Search Results (11)

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. Full article

The utilization of Effective Microorganisms (EMs) for lake restoration represents a sustainable approach to enhancing water quality and rebalancing the ecology of aquatic ecosystems. The primary objective of this study was to evaluate the effects of two bioremediation treatment cycles employing EM-enriched biopreparations on water quality in the Siemiatycze lakes. Specifically, this research analyzed various parameters, including dissolved oxygen, transparency, chlorophyll-a, pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, and suspended matter (SM), across eleven designated sampling locations. Additionally, this study employed remote sensing techniques, leveraging Sentinel-2 satellite imagery and the Maximum Chlorophyll Index (MCI), to detect and quantify algal blooms, with a particular focus on elevated chlorophyll-a concentrations. This comprehensive approach aimed to provide a holistic understanding of the impact of biotechnological reclamation on aquatic ecosystem restoration and sustainability. The study’s findings indicated a significant improvement in water quality in all lakes, with enhanced water clarity and oxygen profiles. Further, remote sensing studies indicated a reduction in algal blooms, particularly those with high chlorophyll-a concentrations. A considerable decrease in water eutrophication intensity was observed due to diminished nutrient concentrations. The improvements in water parameters are likely to enhance the living conditions of aquatic organisms. These results demonstrate the effectiveness of using EM-enriched biopreparations in the bioremediation of lakes, providing a sustainable approach to enhancing water quality and balancing aquatic ecosystems. Full article

Since 2011, the proliferation of brown macro-algae of the genus Sargassum has considerably increased in the North Tropical Atlantic Sea, all the way from the Gulf of Guinea to the Caribbean Sea and the Gulf of Mexico. The large amount of Sargassum aggregations in that area cause major beaching events, which have a significant impact on the local economy and the environment and are starting to present a real threat to public health. In such a context, it is crucial to collect spatial and temporal data of Sargassum aggregations to understand their dynamics and predict stranding. Lately, indexes based on satellite imagery such as the Maximum Chlorophyll Index (MCI) or the Alternative Floating Algae Index (AFAI), have been developed and used to detect these Sargassum aggregations. However, their accuracy is questionable as they tend to detect various non-Sargassum features. To overcome false positive detection biases encountered by the index-thresholding methods, we developed two new deep learning models specific for Sargassum detection based on an encoder–decoder convolutional neural network (CNN). One was tuned to spectral bands from the multispectral instrument (MSI) onboard Sentinel-2 satellites and the other to the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 satellites. This specific new approach outperformed previous generalist deep learning models, such as ErisNet, UNet, and SegNet, in the detection of Sargassum from satellite images with the same training, with an F1-score of 0.88 using MSI images, and 0.76 using OLCI images. Indeed, the proposed CNN considered neighbor pixels, unlike ErisNet, and had fewer reduction levels than UNet and SegNet, allowing filiform objects such as Sargassum aggregations to be detected. Using both spectral and spatial features, it also yielded a better detection performance compared to algal index-based techniques. The CNN method proposed here recognizes new small aggregations that were previously undetected, provides more complete structures, and has a lower false-positive detection rate. Full article

“Sargassum” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of Sargassum aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio-economic issue for the population and the coastal ecosystem. Satellite ocean color data, such as those provided by the Sentinel-3/OLCI satellite sensor (Copernicus), can be used to detect the occurrences of Sargassum and to estimate their abundance per pixel using the Maximum Chlorophyll Index (noted MCI). Such an index is, however, ineffective if the algae are located beneath the sea surface, which frequently happens, considering the rough Caribbean oceanic waters. The objective of this study is to propose a relevant methodology that enables the detection of underwater Sargassum aggregations. The methodology relies on the inversion of the radiative transfer equation in the water column. The inverted model provides the immersion depth of the Sargassum aggregations (per pixel) and their fractional coverage from the above water reflectances. The overall methodology has been applied to Sentinel-3/OLCI data. The comparison with the MCI method, which is solely devoted to the sea surface retrieval of Sargassum aggregations, shows that the proposed methodology allows retrieving about twice more Sargassum aggregation occurrences than the MCI estimates. A relative increase of 31% of the fractional coverage over the entire study area is observed when using the proposed method in comparison to MCI method. For the satellite scenes considered here, the rate of Sargassum aggregations immersed between 2 m and 5 m depth ranges between 30% and 51% over the total amount (i.e., surface + in-water), which clearly demonstrates the importance of considering the retrieval of in-water aggregations to gain understanding on Sargassum spatial variability in the oceanic and coastal ecosystems. Full article

Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors, such as MODIS Aqua, offering neither the same spectral band configuration nor consistent spatial resolution, present challenges in ensuring continuity in derived bloom products. This study explores a neural network (NN) solution to fill the observation gap between MERIS and OLCI with MODIS Aqua data, delivering consistent algal bloom spatial extent products from 2002 to 2020 using these three sensors. With 14 bands of MODIS level 2 partially atmospherically corrected spectral reflectance as the NN input, the missing MERIS/OLCI band at 708 nm required for the MCI is simulated. The resulting NN-derived MODIS MCI (NNMCI) is shown to be in good agreement with MERIS and OLCI MCI in 2011 and 2017 respectively over the western basin of Lake Erie (R² = 0.84, RMSE = 0.0032). To overcome the impact of MODIS sensor saturation over bright water targets, which otherwise renders pixels unusable for bloom detection using R-NIR wavebands, a variant NN model is employed which uses the 9 MODIS bands with the lowest probability of saturation to simulate the MCI. This variant NN predicts MCI with only a small increase in uncertainty (R² = 0.73, RMSE = 0.005) allowing reliable estimates of bloom conditions in those previously unreported pixels. The NNMCI is shown to be robust when applied beyond the initial training dataset on Lake Erie, and when re-trained on different geographic areas (Lake Winnipeg and Lake of the Woods). Despite differences in spatial, temporal, and spectral resolution, MODIS algal bloom presence/absence was correctly classified in >92% of cases and bloom spatial extent derived within 25% uncertainty, allowing the application to the 2012–2015 time period to form a continuous and consistent multi-mission monitoring dataset from 2002 to 2020. Full article

Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m⁻³ in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m⁻³ in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs. Full article

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. Full article

Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m³ and saturation of the index at Chl-a ~300 mg/m³; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m³; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m³. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m³. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms. Full article

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R² of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R² of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R² of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments. Full article

Abstract: The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10–187.40 mg∙m⁻³ using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e. the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e. Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R²) in the range of 0.42–0.65. However, these processors underestimated the retrieved Chla > 100 mg∙m⁻³, reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R² = 0.65, Root mean square error (RMSE) = 22.18 mg∙m⁻³, the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg∙m⁻³, mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors—except for the BOL and FUB—were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e. band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime. Full article

The spatial and temporal variability of phytoplankton blooms was investigated in two tropical coastal regions of northern Australia using the MEdium Resolution Imaging Spectrometer (MERIS) full mission (2002–2012) reduced resolution dataset. Satellite-derived proxies for phytoplankton (Chlorophyll-a (Chl), Fluorescence Line Height (FLH), Maximum Chlorophyll Index (MCI)) and suspended sediment (Total Suspended Matter (TSM)) were jointly analyzed for two clusters of the Great Barrier Reef Wet tropics (GBRW; 15°–19.5°S; Queensland) and the Van Diemen Gulf (VDG; 9°–13°S; Northern Territory). The analysis of time-series and Hovmöller diagrams of the four MERIS products provided a unique perspective on the processes linking phytoplankton blooms and river runoff, or resuspension, across spatio-temporal scales. Both regions are characterized by a complex oceanography and seasonal inflows of sediment, freshwater and nutrients during the tropical wet season months (November to April). The GBRW is characterized by a great variability in water clarity (Secchi depth 0–25 m). A long history of agricultural land use has led to a large increase in the seasonal discharge of sediments and nutrients, triggering seasonal phytoplankton blooms (>0.4 mg∙m⁻³) between January and April. In contrast, the VDG is a poorly flushed, turbid (Secchi depth <5 m) environment with strong tidal-energy (4–8 m) and very limited land use. Phytoplankton blooms here were found to have higher Chl concentrations (>1.0 mg∙m⁻³) than in the GBRW, occurring up to twice a year between January and April. Over the 10-year MERIS mission, a weak decline in Chl and TSM was observed for the VDG (Sen slope: −2.85%/decade, τ = −0.32 and −3.57%/decade, τ = −0.24; p 0.05), while no significant trend in those two satellite products was observed in the GBRW. Cyanobacteria surface algal blooms occur in both regions between August and October. The MCI and FLH products were found to adequately complement Chl, while TSM provided relevant insight for the assessment of sediment resuspension and river runoff. Full article