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Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake. [ABSTRACT FROM AUTHOR]

The high concentration of chlorophyll a (Chl-a) and pollutants in estuaries has led to the deterioration of marine environmental quality and a reduction in the sustainability and safety of marine ecosystems. This study analyzed the distribution characteristics of Chl-a and pollutants in the Liugu Estuary (LE) based on field survey data. By incorporating environmental factors, the water's nutritional status and nutrient limitations were evaluated. The results indicated that there were variations in the concentration range of Chl-a and pollutants between flood and non-flood seasons in the LE, with nitrogen identified as the main limiting nutrient. The correlation indicated that Chl-a and nutrient levels in the LE were influenced by a complex interplay of multiple factors. River inputs with higher pollution levels increased concentrations and reduced DO levels, limiting the rise in Chl-a concentration and the uptake of nutrients by phytoplankton. The findings of this study could provide essential information for an in-depth examination of the eutrophication characteristics of the LE and offer a scientific foundation for the restoration and management of the estuary's ecological environment. [ABSTRACT FROM AUTHOR]

As a result of the Russian armed aggression, Ukraine has lost almost a third of its accumulated fresh water reserves worth more than USD18 billion. It has caused a loss of access to quality drinking water for 6 million people in Ukraine and more that 13 million people have a limited access to water for satisfying sanitary and hygienic needs. The situation is complicated due to the destruction of the Kakhovka hydroelectric power station dam which has led to a large-scale man-made disaster, severe negative environmental and socioeconomic consequences, the beginning of natural-climatic transformations of the drained water reservoir. The article presents comprehensive research of the state of the Kakhovka Reservoir functioning before and after the destruction of the hydroelectric power station dam on the basis of the actual facts and the results of decoded satellite imagery of Sentinel 2, Sentinel-3 and Landsat 8–9. It was found that the water reservoir drainage has caused disruption of microclimatic conditions, an increase in the air temperature by 2.0°С and more, accelerated evapotranspiration by 1.41–2.04 times, exacerbation of water scarcity in 58.2% of the territory of the drained water area and in the adjacent territories. About 52.0% of the territory of the drained water reservoir has stressful conditions of natural-climatic functioning. The obtained results confirm that the formed aboveground plant biomass of bottom landscapes is not capable of creating appropriate microclimatic conditions which existed in the water area of the Kakhovka Reservoir.

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating the coastal Chl-a concentration levels. The present research primarily focuses on understanding the significance of Tiber River −driven spatial dynamics of Chl-a and TSM along the central Tyrrhenian Sea coasts. The research also focuses on evaluating the applicability of Sentinel-2 and identifying a suitable method for estimating Chl-a and TSM from Sentinel-2. Neural networks and dark spectrum fitting techniques were applied using multiple algorithms to estimate the dynamic distribution of Chl-a and TSM driven by the Tiber River in the study area. Multiple statistical analyses were performed, and statistically significant relationships were observed. The Case-2 Regional Coast Colour Neural Network (C2RCC-Net) outperformed all other algorithms, with an R2 value of 0.903 for Chl-a and an R2 value of 0.966 for TSM. Furthermore, the present research also identified a positive pixel to pixel spatial correlation between Chl-a and TSM in all four seasons, highlighting the positive impact of Tiber River on maintaining Chl-a levels along the coasts of Tyrrhenian Sea. This stands in contrast with the negative trend seen in the Mediterranean scale.

Tropical cyclone (TC) Amphan is analyzed in terms of the various factors that governed the intensification process associated with it and compared with Fani. Furthermore, the TC radial characteristics and ocean productivity are examined. Notably, both TCs formed in the Bay of Bengal during the pre‐monsoon seasons of 2020 and 2019, respectively. For this study, both ocean and atmospheric parameters from various sources including global analyses, satellite observations, and outputs from Model for Prediction Across Scales‐Atmosphere (MPAS‐A) and Advanced Research Weather Research and Forecasting (WRF‐ARW), are considered. The results indicate a gradual decrease in vertical wind shear during Fani. In the case of Amphan, the increase in mid‐tropospheric relative humidity values is found to be substantial. The sea surface cooling after the passage of Amphan was higher than in the case of Fani. The higher sea surface temperature in the Amphan case corresponds to the lower aerosol loading (partly because of lockdown measures) than that of Fani in the pre‐cyclone phase. And the decrease (increase) in aerosol loading coincides with an increase (decrease) in the direct radiative forcing at the ocean surface. The Madden–Julian Oscillation played a greater role in the cyclogenesis of Fani, but Kelvin waves offered a major support in the case of Amphan. The warmer sea surface, higher tropical cyclone heat potential, and conducive ocean and atmospheric setting together supported the further intensification of Amphan to the supercyclone stage. The difference in chlorophyll concentration showed a significant variation, with higher positive values seen in the case of Amphan implicating greater vertical mixing. The numerical modeling effort indicated superior performance of MPAS‐A compared to WRF‐ARW in simulating the radial parameters of the TCs. [ABSTRACT FROM AUTHOR]

Phytoplankton blooms occurred in the Central Indian Ocean after tropical cyclone (TC) Emeraude with an elliptic track in March 2016. Here, the ecological responses of the upper ocean layer were first researched based on remote sensing, reanalysis data and Argo data. The results show that two clockwise cyclonic eddies with a vorticity of 0.22 s–1 appeared on the southern equatorial current after the TC. Chlorophyll-a (Chl-a) blooms were found in the two eddies after the TC. The Chl-a concentration in the center of the cyclonic eddies reached 0.5 mg∙m–3. The deepened mixed layer, weakened thermocline, and thinned barrier layer facilitated the uplift of nutrients from the deep sea to the upper layer. Additionally, the sufficient photosynthetically available radiation (PAR) over the two eddies reached 50 Einstein m–2∙day–1 after March 24, thereby providing favorable conditions for the Chl-a blooms that occurred on March 26 in Box A and March 28 in Box B. This study contributes to assessments of the ecological impacts of ocean eddies in the Central Indian Ocean after TCs.

