Chl-a Concentration Research Articles

Seasonal nutrients variation, eutrophication pattern, and Chlorophyll a response adjacent to Guangdong coastal water, China

Nutrients were the key biogenic elements for the primary production in coastal water, and the increase of nutrient concentration led to eutrophication and frequent occurrence of harmful algal blooms. However, the seasonal nutrients variation, eutrophication pattern, and Chlorophyll a (Chl-a) response adjacent to Guangdong coastal water were still scarcely. In this study, to clarity the seasonal nutrients variation, eutrophication pattern, and Chl-a, response adjacent to coastal water, the spatiotemporal dissolved inorganic nitrogen (DIN) and phosphorus (DIP) patterns and Chl-a were explored by field observation using 52 stations in the coastal waters of Guangdong Province during the dry (April and May), wet (July and August) and normal (October and November) seasons in 2020. The results showed that the variability of Chl-a, DIN and DIP were significantly different in seasons (P<0.01), and the mean concentrations of Chl-a, DIN and DIP were 11.97 ± 28.12 μg/L, 25.84 ± 35.72 μmol/L and 0.59 ± 0.71 μmol/L.Among them, the mean value of Chl-a increased significantly from 9.99 ± 9.84 μg/L in the dry season to 18.28 ± 38.07 μg/L in the wet season, and then decreased significantly to 7.65 ± 27.64 μg/L in the normal season.The mean DIN value decreased significantly from 30.68 ± 43.58 μmol/L in the dry season to 21.91 ± 35.45 μmol/L in the wet season, and then increased to 24.91 ± 26.12 μmol/L in the normal season. the mean DIP value decreased from 0.58 ± 0.73 μmol/L in the dry season to 0.48 ± 0.65 μmol/L in the wet season and then increased significantly to 0.70 ± 0.73μmol/L in the normal season. In addition, the DIN and DIP concentrations at most monitoring stations met the Grade II national seawater quality standards, and only a few monitoring stations fail to meet the Grade IV national seawater quality standard. The DIN/DIP ratios ranged from 2.05 to 259.47, with an average of 43.77 ± 41.01, far exceeding the Redfield ratio, indicating the presence of P limitation in the nearshore waters of Guangdong Province. Besides, the EI values in the coastal waters of Guangdong Province are higher at 0.00 and 82.51, with an average of 4.16 ± 10.90. DIN and DIP were significantly and positively correlated with COD in each season (P<0.05). Moreover, DIN/DIP showed significantly positive correlations with Chl-a in all seasons (P<0.01), indicating that high Chl-a concentrations could be sustained by the nutrients supply in marine ecosystems. Therefore, it is necessary to strengthen the integrated management of land and sea and effectively mitigate regional estuarine and coastal water eutrophication and harmful algal blooms.

Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China

Karst wetlands are widely distributed throughout the southwest China, and play an important role in enhancing carbon sequestration and improving water quality in karst areas. The internationally important karst wetland of Huixian is the largest karst wetland in China, but its water quality has continued to deteriorate as a result of human influences in recent years. Remote sensing technology has become an important approach to estimate water quality parameters (WQPs). However, the feasibility of combining multi-sensor remote sensing images with deep learning to estimate different WQPs in karst wetlands has not been demonstrated yet. To resolve this issue, this study constructed multiple retrieval models of WQPs (Chlorophyll-a (Chla), Phycocyanin (PC), Turbidity (Turb), Dissolved Oxygen (DO)) in karst wetlands using deep learning (Transformer and Mixture Density Network (MDN)) and optimized shallow machine learning (Random Forest (RF), XGBoost (XGB) and Gradient Boosting (GB)) based on multi-sensor images from satellite and UAV platforms. The performance of deep learning in the inversion of WQPs demonstrated to compare with shallow machine learning using multispectral and hyperspectral images. We further quantitatively evaluated the retrieval performance of UAV and satellite, multispectral and hyperspectral images, and presented predictive mapping of the gradient distribution of WQPs. Finally, this study adopted the SHapley Additive exPlanations (SHAP) to tackle the local and global interpretability of the input features contribution to the output of retrieval models. The results showed that (1) Transformer model presented a good prediction of PC and DO (R2 = 0.649 ∼ 0.844), XGB and GB models achieved the highest accuracy estimation of Chla and Turb (R2 = 0.75). (2) The estimation results of WQPs based on UAV platform (R2 = 0.419 ∼ 0.695) was higher than that of satellite-based images. The estimation accuracy of multispectral images (R2 = 0.338 ∼ 0.718) was slightly higher than that of Zhuhai-1 Orbita hyperspectral (OHS) images. The average accuracy of Turb estimated by UAV images (R2 = 0.565 ∼ 0.752) was higher than that of satellite-based images. OHS hyperspectral images had the best DO estimation (R2 = 0.314 ∼ 0.649). (3) This study found 32.66% and 23.01% of water area with the Chla and Turb concentrations exceeding 60 μg/L and 60 NTU, respectively, which revealed that the Huixian karst wetland has suffered serious water pollution. (4) The SHAP analysis reveals that near infra-red and red band are sensitive to predict Chla and DO, red and red-edge bands are sensitive to predict PC and Turb in the karst wetland.

