These points highlight their significance and emphasize their potential to contribute to effective water quality monitoring, informed decision-making, conservation efforts, climate change research, and scalability [137].

4.2. Limitations

The spatial resolution may not capture all small-scale water quality variations, particularly in areas with complex shoreline features [138]. Our approach provides depth-integrated measurements, which may not accurately represent water quality at specific depths or in regions with stratified water columns. Atmospheric and environmental factors, such as cloud cover, aerosols, and sediment resuspension, can affect the accuracy of spectral indices and water quality retrievals [139]. The availability and quality of ground truth data may limit the accuracy of our approach, particularly in areas with limited in-situ measurements [140]. Our study focuses on a specific period, and our results may not represent long-term water quality trends or seasonal variations. Spectral indices may not be specific to certain water quality parameters, potentially leading to ambiguous interpretations [141,142]. While we used available in-situ data for validation, more extensive in-situ measurements would strengthen the accuracy and reliability of our approach [143,144,145,146,147].

4.3. Recommendations

We recommend the following avenues for further studies or analyses based on our findings: a combined GEE-derived spectral index with extensive in situ water quality measurements to improve accuracy and validation; extension of the study period to capture seasonal and inter-annual water quality variations, enabling a better understanding of Chilika Lake’s dynamics; utilization of high-resolution satellite or aerial imagery to capture small-scale water quality variations and complex shoreline features; exploration of the application of machine learning algorithms to improve the accuracy and robustness of spectral index-based water quality retrievals; integration of GEE-derived water quality parameters into hydrodynamic or water quality models to simulate and predict Chilika Lake’s water quality; and conducting comparative studies with other lakes or coastal ecosystems to evaluate the transferability and scalability of our approach. To enhance confidence in our findings, we could validate our results with alternative data sources, such as autonomous underwater vehicles (AUVs) or unmanned aerial vehicles (UAVs).

5. Conclusions

The NDTI, NDCI, and TSS are the primary indices we used to monitor water quality throughout our study. TSS and the NDTI indicate the water’s purity and are indirectly related to water quality. Additionally, the NDCI is a separate indicator that displays the rate of chlorophyll-a growth. Our research demonstrates that other elements, such as climatic or meteorological conditions, rain, flooding, tourism, etc., occasionally affect the water quality in Chilka Lake. The water quality index primarily varied throughout the winter and monsoon seasons, as we can see. Even though foreign birds migrate through Chilka, it is a popular tourist destination. Additionally, the geographic location of Chilika contributed to the water contamination there. Its water is salty, which is a relief. According to our research, Chilika Lake water is becoming more contaminated than it was in 2019. During winter, it makes sense that human activity, natural disasters, and migratory animals are the leading causes of pollution in Chilika Lake water.

Author Contributions

Subhasmita Das, Debabrata Nandi, and Rakesh Ranjan Thakur: conceptualization and writing—original draft; Dillip Kumar Bera and Duryadhan Behera contributed to all sections; Bojan Đurin and Vlado Cetl: supervision and review, and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data will be provided upon request.

Acknowledgments

All authors acknowledge the Remote Sensing and GIS Department for use of their software and infrastructure to prepare the manuscript.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Study area.

Figure 2. Flow chart of the methodology.

Figure 3. JavaScript code for NDTI calculation.

Figure 4. JavaScript code for NDTI visualization.

Figure 5. JavaScript code for NDCI calculation.

Figure 6. JavaScript code for NDCI visualization.

Figure 7. JavaScript code for TSS calculation.

Figure 8. JavaScript code for TSS visualization.

Figure 9. Winter season NDTI map and chart (2019/2021/2023).

Figure 10. Winter season NDCI map and chart (2019/2021/2023).

Figure 11. Winter season TSS map and chart (2019/2021/2023).

Figure 12. Pre-monsoon season NDTI map and chart (2019/2021/2023).

Figure 13. Pre-monsoon season NDCI map and chart (2019/2021/2023).

Figure 14. Pre-monsoon season TSS map and chart (2019/2021/2023).

Figure 15. Monsoon season NDTI map and chart (2019/2021/2023).

Figure 16. Monsoon season NDCI map and chart (2019/2021/2023).

Figure 17. Monsoon season TSS map and chart (2019/2021/2023).

Figure 18. Post-monsoon season NDTI map and chart (2019/2021/2023).

Figure 19. Post-monsoon season NDCI map and chart.

Figure 20. Post-monsoon season TSS map and chart (2019/2021/2023).

Table 1

Sentinel-2 Level 2A imagery datasets.

MSI Bands (Spatial Resolution)	Central Wavelength (nm)	Band Width (nm)	Lref W/(m2 sr µm)
B1—(60 m)	443.9 (Aerosols)	21	0.0001
B2—(10 m)	496.6 (Blue)	66	0.0001
B3—(10 m)	560 (Green)	36	0.0001
B4—(10 m)	664.5 (Red)	31	0.0001
B5—(20 m)	703.9 (Red-edge 1)	15	0.0001
B6—(20 m)	740.2 (Red-edge 2)	15	0.0001
B7—(20 m)	782.5 (Red-edge 3)	20	0.0001
B8—(10 m)	835.1 (NIR)	106	0.0001
B8-A—(20 m)	864.8 (Red-edge 4)	21	0.0001
B9—(60 m)	945 (Water Vapor)	20	0.001
B10—(60 m)	1375 (SWIR)	31	1
B11—(20 m)	1613.7 (SWIR 1)	91	0.0001
B12—(20 m)	2202.4 (SWIR 2)	175	0.0001

Table 2

Details of the study area with no indices and different seasonal imagery.

Study Area	Area of Lake (km2)	No. of Indices	Different Seasonal Imagery
Chilika lake, Odisha, India	1165	NDTI NDCI TSS	Winter season (January–February)
			Pre-Monsoon Season (March–May)
			Monsoon Season (June–August)
			Post-Monsoon Season (September–November)

Table 3

Yearly overview of maximum and minimum values of water quality indices.

Year
2019						2021						2023
NDTI		NDCI		TSS		NDTI		NDCI		TSS		NDTI		NDCI		TSS
(FNU)		(mg)		(mg/L)		(FNU)		(mg)		(mg/L)		(FNU)		(mg)		(mg/L)
MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN	MAX	MIN
702.304	23.312	0.135	−0.073	9282	208.886	365.224	−5.187	0.106	−0.084	7608.18	188.934	2105.41	−433.419	0.083	−0.0321	1178.01	90.01

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