The purpose of this study is to assess long-term trends in Chl-a and SST in major fishing zones associated with fish production over an 18-year period (2002–2019) in the Gulf of Suez, Gulf of Aqaba, and the Red Sea proper. The study found that seasonal SST mean values ​​differed significantly (P

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake. [ABSTRACT FROM AUTHOR]

Amino acids are the more labile compounds of organic matter in aquatic and terrestrial ecosystems. The variability of particulate amino acids in coastal waters depends on several biological (in situ production, grazing by heterotrophs) and physical (river discharge, terrestrial runoff, precipitation) processes. Coastal water samples of the Bay of Bengal were collected at three fixed locations from surface to 100m depth to investigate seasonal variability, transformation, and degradation of amino acids and their sources in Bay of Bengal coastal waters. Various biogeochemical parameters were collected along with particulate amino acids to understand their impact on amino acid composition distribution and variability. Particulate amino acids vary from 0.21 to 7.1µ mol/L, and the highest concentrations are during the non-monsoon period (4.3 µ mol/L) than the monsoon period (2.5 µ mol/L). During the non-monsoon period, amino acids show a significant linear positive relation with phytoplankton biomass (Chl-a) and suspended matter in the monsoon period. The relations indicate that amino acids during the non-monsoon were contributed from the in situ production and during the monsoon from terrestrial inputs. The ratio of amino acids to hexosamines (AAs/HAs) and the ratios of glucosamine (Glc-NH2) to galactosamine (Gal-NH2) were on average 15.5 and 5.2 in non-monsoon and during monsoon; they were 3.3 and 2.1, indicating that during the monsoon period, the amino acids were more of aged and which could be from resuspension of soil or refractory organic matter, which could enter the coastal waters because of precipitation and in non-monsoon, the higher ratios suggesting that in situ biological processes contributed these amino acids. This study indicated that during the monsoon period, physical processes like precipitation, terrestrial runoff, sediment resuspension, and non-monsoon period biological processes like in situ production significantly contribute organic carbon to the coastal waters of the Bay of Bengal. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess long-term trends in Chl- a and SST in major fishing zones associated with fish production over an 18-year period (2002–2019) in the Gulf of Suez, Gulf of Aqaba, and the Red Sea proper. The study found that seasonal SST mean values ​​differed significantly (P < 0.01) when compared across seasons. Chl -a (mg/m3) exhibited variation between winter and summer (P = 0.01). Regarding regional variability, SST differed significantly between the three regions (P ≤ 0.01), while Chl -a showed no significant difference between the Gulf of Aqaba and the Red Sea, otherwise, chlorophyll exhibited significant variation between the gulfs of Suez and Aqaba (P < 0.01) and the Gulf of Suez and the Red Sea (P < 0.01). The current study found that the total catch varied seasonally in the studied regions from 2010 to 2015. Seasonal (2010–2015) and annual (2002–2019) time series analyses of fish production variance indicated significant zonal production trends. On the other hand, annual trends showed a gradual decline in the total catch from 2002 to 2015, followed by an increased trend until 2019. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in urbanization and anthropogenic activity in the lake catchment, nutrients like phosphorus and nitrogen are abundant in urban sewage, leading to lake eutrophication and promoting algal blooms. Chlorophyll‐a (Chl‐a) is an important indicator of algal blooms; thus, predicting concentration is vital for lake restoration and management. Literature reveals that most of the previous studies of Chl‐a prediction required multiple variables and considerable computation. Thus, it is crucial to scientifically identify the key drivers of Chl‐a in Taihu Lake for the formulation of effective environmental management measures. In this study, a long‐term dataset (1991–2015) and Daniel trend test method were employed to analyse the trend of meteorological and water quality indicators in Taihu Lake in China. The redundancy of variables was eliminated by using the rank correlation method, combined with prior knowledge that provides a solid foundation for the analysis of the driving factors of Chl‐a. Four machine learning models, named Extreme Gradient Boosting (XGBoost), random forest (RF), support vector regression (SVR) and gradient boosting decision tree (GBDT), were employed to predict the concentration of Chl‐a in different regions of Taihu Lake. The result shows that the XGBoost model outperformed the other three models in terms of R2 (>0.90) and root mean square error (RMSE) (3.69–8.40). Finally, based on the predictions of the concentration of Chl‐a using the XGBoost model, the SHapley Additive exPlanation (SHAP) framework was utilized to analyse the key factors influencing Chl‐a concentration. Finding of this study suggests that the XGBoost model performed best and could be applied to the overall stretches of Taihu Lake and other lakes in China; model prediction will be helpful in providing a zoning decision‐making approach for the rational protection of the lake environment and the prevention of algal bloom hazards. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to "re-suspend" and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as "self-digging". The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and "re-suspension", which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC "tails" appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of "self-digging" induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the "self-digging" problem to a certain extent and ensure the safety of OWFs. [ABSTRACT FROM AUTHOR]