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500–850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001–0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603–778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R2 = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R2 = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R2 = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R2 = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m3 compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.

The Seasonality of Eddy-Induced Chlorophyll-a Anomalies in the Kuroshio Extension System

The Kuroshio Extension (KE) System exhibits highly energetic mesoscale phenomena, but the impact of mesoscale eddies on marine ecosystems and biogeochemical cycling is not well understood. This study utilizes remote sensing and Argo floats to investigate how eddies modify surface and subsurface chlorophyll-a (Chl-a) concentrations. On average, cyclones (anticyclones) induce positive (negative) surface Chl-a anomalies, particularly in winter. This occurs because cyclones (anticyclones) lift (deepen) isopycnals and nitrate into (out of) the euphotic zone, stimulating (depressing) the growth of phytoplankton. Consequently, cyclones (anticyclones) result in greater (smaller) subsurface Chl-a maximum (SCM), depth-integrated Chl-a, and depth-integrated nitrate. The positive (negative) surface Chl-a anomalies induced by cyclones (anticyclones) are mainly located near (north of) the main axis of the KE. The second and third mode represent monopole Chl-a patterns within eddy centers corresponding to either positive or negative anomalies, depending on the sign of the principal component. Chl-a concentrations in cyclones (anticyclones) above the SCM layer are higher (lower) than the edge values, while those below are lower (higher), regardless of winter variations. The vertical distributions and displacements of Chl-a and SCM depth are associated with eddy pumping. In terms of frequency, negative (positive) Chl-a anomalies account for approximately 26% (18%) of the total cyclones (anticyclones) across all four seasons. The opposite phase suggests that nutrient supply resulting from stratification differences under convective mixing may contribute to negative (positive) Chl-a anomalies in cyclone (anticyclone) cores. Additionally, the opposite phase can also be attributed to eddy stirring, trapping high and low Chl-a, and/or eddy Ekman pumping. Based on OFES outputs, the seasonal variation of nitrate from winter to summer primarily depends on the effect of vertical mixing, indicating that convective mixing processes contribute to an increase (decrease) in nutrients during winter (summer) over the KE.

Seasonal and spatial variability in bio-optical properties of the Persian Gulf: Implications for ocean color remote sensing

