First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel - 2 and Google Earth engine - Bhadra and Tungabhadra, India

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (ZSD). The relationship between in situ ZSD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.

On the Retrieval of the Water Quality Parameters from Sentinel-3/2 and Landsat-8 OLI in the Nile Delta’s Coastal and Inland Waters

Reduced water quality due to the eutrophication process causes large economic losses worldwide. Multi-source remotely-sensed water quality monitoring can help provide effective water resource management. The research evaluates the retrieval of the water quality parameters: chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), over optically different water types. Cross-sensor performance analysis of three satellite data sources: Sentinel-3 Ocean Land Color Imager (OLCI), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI), acquired during a 45 min overpass on the Nile Delta coast on 22 March 2020 was performed. Atmospheric correction using the case 2 Regional Coast Color (C2RCC) was applied using local water temperature and salinity averages. Owing to the lack of ground-truth measurements in the coastal water, results were inter-compared with standard simultaneous color products of the Copernicus Marine Environment Monitoring Service (CMEMS), OLCI water full resolution (WFR), and the MODIS Aqua, in order to highlight the sensor data relative performance in the Nile Delta’s coastal and inland waters. Validation of estimates was carried out for the only cloud-free MSI data available in the 18–20 September 2020 period for the Burullus Lake nearly contemporaneous with in situ measurements in the 22–25 September 2020. Inter-comparison of the retrieved parameters showed good congruence and correlation among all data in the coastal water, while this comparison returned low positive or negative correlation in the inland lake waters. In the coastal water, all investigated sensors and reference data showed Chl-a content average of 3.14 mg m−3 with a range level of 0.39–4.81 mg m−3. TSM averaged 7.66 g m−3 in the range of 6.32–10.18 g m−3. CDOM clarified mean of 0.18 m−1 in the range level of 0.13–0.30 m−1. Analysis of the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) clarified that the MSI sensor was ranked first achieving the smallest MAE and RMSE for the Chl-a contents, while the EFR proved superior for TSM and CDOM estimates. Validation of results in Burullus Lake indicated a clear underestimation on average of 35.35% for the Chl-a induced by the land adjacency effect, shallow bottom depths, and the optical dominance of the TSM and the CDOM absorption intermixed in turbid water loaded with abundant green algae species and counts. The underestimation error increased at larger estimates of the algal composition/abundance (total counts, Chlorophyacea, Euglenophycaea, and Bacillariophycaea) and the biological contents (carbohydrates, lipids, and proteins), arranged in decreasing order. The largest normalized RMSE estimates marked the downstream areas where the inflow of polluted water persistently brings nutrient loads of nitrogen and phosphorous compounds as well as substantial amounts of detrital particles and sediments discharged from the agricultural and industrial drains and the land use changes related to agricultural practices, resulting in the increase of water turbidity giving rise to inaccurate Chl-a estimates.

Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil

In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and r2 = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton.

Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.

A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Assessment of water quality in a tropical ramsar wetland of southern India in the wake of COVID-19

The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April-May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.

New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters

The increased frequency of harmful algal blooms (HABs) is a major environmental concern worldwide, resulting not only in increased treatment costs for drinking water but also in impacts on tourism, commercial fishing and aquaculture and risks to human and animal health. Traditional strategies with ship-based approaches based on field sampling and laboratory analysis have been adopted to assess HABs. However, these methods are labour intensive and costly and do not provide synoptic views of the bloom conditions. Here, we show that the Sentinel-2 twin satellite mission of the Copernicus programme, in combination with in situ data, is a powerful tool that can offer valuable spatiotemporal information about a bloom of the dinoflagellate Lingulodinium polyedra that occurred on the SW Iberian Peninsula. Using the robust ACOLITE atmospheric correction processor combined with the normalized difference chlorophyll index (NDCI), the enhanced mapping of small blooms can be performed at a 10 m spatial resolution, revealing surface patches and a heterogeneous distribution. This research also demonstrates the improved capabilities of Sentinel-2 compared to those of Landsat-8 and Sentinel-3 for continuous monitoring. The Sentinel-3 and Sentinel-2 missions provide ecosystem observations that allow the environmental community and water managers to evaluate changes in water quality and bloom distribution and that facilitate field-based measurements. Therefore, the value added by the Copernicus products in terms of frequency and synoptic observations is of paramount importance for ecological and management purposes at regional and national scales.

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

Eutrophication Monitoring for Lake Pamvotis, Greece, Using Sentinel-2 Data

The use of remote sensing to monitor inland waters and their current state is of high importance, as fresh waters are the habitat of many species of flora and fauna, and are also important for anthropogenic activities. Water quality can be monitored by many parameters, including dissolved suspended matter, phytoplankton, turbidity, and dissolved organic matter, while the concentration of chlorophyll-a (chl-a) is a representative indicator for detecting phytoplankton and monitoring water quality. The detection of phytoplankton in water layers, through chl-a indicators, is an effective method for displaying eutrophication. Numerous scientific publications and studies have shown that remote sensing data and techniques are capable of monitoring the temporal and spatial distribution and variation of this phenomenon. This study aimed to investigate the eutrophication in Pamvotis Lake, in Ioannina, Greece with the application of chl-a detection algorithms, by using Sentinel-2 satellite imagery data for the time period of 2016–2018. The maximum chlorophyll index (MCI) and maximum peak-height (MPH) algorithms have been applied to top of atmosphere (TOA) reflectance data, to detect chl-a and monitor the trophic range of the water body. Both algorithms were correlated and resulted in Pearson’s r values up to 0.95. Finally, the chl-a concentration was estimated by applying an empirical equation that correlates the MPH and chl-a concentration developed within previous studies. Those results were further analyzed and interpreted with spatial statistical methods, to understand the spatial distribution pattern of the eutrophication in our study area. Our results demonstrated that Pamvotis Lake is a eutrophic lake, and the highest chl-a concentration was located in the east and south-east of the lake during the study period. Sentinel-2 data can be a useful tool for lake managers, in order to estimate the spatial distribution of the chl-a concentration and identify areas prone to eutrophication, as well as the coastal zones that may influence the lake through water canals.