Geographically dissimilar aquatic ecosystems from similar origin promote our comprehensive understanding of the role of abiotic factors in the distribution of biodiversity from one extreme, and they provide outstanding examples of convergent evolution in biological diversity on the other extreme. A new approach to monitoring and management of fisheries and marine ecosystems is required. Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) concentration are both crucial environmental variables that have significant impacts on marine ecosystems, particularly for fishes and overall aquatic life. These factors play important roles in shaping the health, distribution, behavior, and productivity of marine organisms. In the current study, Modis-aqua daily SST and Chl-a derived data from 2002 to 2022 were loaded into MATLAB, analyzed, and compared between the Arabian Gulf and Red Sea (Egyptian territory). Additionally, the catch of the family Lutjanidae (snapper fish) was recorded in 2002 to determine the distribution of napper fish species and compare production (based on data available from 2002 to 2022) in relation to SST and Chl-a variabilities. According to the current study, Chl-a in the Arabian Gulf showed narrow fluctuations during the winter and spring months, but higher than the Red Sea, and had a significant increase in sea surface temperature (SST) throughout the summer compared to the Red Sea. Spatial and temporal interaction between the means of Chl-a and SST was significantly different (P < 0.01). On the other hand, the results showed that different thermal stresses were experienced by the Arabian Gulf and the Red Sea, as shown by the total positive SST anomaly and by events occurring in different trends. Both the total catch of lutjanid snappers and the species identified by DNA barcoding varied significantly between the two geographical regions. The current work suggests using large-scale, fisheries-independent data to map the main gradients in marine fish community and species abundance and forecast their future distribution in connection to climate change.

The dynamics of physico-chemical, nutrient, and chlorophyll-a variables were studied in the bloom and non-bloom locations along the off-Gujarat coastal waters to understand the variability in biogeochemistry using multivariate analytical tests. The dissolved oxygen was significantly lower in the bloom stations (3.89 ± 0.44 mgL−1) than in the non-bloom stations (5.50 ± 0.70 mg L−1), due to the biological degradation of organic matter in addition to anaerobic microbial respiration. Nutrients (PO4 and NO3) and Chl-a concentrations were recorded higher in the bloom locations at 0.83 ± 0.21 µmol L−1, 4.47 ± 0.69 µmol L−1, 4.14 ± 1.49 mg m−3, respectively. PO4 and NO3 have shown a significantly higher positive correlation of r = 0.73 and r = 0.69 with Chl-a for bloom data than the non-bloom data. The percentage variance contributed by PC1 and PC2 for both bloom and non-bloom locations were estimated at 52.33%. The variable PO4 explains the highest 24.19% variability in PC1, followed by Chl-a (19.89%). The PO4 triggers the bloom formation and also correlates to the higher concentrations of Chl-a in the bloom locations. The bloom concentration ranges from 9553 to 12,235 trichomes L−1. The bloom intensity has shown a significant positive correlation with Chl-a (r = 0.77), NO3 (r = 0.56), and PO4 (r = 0.30), but a negative correlation was noticed with DO (r = − 0.63) and pH (r = − 0.49). The study also initiates a way forward research investigation on ocean-color technologies to identify and monitor blooms and climate change–driven factors for bloom formation. The occurrence of bloom and its influence on fishery resources and other marine biotas will open many research windows in marine fisheries, oceanography, remote sensing, marine biology, and trophodynamics. [ABSTRACT FROM AUTHOR]

Chlorophyll–a (Chl–a) concentration is an indicator of phytoplankton pigment, which is associated with the health of marine ecosystems. A commonly used method for the determination of Chl–a is satellite remote sensing. However, due to cloud cover, sun glint and other issues, remote sensing data for Chl–a are always missing in large areas. We reconstructed the Chl–a data from MODIS and VIIRS in the Arabian Sea within the geographical range of 12–28° N and 56–76° E from 2020 to 2021 by combining the Data Interpolating Convolutional Auto–Encoder (DINCAE) and the Bayesian Maximum Entropy (BME) methods, which we named the DINCAE–BME framework. The hold–out validation method was used to assess the DINCAE–BME method's performance. The root–mean–square–error (RMSE) and the mean–absolute–error (MAE) values for the hold–out cross–validation result obtained by the DINCAE–BME were 1.8824 mg m−3 and 0.4682 mg m−3, respectively; compared with in situ Chl–a data, the RMSE and MAE values for the DINCAE–BME–generated Chl–a product were 0.6196 mg m−3 and 0.3461 mg m−3, respectively. Moreover, DINCAE–BME exhibited better performance than the DINEOF and DINCAE methods. The spatial distribution of the Chl–a product showed that Chl–a values in the coastal region were the highest and the Chl–a values in the deep–sea regions were stable, while the Chl–a values in February and March were higher than in other months. Lastly, this study demonstrated the feasibility of combining the BME method and DINCAE. [ABSTRACT FROM AUTHOR]

Increases in algal growth have been reported in rivers, reservoirs, and other water resources worldwide, including Korea. Algal overgrowth can result in algal bloom, which has several negative impacts, such as ecosystem degradation and economic losses. Mitigation measures employed in Korea include an algal warning system and survey-based water quality forecast systems. However, these methods are time-consuming and require sample collection from the site. On the other hand, remote sensing techniques that use chlorophyll a are unable to distinguish between different types of algal species. In this paper, we aimed to identify a classification technique based on remote sensing methods that can be used to distinguish between blue-green algae and green algae. We acquired and prepared an algal culture solution and used a hyperspectral sensor to obtain an algae spectrum. Thereafter, we measured the absorption and emission spectra of blue-green and green algae and distinguished them using the instantaneous slope change of the spectrum. The absorption spectra for green algae showed two peaks at 417–437 nm and 661–673 nm, whereas those of blue-green algae showed three peaks at 449–529 nm, 433–437 nm, and 669–677 nm. The results of this study could form a basis for developing mitigation measures for algal overgrowth. [ABSTRACT FROM AUTHOR]

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to “re-suspend” and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as “self-digging”. The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and “re-suspension”, which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC “tails” appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of “self-digging” induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the “self-digging” problem to a certain extent and ensure the safety of OWFs.