In this study, the absorption coefficients of phytoplankton community (aPh(λ)), Colored Dissolved Organic Matter (CDOM) [aCDOM(λ)], and Non-Algal Particles (NAP) [aNap(λ)] were analyzed in the northern Persian Gulf, a Mediterranean-type semi-enclosed marginal sea, through seven cruise expeditions conducted from May 2018 to February 2022. The highest and lowest aPh(443) values were observed in the shallow area (depth ≤20 m) during the wet season (November–April) (0.043 ± 0.004 m-1) and within the deep area (depth >20 m) through the dry season (July–September) (0.024 ± 0.003 m-1), respectively. The chlorophyll-specific absorption (a*Ph(λ)) values increased from the deep to shallow areas as well as the wet to dry seasons. A significant difference was further detected in a*Ph(λ) magnitude at the blue (443 nm) and red (675 nm) wavelengths, with the mean values of 0.0124/0.0223 m2 mg−1 and 0.0069/0.0135 m2 mg−1 in the wet/dry seasons, respectively. The mean values of aCDOM(443) in the shallow area during the wet season (0.10 ± 0.001 m-1) were significantly larger than those in the deep area during the dry season (0.06 ± 0.002 m-1). Similarly, aNAP(443) decreased from the shallow (0.056 ± 0.021 m-1) to deep areas (0.019 ± 0.011 m-1), and from the wet (0.035 ± 0.020 m-1) to dry (0.029 ± 0.015 m-1) seasons. The statistical analysis revealed that the differences in aCDOM(λ) and aNAP(λ) spectral distribution were not statistically significant for the duration of the wet and dry seasons, whereas significant differences were observed in the shallow and deep samples. During both wet and dry seasons, the magnitude of the blue-green absorption spectra was dominated by CDOM, but the red bands were associated with phytoplankton dominated samples. The ratio of aCDOM(λ)/a*Ph(λ) was utilized as an index for uncertainty assessment to estimate Chl-a concentrations by the National Aeronautics and Space Administration (NASA) standard Chl-a algorithms for the Moderate Resolution Imaging Spectrometer (MODIS; OC3M) and Sentinel-3 Ocean and Land Color Instrument (OLCI) (OLCI; OC4v6). The results additionally demonstrated that the satellite-derived Chl-a was overestimated due to higher aCDOM(λ) and underestimated owing to lower a*Ph(λ) at the low (<0.5 mg m−3) and high (>3 mg m−3) concentrations of Chl-a, respectively. The study findings established that the red bands should be considered for tuning or developing new Chl-a algorithms in the Persian Gulf.

Estimating the Chlorophyll-a in the Nha Trang Bay using Landsat-8 OLI data

The pigment chlorophyll-a (Chl-a) is used to evaluate aquatic ecological health. Using remote sensing techniques to estimate this pigment and spatially mapping its distribution becomes essential for measuring and assessing water quality in coastal areas. Nha Trang Bay is famous not only for its scenery but also for its biodiversity values, especially the existence of coral reefs. In this study, Landsat-8 OLI was taken on June 3, 2015, and field measurements of Chl-a at 13 survey sites from June 6-8, 2015, were used to build a local algorithm to monitor the spatio-temporal distribution of Chl-a content in Nha Trang Bay. The ACOLITE processor was employed for the atmospheric modification of Landsat-8 OLI images to obtain atmospherically corrected surface reflectance products. Four types of simple regression models, including linear, exponential, logarithmic, and power models, were used to describe the relationship between the in-situ measurement of Chl-a and remote sensing reflectance of Landsat-8 OLI data. The results of correlation analysis have shown a statistically significant relationship between field measurement of Chl-a concentration and the remote sensing reflectance ratio of Landsat-8 band 3 versus band 2 using an exponential model with a coefficient of determination of 0.88 (p < 0.001) and root mean square error (RMSE) of 0.40 mg/m3. This empirical relationship was applied to map the spatial distribution of Chl-a concentration from 27 cloudless Landsat-8 OLI images from 2013 to 2021. The spatio-temporal distribution of Chl-a indicated that the Chl-a concentration in the Bay has a low value (less than 1 mg/m3). However, this concentration becomes high in the coastal areas (greater than 1 mg/m3) and the Cai and Tac rivers (greater than 2 mg/m3). It is also noted that the content of Chl-a in the rainy season is fairly higher than in the dry season, by an average of 0.68 mg/m3 and 0.53 mg/m3, respectively. This research highlights that Landsat-8 OLI data can be an effective and valuable tool in monitoring Chl-a for coastal areas.

Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.

The Impact of Typhoon “In-Fa” (2021) on Temperature, Salinity, and Chlorophyll-a Concentration in the Upwelling Area of Northwestern East China Sea

