Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Analysis of bio-optical active constituents for lentic ecosystem through spectral-spatial and in-vitro observation

The neural network algorithm approach was adopted in Kolavai Lake to retrieve the inherent optical properties (IOP) of active constituents. The retrieval of IOP by absorption and the scattering of optically active constituents (OAC) through employing Sentinel-2 MSI reflectance and field measured the salinity and temperature. The result illustrates the relationship between the IOP and measured OAC's concentrations and its sensitivity towards spectral wavelength. It shows that the phytoplankton absorption ap is highly related with chlorophyll-a concentration and has an R2 value of 0.808. Furthermore, at the total absorption of water has high correlation with chl-a which indicates the significant dominance in the lentic water. Also, the pigment constituents are showing an R2 value of 0.754. The total backscattering of water (btot) is strongly related to the total suspended matter with R value > 0.73. The spatial distribution of OAC in Kolavai Lake helps monitor the lake water quality. This approach is well-performed in estimating the inherent optical properties of optically active constituents that gives insight for assessing the relationship between IOP and water quality. The research has proved to be a good potential for monitoring lentic water quality through Sentinel-2 MSI.

Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images

The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R² values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R² > 0.90.

Long-term variation of dissolved metals and metalloid in the waters of an Atlantic mesotidal estuary (Sado Estuary, Portugal)

Estuaries have long been preferred sites of human settlement due to the benefits regarding proximity to fresh water and the ocean. As such, these environments have been subject to increasing anthropogenic pressures, resulting in issues of pollution and contamination. However, since the second half of the 20th century an environmental concern has reflected in the development of legislation, monitoring programmes and measures to diminish and control those impacts. The study presented herein integrates metals and metalloid concentrations from surface water samples obtained in a long-term monitoring programme (1986-2020) conducted in the Sado Estuary. The results obtained show a decrease and stabilisation of the concentrations of elements (between 81 % for Pb and 11 % for As in the average concentrations, between 83 % for Pb and 11 % for Cd in the median concentrations, and an increase of 1 % in the As median values). Nevertheless, high concentrations were still observed in the stations closest to the industrial area and the main freshwater to confluence with the estuary. Despite the efforts in improving the environmental quality of the Sado Estuary, possible effects in native species such as cuttlefishes and oysters are still a possibility, particularly in the stations where higher concentrations were registered, as well as close to nurseries as a result of trace metal transport through currents and tides.

Increased oyster aquaculture in the Sado Estuary (Portugal): How to ensure ecosystem sustainability?

Aquaculture is one of the fastest growing sectors in the world. However, this may come with a cost, as increasing aquatic production is likely to impose changes in the environment. To ensure ecosystem sustainability, it is essential to think on this larger scale. This study aims to use the Delft3D model suite to evaluate the ecological carrying capacity for bivalve production in the Sado Estuary (Portugal), under present and future conditions (2050). Scenarios for increased oyster production resulted in reductions of chlorophyll a associated with increased nutrient concentrations. In the most extreme production scenario, which considered an increase of 100 ha in production area, a predicted decrease of 90 % in phytoplankton biomass was observed. Climate change (CC) was incorporated as an increase in sea level and water temperature, as well as a reduction in river flow. Under present oyster production conditions, CC revealed contrasting patterns, i.e. an increase in chlorophyll a concentrations and a reduction in nutrients. These results suggest that CC has a positive effect in counteracting the impacts of increased oyster production, however further research is necessary. All scenarios point to reduced dissolved oxygen concentrations, highlighting the need to monitor this parameter. Given the difficulty in defining what are unacceptable impacts to the ecosystem it would be prudent to include a socio-ecological framework in the future, in order to integrate ecosystem services and the perception of local stakeholders.

Assessment of some water quality parameters in the Red River downstream, Vietnam by combining field monitoring and remote sensing method

The Red River is the largest river in northern Vietnam, and it serves as the main water source for production and human activities in the Red River Delta region. Cities and provinces located in the Red River Delta, for example, Hanoi, Nam Dinh, and Ha Nam, have experienced rapid economic growth with various large urban, industrial zones, and agricultural areas. As a result of urbanization and industrialization, surface water in this region has been contaminated by multiple anthropogenic sources. In this study, in addition to water quality assessment using WQI, we used the reflectance values of visible and near-infrared bands and in situ data to build a regression model for several water quality parameters. Among ten parameters examined, two parameters, including total suspended solids (TSS) and turbidity, were used to construct regression correlation models using the Sentinel-2 multispectral images. Our results can contribute useful information for comprehensive monitoring, evaluation, and management scheme of water quality in the Red River Delta. The application of this method can overcome the limitation of actual observation results that only reflect local contamination status and require a lot of sampling and laboratory efforts.

Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms

Suspended matter concentration is an important index for the assessment of a water environment and it is also one of the core parameters for remote sensing inversion of water color. Due to the optical complexity of a water body and the interaction between different water quality parameters, the remote sensing inversion accuracy of suspended matter concentration is currently limited. To solve this problem, based on the remote sensing images from Gaofen-2 (GF-2) and the field-measured suspended matter concentration, taking a section of the Haihe River as the study area, this study establishes a remote sensing inversion model. The model combines the partial least squares (PLS) algorithm and the particle swarm optimization (PSO) algorithm to optimize the back-propagation neural network (BPNN) model, i.e., the PLS-PSO-BPNN model. The partial least squares algorithm is involved in screening the input values of the neural network model. The particle swarm optimization algorithm optimizes the weights and thresholds of the neural network model and it thus effectively overcomes the over-fitting of the neural network. The inversion accuracy of the optimized neural network model is compared with that of the partial least squares model and the traditional neural network model by determining the coefficient, the mean absolute error, the root mean square error, the correlation coefficient and the relative root mean square error. The results indicate that the root mean squared error of the PLS-PSO-BPNN inversion model was 3.05 mg/L, which is higher than the accuracy of the statistical regression model. The developed PLS-PSO-BPNN model could be widely applied in other areas to better invert the water quality parameters of surface water.

Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

Tidal variability of water quality parameters in a mesotidal estuary (Sado Estuary, Portugal)

To establish effective water quality monitoring strategies in estuaries, it is imperative to identify and understand the main drivers for the variation of water quality parameters. The tidal effect is an important factor of the daily and fortnightly variability in several estuaries. However, the extent of that influence on the different physicochemical and biological parameters is still overlooked in some estuarine systems, such as the Sado Estuary, a mesotidal estuary located on the west coast of Portugal. The main objective of this study was to determine how the water quality parameters of the Sado Estuary varied with the fortnightly and the semidiurnal tidal variation. To achieve this goal, sampling campaigns were conducted in May/18, Nov/18 and Jun/19, under neap and spring tidal conditions, with data collection over the tidal cycle. Results were observed to be significantly influenced by the tidal variation, in a large area of the estuary. Flood seemed to mitigate possible effects of nutrient enrichment in the water column. Additionally, significant differences were also observed when considering the different sampling stations. Temperature, Suspended Particulate Matter (SPM) and nutrients showed the highest values at low water. Lastly, the implications of the tidal variability in the evaluation of the water quality according to Water Framework Directive were also discussed, highlighting the importance of studying short-time scale variations and the worst-case scenario to ensure water quality is maintained. These findings are relevant for the implementation of regional management plans and to promote sustainable development.

A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.