To understand the lake status and reasons of eutrophication at Lake Erhai in recent years, water quality, including water temperature (T), pH, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP) and chlorophyll-a (Chl-a) from 2016 to 2020 was monitored and analyzed. The results showed no obvious thermocline in the vertical direction at Lake Erhai, while Chl-a demonstrated obvious spatiotemporal distribution characteristics in Lake Erhai. Chl-a concentrations increased to a maximum in summer in August with the low TN:TP value, leading to algal blooms, most notably in the southern lakes. Low pH and DO appeared due to the thermocline of Erhai Lake (August 2016). A large area of algae distribution due to the increase of total phosphorus appeared in the northern lake area of Lake Erhai in December 2016, with a tendency of mesotrophic to light eutrophic in summer by the nutritional evaluation of Lake Erhai, especially in the central lake area and the northern lake area. Pearson's correlation coefficient and principal component analysis showed a significant positive correlation between Chl-a and T (r = 0.34, p ≤ 0.01) and TP (r = 0.31 p ≤ 0.01) in the mesotrophic Lake Erhai, indicating that TP content was one of the triggering factors for the algal blooming. Based on the spatiotemporal changes in water quality parameters and their relationship with eutrophication, scientific agencies should implement management strategies to protect Lake Erhai, supplemental to the costly engineering measurements. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) frequently threaten the health of marine environments and ecosystems in the adjacent waters of the Yangtze River Estuary (YRE) in summer. To better understand the spatiotemporal distribution, variation characteristics, and major influencing factors of algal bloom-prone zones in the adjacent waters of the YRE in summer, the present study used Geostationary Ocean Color Imager (GOCI) chlorophyll-a (Chl-a) data from 2011 to 2020. The Chl-a concentration threshold for summer algal blooms in the adjacent waters of the YRE was determined to be 16 mg/m3, based on which the monthly and interannual distribution characteristics of algal blooms were further clarified, and the influence of runoff and sea surface temperature (SST) on algal blooms was analyzed. The results showed that the area of the algal bloom-prone zones was the largest in July, at 1337 km2, which was approximately 2.8-fold greater than that in June (474 km2) and 1.3-fold greater than that in August (1028 km2); the algal bloom-prone zones were mainly distributed in the eastern sea area of the YRE, and the spatial distribution of the algal blooms showed significant interannual variation. In 2020, the distribution range of the algal bloom-prone zones was the largest, and the maximum probability was >20%. Runoff had a significant positive effect on the algal bloom index (BI) (r = 0.89, p < 0.05), the correlation between BI and SST was weak (r = 0.50, p = 0.14), and the algal bloom probability had a significant negative correlation with SST (r = −0.71, p < 0.05), implying that above a certain range of temperatures, algal growth rates may decrease with increasing temperature. The research results can provide a scientific basis for the early prevention, control, and emergency response of disasters, which is conducive to the sustainable and healthy development of marine resources and has significant scientific and management significance. • Chlorophyll-a concentration threshold method can be used to identify algal blooms. • The area of algal bloom-prone zones in the YRE is the largest in July in summer. • Spatial distribution of algal blooms shows significant interannual variability. • Yangtze River runoff has a significant positive effect on the bloom intensity. • Temperature has a significant negative effect on the algal bloom probability. [ABSTRACT FROM AUTHOR]

The marine environment is a crucial component of global biogeochemical cycles. Recent BGC-Argo observations provide new opportunities to study the profiles of biogeochemical parameters. The study analyzed the diurnal variations of temperature, salinity, chlorophyll-a and dissolved oxygen using a high-frequency (∼5 h) cycle BGC-Argo float in the Bay of Bengal. The hydrography showed the existence of a strong barrier layer with a thickness of around 30 m, with fresh water on top and an inversion layer within it. Analysis showed that the Mixed Layer Depth (MLD) was dominated by diffuse convection, while the Isothermal Layer Depth (ILD) exhibited salt-fingering regimes. In the upper layer (0–60 m), temperature showed significant variation on a daily scale; however, notable changes were not observed for salinity. Additionally, temperature and chlorophyll-a were found to be strongly linked to solar insolation. The mean chlorophyll-a in the upper layer increased from 0600 h and peaked around 1800 h local time. However, surface chlorophyll-a increased only from 1100 h to 1800 h. It is suggested that this difference between surface and mean chlorophyll-a during high availability of sunlight was due to the process of photoacclimation. The dissolved oxygen cycle closely followed the variability of biomass production. The similarity between dissolved oxygen and the difference between the surface and mean chlorophyll-a further indicated photoacclimation variations on a diurnal scale. The Sverdrup model was used to indicate luminosity and an accumulation time of 14 h was used to show a strong correlation with diel chlorophyll-a variation. The work highlights the importance of having high-frequency BGC-Argo floats with finer vertical resolution and the need for time-series observations of biological parameters in the Bay of Bengal. [ABSTRACT FROM AUTHOR]