Severe typhoon “In-Fa” passed through the northwestern region of East China Sea (ECS) in July 2021, affecting oceanic variables such as seawater temperature, salinity, and chlorophyll-a (Chl-a) concentration over the upwelling area. In this study, we analyzed the influence of the passage of typhoon “In-Fa” on the marine environment over the Upwelling Area off the Yangtze River Estuary (UAYRE) and the Upwelling Area of Zhoushan (UAZS). The results showed a significant decrease in sea surface temperature (SST) during the “In-Fa” typhoon, with maximum SST reductions of 2.98 °C in the UAYRE and 1.46 °C in the UAZS, which showed a “right bias” (indicating a greater cooling effect on the right side of the typhoon path compared to the left side). “In-Fa” influenced the temperature and salinity structure of the study areas and deepened the mixed layer depth (MLD). The MLD varied from the shallowest values of 2.02 m (18 July) to the deepest values of 19.4 m (26 July) in the UAYRE and from 2.43 m (18 July) to 16.79 m (25 July) in the UAZS. Furthermore, “In-Fa” led to an increase in sea surface Chl-a concentration, with a maximum Chl-a concentration enhancement of 285.58% (from 20 July to 28 July) in the UAYRE and 233.33% (from 20 July to 27 July) in the UAZS. The Ekman suction effect of “In-Fa” strengthened the upwelling, facilitating the transport of deep-sea nutrients to the upper ocean and providing favorable conditions for the growth of phytoplankton, thus benefiting the reproduction and survival of zooplankton, fish, and shrimp. This study contributes to understanding the mechanisms by which typhoons impact the ocean environment in upwelling area and provides valuable insights for the sustainable development of marine fisheries resources.

Bacteriological and eutrophication risk assessment of an Argentinian temperate shallow urban lagoon.

The urban lagoons receive strong anthropic pressures and the tensions often coexist between the "urban" and the "natural," and this consequently generates pollution and risks to the environment and human health. Our main objective was to study the water quality and toassess the bacteriological and eutrophication risks in the temperate shallow urban lagoon of the Parque Unzué (Gualeguaychú, Argentina), and to predict these risks in climate change scenarios considering the temperature and the rains as indicators. This urban shallow lagoon is in a recreative multiuse park (Gualeguaychú city), in the floodplain of the Gualeguaychú river in the Center-East of Argentina (Neotropical region). Twenty-seven sampling in 3 sampling points (n = 81) were carried out during 2015-2019, and physicochemical and bacteriological parameters were measured. Phosphorus, organic matter, chlorophyll-a (Chl-a), and total coliforms (TC) frequently had a moderate and very high contamination factor (CF), and the pollution load index (PLI) indicated contamination with a frequency of 74.1%. Moreover, the index (WQI) indicated poor (66.7%) and good (33.3%) water quality. Bacteriological and eutrophication predictive risk models showed an increase of the TC and the Chl-a concentration generating a current and future high risk of contamination of the lagoon under climate change scenarios that could generate ecosystemic function losses in the short-term.

Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network

A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of Rrs565 and Rrs520/Rrs443, Rrs565/Rrs490, Rrs520/Rrs490, Rrs490/Rrs443, and Rrs670/Rrs565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m3, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.

Mapping of tide-dominated Hooghly estuary water quality parameters using Sentinel-3 OLCI time-series data.

The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality.

The study goal was to determine spatiotemporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R2 = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light deficiency due to increased turbidity. In 49% of the models, precipitation intensified Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

Coupling of hydrography and bio-optical constituents in a shallow optically complex region using ten years of in-situ data

Using ten years of in-situ data from a shallow optically complex coastal ecosystem, this study unravels the coupling between bio-optical variability and seasonally changing hydrography. The data presented is novel, as these are long-term simultaneous measurements of bio-optical properties and constituents covering a wide range of hydrographic conditions at different sampling stations in riverine, estuarine, and coastal shelf. The data represent all three seasons experienced in the region, i.e., Indian Summer Monsoon (ISM), post-ISM, and pre-ISM. It allowed us to conduct a baseline study to characterize the bio-optical variability (absorption or scattering dominated) coupled with stratification for remote sensing applications on ecological health. Four groups of sampling stations, representing varying hydrography, were identified as riverine, maximum stratification, maximum chlorophyll-a (Chl-a) concentration, and estuarine/shelf. The maximum concentrations of total suspended matter (TSM) were always collocated with the strongest stratification site, while maximum Chl-a was always located downstream of maximum stratification. Surprisingly, colored dissolved organic matter (CDOM) values were negligible in the ISM season and increased manifold in other seasons. The source of CDOM (either land-derived or in situ generated) inferred from the spectral slope revealed the dominance of land-derived CDOM in the post-ISM season. Combining the entire data into the same groups showed that groups with maximum TSM and samples from estuarine locations were scattering-dominated. In contrast, the group with maximum Chl-a was absorption dominated. The analysis revealed that the optical behavior of the sampling locations being either absorption or scattering dominated depended on the state of stratification and contribution of Chl-a to the absorption budget.