To assess the anthropogenic effect on biodiversity, it is essential to understand the global diversity distribution of the major groups at the base of the food chain, ideally before global warming initiation (1850 Common Era CE). Since organisms in the plankton are highly interconnected and carbonate synthesizing species have a good preservation state in the Atlantic Ocean, the diversity distribution pattern of planktonic foraminifera from 1741 core-top surface sediment samples (expanded ForCenS database) provides a case study to comprehend centennial to decadal time-averaged diversity patterns at pre-1970 CE times, the tempo of the substantial increase in tropospheric warming. In this work, it is hypothesized and tested for the first time, that the large-scale diversity patterns of foraminifera communities are determined by sea surface temperature (SST, representing energy), Chl-a (a surrogate for photosynthetic biomass), and ocean kinetic energy (as EKE). Alpha diversity was estimated using species richness (S), Shannon Wiener index (H), and Simpson evenness (E), and mapped using geostatistical approaches. The three indices are significantly related to SST, Chl-a, and EKE (71-88% of the deviance in the generalized additive mixed model, including a spatial component). Beta diversity was studied through species turnover using gradient forest analysis (59% of the variation). The primary community thresholds of foraminifera species turnover were associated with 5-10 °C and 22-28 °C SST, 0.05-0.15 mg m-3 Chl-a, and 1.2-2.0 cm2 s-2 log10 EKE energy, respectively. Six of the most important foraminifera species identified for the environmental thresholds of beta diversity are also fundamental in transfer functions, further reinforcing the approaches used. The geographic location of the transition between the four main biogeographic zones was redefined based on the results of beta diversity analysis and incorporating the new datasets, identifying the major marine latitudinal gradients, the most important upwelling areas (Benguela Current, Canary Current), the Equatorial divergence, and the subtropical fronts (Gulf Stream-North Atlantic Drift path in the north, and the South Atlantic current in the south). In conclusion, we provide statistical proof that energy (SST), food supply (Chl-a), and currents (EKE) are the main environmental drivers shaping planktonic foraminifera diversity in the Atlantic ocean and define the associated thresholds for species change on those variables.

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Erhai Lake is the second largest freshwater lake in Yunnan Province but suffers from the deterioration of water quality and agricultural non-point source pollution (ANPSP). However, little is known about the influence of ANPSP on the water quality of Erhai Lake. The export coefficient model (ECM) was used to obtain the total nitrogen (TN) and total phosphorus (TP) loads from ANPSP in Erhai Lake Basin (ELB). The trophic status of Erhai Lake as influenced by such sources of nutrient input was also been assessed. Results indicated that the TN and TP loads in ELB increased from 1985 to 2005 due to sustainable agricultural development; thereafter, the TN and TP loads decreased from 2005 to 2020, indicating that agricultural pollution prevention improved in ELB. The northern part of ELB had higher pollution intensity than the southern part and the central part, indicating that the ecosystem in the northern part of ELB appeared to be vulnerable. Driving force analysis showed that cattle breeding was the main reason for the exported TN loads in most watersheds, and intensive agricultural planting was the major contributor to TP loads. The mean annual Chl-a concentration had a strong correlation with the TN and TP loads exported from north of ELB, and this finding suggested that ANPSP could lead to eutrophication. The results of this study demonstrate the impacts of agricultural activities on water quality at the watershed scale and provide a scientific foundation for lake management decision-making. [ABSTRACT FROM AUTHOR]

The Hainan Island coastal upwelling (HIU hereafter) off the eastern Hainan Island (EHI) is an important phenomenon in the northern South China Sea. Though this phenomenon has been extensively studied, the fast responses of its inner oceanic processes to typhoon passage remain unclear. Based on satellite data and a validated three-dimensional numerical model, the HIU response to the passage of Typhoon Rammasun (2014) are explored in this study. Impacted by Rammasun, the climbing of deep water off the EHI was depressed and the bottom cold water moved seaward for at least 10 km. Though the HIU was weakened, a cooling event and Chl-a bloom in the HIU were formed after the passage of Rammasun. Diagnostic analyses revealed that both advection and diffusion terms contributed to controlling the local rate of temperate change during the typhoon passage and the advection terms showed significant internal wave signals along the shelf break off southeastern Hainan Island. At the same time, strong near-inertial waves (NIWs) with 0.705 cpd were triggered in the HIU. The NIWs lasted for about two weeks and caused strong vertical mixing in the HIU after Rammasun. Overall, typhoon wind and NIWs combined to dominate temperature variation in the HIU during the typhoon period.

Reconstructing chlorophyll-a (Chl-a) vertical profile is a promising approach for investigating the internal structure of marine ecosystem. Given that the process of profile classification in current process-oriented profile inversion methods are either too subjective or too complex, a novel Chl-a profile reconstruction method was proposed incorporating both a novel binary tree profile classification model and a profile inversion model in the Mediterranean Sea. The binary tree profile classification model was established based on a priori knowledge provided by clustering Chl-a profiles measured by BGC-Argo floats performed by the profile classification model (PCM), an advanced unsupervised machine learning clustering method. The profile inversion model contains the relationships between the shape-dependent parameters of the nonuniform Chl-a profile and the corresponding Chl-a surface concentration derived from satellite observations. According to quantitative evaluation, the proposed profile classification model reached an overall accuracy of 89%, and the mean absolute percent deviation (MAPD) of the proposed profile inversion model ranged from 12%–37% under different shape-dependent parameters. By generating monthly three dimensions Chl-a concentration from 2011 to 2018, the proposed process-oriented method exhibits great application potential in investigating the spatial and temporal characteristics of Chl-a profiles and even the water column total biomass throughout the Mediterranean Sea.

This study documents a novel method for tracking spatio-temporal variability of productivity-related elements, including particulate organic carbon (POC), particular inorganic carbon (PIC), chlorophyll-a (Chl-a), dissolved nitrate, total phosphate, and dissolved phosphate, in the Sundarbans coastal aquatic system over a period of twenty years (2002–2021). Machine learning (ML) algorithms were employed to compute all the parameters from optical remote sensing data. Input data were obtained from the MODIS multispectral imageries bands satellite data sets and data extraction and analysis were performed using SVM regression models. Moving average study of POC and Chl-a concentration along the coastal zone revealed shallow deltaic coagulation, eutrophication, microbial decomposition, and lysis-caused variation and deterioration. However, high ambient temperatures and organic waste decomposition increase dissolved phosphate in inland mangrove creeks before the monsoon. Elevated nutrient levels lead to a reduction in Chl-a and particulate organic carbon is highly correlated with Chl-a to the extent of 0.79 Especially in summer time nutrient loading from agriculture and urban runoff cause harmful algal blooms that destroy aquatic life and degrade ecosystem functions. Even though collecting samples from the field on a seasonal basis for monitoring water quality and aquatic productivity is the ideal approach, it is time-consuming and also uneconomical, given the remoteness of the expansive mangrove forest. This study demonstrates efficacy of cost-effective methods like utilizing satellite data and adopting ML techniques for continuous monitoring of Sundarbans deltaic coast for its water quality and aquatic health. [Display omitted] • Long-term field and remote Sensing datasets have been analysed to evaluate particle inorganic carbon, particulate organic carbon, and chlorophyll concentration in the study area. • Remote sensing data from the MODIS Aqua satellite during 2002–2021 along with in-situ data from eight locations along the coastal zone of Sundarbans used for aquatic productivity mapping. • ML-based empirical models have been examined to ascertain how these variables might change over a 20-year period for supporting long-term prediction. [ABSTRACT FROM AUTHOR]

Increases in algal growth have been reported in rivers, reservoirs, and other water resources worldwide, including Korea. Algal overgrowth can result in algal bloom, which has several negative impacts, such as ecosystem degradation and economic losses. Mitigation measures employed in Korea include an algal warning system and survey-based water quality forecast systems. However, these methods are time-consuming and require sample collection from the site. On the other hand, remote sensing techniques that use chlorophyll a are unable to distinguish between different types of algal species. In this paper, we aimed to identify a classification technique based on remote sensing methods that can be used to distinguish between blue-green algae and green algae. We acquired and prepared an algal culture solution and used a hyperspectral sensor to obtain an algae spectrum. Thereafter, we measured the absorption and emission spectra of blue-green and green algae and distinguished them using the instantaneous slope change of the spectrum. The absorption spectra for green algae showed two peaks at 417–437 nm and 661–673 nm, whereas those of blue-green algae showed three peaks at 449–529 nm, 433–437 nm, and 669–677 nm. The results of this study could form a basis for developing mitigation measures for algal overgrowth.

Chlorophyll–a (Chl–a) concentration is an indicator of phytoplankton pigment, which is associated with the health of marine ecosystems. A commonly used method for the determination of Chl–a is satellite remote sensing. However, due to cloud cover, sun glint and other issues, remote sensing data for Chl–a are always missing in large areas. We reconstructed the Chl–a data from MODIS and VIIRS in the Arabian Sea within the geographical range of 12–28° N and 56–76° E from 2020 to 2021 by combining the Data Interpolating Convolutional Auto–Encoder (DINCAE) and the Bayesian Maximum Entropy (BME) methods, which we named the DINCAE–BME framework. The hold–out validation method was used to assess the DINCAE–BME method’s performance. The root–mean–square–error (RMSE) and the mean–absolute–error (MAE) values for the hold–out cross–validation result obtained by the DINCAE–BME were 1.8824 mg m−3 and 0.4682 mg m−3, respectively; compared with in situ Chl–a data, the RMSE and MAE values for the DINCAE–BME–generated Chl–a product were 0.6196 mg m−3 and 0.3461 mg m−3, respectively. Moreover, DINCAE–BME exhibited better performance than the DINEOF and DINCAE methods. The spatial distribution of the Chl–a product showed that Chl–a values in the coastal region were the highest and the Chl–a values in the deep–sea regions were stable, while the Chl–a values in February and March were higher than in other months. Lastly, this study demonstrated the feasibility of combining the BME method and DINCAE.

Chlorophyll is a significant biochemical component and can be determined in the laboratory (destructive) and using various chlorophyll content measuring devices (non-destructive). In this study, destructive and non-destructive methods were used to determine chlorophyll content and compared in peanut (Arachis hypogaea cv. NC-7) grown under different soil texture and saline water applications. The experiment was carried out in a complete randomized block design in pots using two soil textures (clay-loam and sandy) and three irrigation water salinity (0.7, 2.1 and 3.3 dS m-1). While the chlorophyll contents (Chl-a, Chlb, Chl-a+b, Chl-a/b) were determined with the acetone extraction procedure, which is classified as destructive methods under laboratory conditions, the Chlorophyll Content Index (CCI) values were measured with the hand-held chlorophyll meter device (Apogee CCM-200), which is a non-destructive method. While irrigation water salinity decreased all types of chlorophyll contents (Chl-a, Chl-b, Chl-a+b) (mg cm-2), it did not cause a statistical difference in Chl-a/b. Linear and polynomial models were fitted between the different chlorophyll contents and the CCI values under different soil textures and saline water levels. Model performances were slightly better with the polynomial model compared to the linear model in all experimental treatments. Since the difference between model performances is small, it is recommended to use the linear model due to its ease of use. In addition, the total chlorophyll content can be safely estimated under saline conditions by using portable chlorophyll meters. [ABSTRACT FROM AUTHOR]

The Western Waters of Indonesian (WWI) present a diverse interaction of ocean-atmosphere dynamics. One of them represents the event of Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), and upwelling. The objective of this study is to determine the dynamics of chlorophyll-a concentration (Chl–a), especially during IOD and ENSO. Also, this study is aimed to examine the temporal and spatial distribution of the upwelling area from 2000 to 2017. The data utilized consisted of Chl–a, wind stress, Sea Level Anomaly (SLA), and Sea Surface Temperature (SST). The technique used to determine the upwelling area was by examining the maximum conditions of Chl–a, the low temperature of SST, and SLA. The results showed the sea surface temperature had a relationship with the concentration of Chl–a. It was obtained if the Directional Movement Index (DMI) and N3.4 (Niño 3.4 Index) moved stably (not too fluctuation) resulting in high concentrations of Chl–a. High standard deviations of SST are recognized around the Sunda Strait (June – October). When the standard deviation of SST is high, there is also a tendency for high Chl–a concentrations, while the results of empirical calculations show that large areas of upwelling occurred in January and September respectively at 12,447.72 km2 and 8,146.20 km2. Based on the results of the analysis, it can be concluded that the upwelling does not only occur at the coastal area of Western Sumatra (coastal upwelling), but it also occurs in the eastern territorial waters of the Indian Ocean. In addition, the upwelling area has the same pattern as the Chl–a concentration in January - October.

Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monitoring and data collection were conducted during a cyanobacterial bloom period. Finally, 155 sets of valid data were obtained. The imaging data were obtained using a multispectral UAV, water samples were collected from the lake, and the chl-a concentration was obtained in the laboratory. Then, the images were preprocessed to extract the information from different wavebands. The univariate regression of each vegetation index and the regression using band information were used for comparative analysis. Four machine learning algorithms were used to build the model: support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and convolutional neural network (CNN). The results showed that the effect of estimating the chl-a concentration via multiple regression using vegetation indices was generally better than that via regression with a single vegetation index and original band information. The CNN model obtained the best results (R2 = 0.7917, RMSE = 8.7660, and MRE = 0.2461). This study showed the reliability of using multiple regression based on vegetation indices to estimate the chl-a of surface water.

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This study aims to explore the variation of Chlorophyll-a (Chl-a), particulate organic carbon (POC) and sea surface temperature (SST) before (pre-cyclone) and after (post-cyclone) the cyclone Amphan in the Bay of Bengal (BoB). Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite level-3 data were used to assess the variability of the mentioned parameters. Chl-a concentration was observed to be significantly (t = −3.16, df ≈ 18.03, p = 0.005) high (peak 2.30 mg/m3) during the post-cyclone period compared to the pre-cyclone (0.19 mg/m3). Similarly, POC concentration was significantly (t = 3.41, df ≈ 18.06, p = 0.003) high (peak 464 mg/m3) during the post-cyclone compared to the pre-cyclone (59.40 mg/m3). Comparatively, high SST was observed during the pre-cyclone period and decreases drastically with a significant difference (t = 14, df = 33, p = 1.951e-15) after the post-cyclone period. The results indicated an increase in Chl-a (502%) and POC (240%) with a positive anomaly of 0.55 and 94.11 mg/m3, but a decrease in SST (8.52%) with a negative anomaly of 2.7°C in the BoB. [ABSTRACT FROM AUTHOR]

To understand the lake status and reasons of eutrophication at Lake Erhai in recent years, water quality, including water temperature (T), pH, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP) and chlorophyll-a (Chl-a) from 2016 to 2020 was monitored and analyzed. The results showed no obvious thermocline in the vertical direction at Lake Erhai, while Chl-a demonstrated obvious spatiotemporal distribution characteristics in Lake Erhai. Chl-a concentrations increased to a maximum in summer in August with the low TN:TP value, leading to algal blooms, most notably in the southern lakes. Low pH and DO appeared due to the thermocline of Erhai Lake (August 2016). A large area of algae distribution due to the increase of total phosphorus appeared in the northern lake area of Lake Erhai in December 2016, with a tendency of mesotrophic to light eutrophic in summer by the nutritional evaluation of Lake Erhai, especially in the central lake area and the northern lake area. Pearson’s correlation coefficient and principal component analysis showed a significant positive correlation between Chl-a and T (r = 0.34, p ≤ 0.01) and TP (r = 0.31 p ≤ 0.01) in the mesotrophic Lake Erhai, indicating that TP content was one of the triggering factors for the algal blooming. Based on the spatiotemporal changes in water quality parameters and their relationship with eutrophication, scientific agencies should implement management strategies to protect Lake Erhai, supplemental to the costly engineering measurements.

The analysis of satellite images allowed us to define the variation of marine chlorophyll (Chl-a) combined with Sea Surface Temperature (SST) variation along the Algerian coast. The database was downloaded daily from the MODIS Aqua satellite between 2008 and 2018 and classified by month. Processing of the satellite images showed significant thermal variations between the West, Centre, and East of the coast, accompanied by local fluctuations in chlorophyll variation. The correlative study between the Spatio-temporal variations of SST and Chl-a indicates a strong negative correlation at the temporal scale (r=-0.92) and a weak negative correlation at the spatial scale (r=-0.39). These results indicated that Chl-a was influenced by seasonal changes in SST and marine hydrodynamics and local anthropogenic inputs. Furthermore, the principal component analysis (PCA) between the Spatiotemporal variations of SST and Chl-a allowed us to classify the Algerian coast into two sub-basins: the southwestern (S.E.) Alboran sub-basin and the Algerian sub-basin, as well as a central transitional zone between the two sub-basins. The study showed that satellite images could determine the Spatio-temporal relationship between SST and Chl-a variations and understand marine ecosystems' functioning. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012−2021) in 217 large lakes (> 50 km2) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (R rs (λ)) products were reliable over 28 of China's examined lakes (N = 340, bias = −12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1–300 μg L−1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 μg L−1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity. • The performance of VIIRS-derived R rs was evaluated in clear and turbid lakes. • A robust deep-learning algorithm was developed for Chl-a retrievals in lakes. • The algorithm showed capability of integrating VIIRS and OLCI for Chl-a retrievals. • Chl-a variations in 217 large Chinese lakes were mapped from VIIRS images. • Chl-a increased in East China Plain but decreased in the Tibetan Plateau since 2012. [ABSTRACT FROM AUTHOR]