Monitoring the spatial distribution of water quality of an urban stormwater pond using satellite images

Stormwater ponds play a critical role in managing urban runoff and mitigating the impact of pollutants on receiving water bodies. This study investigates the potential of remote sensing (RS) techniques in retrieving water level and key water quality parameters in urban stormwater wet ponds. Specifically, the focus is on water level, chlorophyll-a (Chl-a), total suspended solids (TSS), turbidity, and Secchi depth (SD). The results indicate that RS techniques can provide reliable estimates of water level, Chl-a, turbidity, SD, and TSS in urban stormwater wet ponds. Results revealed that Chl-a concentrations are higher near the inlets and within the forebay, with a significant decrease downstream of the forebay. TSS and total dissolved solids (TDS) concentrations follow a similar pattern to Chl-a, being higher near the inlet and within the forebay, but significantly lower downstream. Notably, the highest concentrations of TSS, TDS, and Chl-a are observed in July, likely due to heavy rainfall events during that period. Through principal component analysis (PCA) and correlation analysis, the study effectively captured the interrelationships among the water quality parameters in the stormwater pond. The spatial distribution of TSS and Chl-a shows a correlation. Furthermore, the study uncovers a relationship between water temperature and Chl-a concentration, with Chl-a decreasing as the temperature exceeds 20 °C and increasing when it drops below 20 °C. This relationship is influenced by sunlight availability, which affects Chl-a growth, as evidenced by variations in Chl-a concentration between the water column's bottom layers and surface. Overall, this research highlights the potential of utilizing RS techniques to retrieve water quality parameters, providing valuable information for water managers and decision-makers. The ability to generate maps of these parameters using reflected light measurements above stormwater wet ponds also contributes to effective water quality management strategies.

Plankton diversity and dynamics in the upper surface of the Indian sector of the Southern Ocean ecosystem and biogeochemical processes

In the Southern Ocean (SO) marine ecosystem, the interplay between CO2 sink and climate change is closely linked to the shifting plankton communities, leading to potentially wide-ranging biogeochemical effects. Within the Indian sector of the Southern Ocean (ISSO), the phytoplankton community was predominantly composed of Fragilariopsis spp., followed by Chaetoceros sp., while the zooplankton consisted mainly of Pleuromamma spp. and Oithona similis. Plankton diversity is collectively influenced by variables such as temperature, photosynthetically available radiation (PAR), and macronutrient availability. During the austral summer of 2019, significant latitudinal variations in phytoplankton were observed at major fronts in the region, including the Subtropical Front (STF), Sub Antarctic Front (SAF), Polar Front (PF), and South of Polar Front (SPF). At the STF, flagellates dominated (87%), while their presence decreased to 12% at the SPF, while diatoms progressively increased from the STF (<1%) to the SPF (87%) through the SAF and PF. Chlorophyll a (Chl-a) concentrations at the surface were highest in the PF (0.62 mg m−3), followed by the SPF (0.56 mg m−3), SAF (0.51 mg m−3), and STF (0.28 mg m−3). The integrated Chl-a values across the water column were higher at the SPF (21.49 mg m−2) than at the STF (14.73 mg m−2). The dominant diatom species at the surface along the fronts, except for STF, was Fragilariopsis spp. (∼5 × 105 Cells L−1), followed by Chaetoceros sp. (∼1 × 105 Cells L−1), and Coscinodiscus sp. (∼5 × 104 Cells L−1). Conversely, at the STF, the most abundant flagellate species were Gyrodinium spp. (∼2.4 × 104 Cells L−1), followed by Protoperidinium sp. (∼2 × 104 Cells L−1), and Amphidinium sp. (∼1 × 104 Cells L−1). Zooplankton biovolume in the upper 200 m ranged from 0.71 to 10 ml 100 m−3, with relatively higher biovolume and numerical abundance found in the SAF compared to the PF. The higher Chl-a concentration in the PF and lower zooplankton biovolume could have significant negative consequences for higher trophic levels. PAR showed higher values up to 40 m than observed depths along the fronts. Additionally, variations in salinity and temperature were recorded, with a significant decline in temperature from the STF to the SPF. Nitrate (NO3−), phosphate (PO43−), and silicate (SiO44−) concentrations were higher at the SPF than at lower latitudes. Overall, these findings indicate that the plankton community composition in the study region is influenced by a diverse set of governing variables over time, highlighting the significant implications for the regional biogeochemical cycle.