Better understanding of the temporal-spatial distribution of chlorophyll-a concentration (Chl-a) is crucial in controlling harmful water blooms. In this study, the dynamical change of Chl-a over the Bohai Sea and Yellow Sea from 2003-2017 were analyzed by using the MODIS/Aqua satellite data, and the effects of sea surface temperature (SST), wind and wave were investigated. The typical distribution modes of long-term surface Chl-a were extracted by using the Self-organizing Mapping (SOM), neural network model. The results showed distinct seasonal variations of the Chl-a along with a gradual increase in the study period. The total Chl-a of the whole area reached the lowest value of 2.41mg/m3 in July, and the highest value 3.43mg/m3 in April; though in Laizhou Bay, the Chl-a concentration was significantly higher than other regions and the value reached at the peak in September. The spatial distribution showed that Chl-a decreased from inshore to offshore. Meanwhile, from clear mode to low, medium, and high concentration modes, the Chl-a gradually increased in coverage and concentration, and modes extracted by the SOM neural network have effectively elucidated the trend of Chl-a in spatial, seasonal, and interannual variability. The Generalized Additive Model (GAM) was used to evaluate the effect of SST, wind, and wave on the changing patterns of Chl-a. It was found that there is a significant nonlinear correlation between Chl-a and SST, wind speed, mean wave direction and significant height of the wave. These influencing factors accounted for 47.9% of the change of Chl-a, which had significant effects on Chl-a change. Compared with wind speed, mean wave direction and significant height of wave, SST can better explain the change of Chl-a. Besides, wind direction and increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in the Bohai Sea and Yellow Sea.

In this study, we investigate spatial and temporal variabilities of particulate organic carbon (POC) in the East China Sea (ECS) and explore the dominant influencing factors for its three subregions—coastal, continental shelf and open sea—by analyzing satellite derived POC from 2001 to 2011 after calibration against in situ samplings. The analysis reveals that the largest POC occurs in spring at the coastal region with a value of 297.5 mg m−3, and the smallest in summer at the open sea region with a value of 108.9 mg m−3. POC in the coastal region is dominated by biological activity related to river discharge of nutrients and organic matter, and is regulated by sediment discharge variation due to the Three Gorge Project and chemical fertilizer application along the Changjiang River watershed; the open sea region is dominated by water exchange with the Kuroshio current, which results in the highest sea surface temperature and the lowest chlorophyll and POC among the three subregions; POC in the continental shelf region is driven by the seasonal competition between the influence of the coastal ocean and the Kuroshio current through biological activity and water exchange. A method to estimate the organic matter reserve in the ECS was developed based on satellite data and in situ samplings. Organic carbon storage was estimated at 4.08 × 1013 g, which could be used as a baseline for future estimates. An improved estimation could be found with additional in situ data and with the use of a more sophisticated algorithm for satellite POC analysis.

The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

Chlorophyll-a (Chl-a) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl-a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl-a. Our study indicates that a global algorithm, on average, underestimates in-situ Chl-a by ~59%, although underestimation was only observed for waters with Chl-a > 0.5 mg m−3. In high Chl-a waters (>1 mg m−3), Chl-a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl-a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl-a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce.

The impoundment of the Three Gorges Dam (TGD) has altered downstream hydrological characteristics and sediment discharge, and it has caused ecological impacts, such as changes in chlorophyll-a (Chl-a) in estuaries and coastal oceans. To investigate the TGD's influence on Chl-a's interannual and seasonal variations in the Changjiang Estuary and the adjacent coastal East China Sea, a physical-biogeochemical model was developed with numerical experiments covering a decade, including TGD's pre-period (pre-TGD, 1999–2003) and post-period (post-TGD, 2004–2008). The modeling results demonstrate an annual increase in the regional average Chl-a from pre- to post-TGD, with the largest increase reaching up to 20.8% in spring in the nearshore region beyond the Changjiang mouth. The spatial variations in Chl-a are high, with the largest variation being observed around the 20–40 m isobaths. The increase in Chl-a is influenced by improved light and nutrient conditions, which is attributed to dam construction and fertilization by human activities. A decline in nitrogen-phosphorus fertilizer usage ratio along the Changjiang River watershed after the TGD's impoundment is another factor that influences the Chl-a's variation. The modeling results also show severe NO3 and PO4 imbalances with a larger N/P ratio during the post-TGD period, especially in regions with large Chl-a increases. Moreover, the greater increase in the usage of phosphorus fertilizer than nitrogen fertilizer influences Chl-a's variation and has potential promotion effects on the outbreak of harmful algal bloom events. [ABSTRACT FROM AUTHOR]

Krishnagiri Reservoir exhibits a hypereutrophic status and continuously receives external sediment and nutrient loads, in addition to its internal phosphorus loading, both affecting the reservoir water quality. Increased nutrient loading attributable to changing anthropogenic activities in the catchment area will further exacerbate the deteriorating trophic status. Temporal Satellite imageries can play a crucial role in the rapid assessment of the trophic status of the reservoir over a large spatial extent. The eutrophication status of freshwater systems is directly related to the chlorophyll‐a (Chl‐a) concentration, which represents a major trophic state indicator by reflecting green and absorbing violet‐blue and orange‐red light of the solar energy spectrum. The present study was undertaken to map seasonal Chl‐a concentration variations using Landsat 8 Operational Land Imager (OLI) images. Multiple regression equations developed using reflectance in the Green, Near Infrared, Shortwave Infrared 1 and 2 and Coastal bands (R2 =.635) were found to be the best fit of the model in mapping the Chl‐a concentration variations in Krishnagiri Reservoir. The derived regression model also can be used to determine the trophic state of the reservoir and can facilitate a more rapid assessment for developing management strategies for sustainable reservoir water quality management. [ABSTRACT FROM AUTHOR]