The growth inhibitory effects and non-targeted metabolomic profiling of Microcystis aeruginosa treated by Scenedesmus sp

The health of the aquatic ecosystem has recently been severely affected by cyanobacterial blooms brought on by eutrophication. Therefore, it is critical to develop efficient and secure methods to control dangerous cyanobacteria, such as Microcystis aeruginosa. In this research, we tested the inhibition of M. aeruginosa growth by a Scenedesmus sp. strain isolated from a culture pond. Scenedesmus sp. culture filtrate that had been lyophilized was added to M. aeruginosa, and cultivation for seven days, the cell density, chlorophyll a (Chl-a) concentration, maximum quantum yield of photosystem II (Fv/Fm), the activities of superoxide dismutase (SOD), catalase (CAT), and the concentration of malondialdehyde (MDA) and glutathione (GSH) were measured. Moreover, non-targeted metabolomics was carried out to provide light on the inhibitory mechanism in order to better understand the metabolic response. According to the results, M. aeruginosa is effectively inhibited by the lyophilized Scenedesmus sp. culture filtrate at a rate of 51.2%. Additionally, the lyophilized Scenedesmus sp. clearly inhibit the photosystem and damages the antioxidant defense system of M. aeruginosa cells, resulting in oxidative damage, which worsens membrane lipid peroxidation, according to changes in Chl-a, Fv/Fm, SOD, CAT enzyme activities and MDA, GSH. Metabolomics analysis revealed that the secondary metabolites of Scenedesmus sp. significantly interfere with the metabolism of M. aeruginosa involved in amino acid synthesis, membrane creation and oxidative stress, which is coherent with the morphology and physiology outcomes. These results demonstrate that the secondary metabolites of Scenedesmus sp. exert algal inhibition effect by breaked the membrane structure, destroyed the photosynthetic system of microalgae, inhibited amino acid synthesis, reduced antioxidant capacity, and eventually caused algal cell lysis and death. Our research provides a reliable basis for the biological control of cyanobacterial blooms on the one hand, and on other hand supply application of non-targeted metabolome on the study of microalgae allelochemicals.

Exploratory study of the Sentinel-3 level 2 product for monitoring chlorophyll-a and assessing ecological status in Danish seas

In situ Chl-a data were used to perform empirical calibration and validation of Sentinel-3 level 2 product in Danish marine waters. Comparing in situ data with both same-day and ±5 days moving averaged Sentiel-3 Chl-a values yielded two similar positive correlations (p > 0.05) with rpearson values of 0.56 and 0.53, respectively. However, as the moving averaged values resulted in significantly more available data than daily matchups (N = 392 vs. N = 1292) at a similar quality of correlation with similar model parameters (slope (1.53 and 1.7) and intercept (−0.28 and −0.33) respectively), which were not significantly different (p > 0.05), the further analyses were focused on ±5 days moving averaged values. A thorough comparison of seasonal and growing season averages (GSA) also showed a very good agreement, except for a few stations characterized by very shallow depth. Overestimation by the Sentinel-3 occurred in shallow coastal areas and was attributed to the interferences from benthic vegetation and high levels of Colored Dissolved Organic matter (CDOM) interfering with the Chl-a signals. Underestimation observed in the inner estuaries with shallow Chl-a rich waters, however, seen as a result of self-shading at high Chl-a concentrations, reducing effective absorption by phytoplankton. Besides the observed minor disagreements, there was no significant difference when the GSA values from in situ and Sentinel-3 were compared for all three water types (p > 0.05, N = 110). Analyzing Chl-a estimates along a depth gradient showed significant (p < 0.001) non-linear trends of declining concentrations from shallow to deeper waters for both in situ (explaining 15.2 % of the variance (N = 109)) and Sentinel-3 data (explaining 36.3 % of the variance (N = 110)), with higher variability in shallow waters. Furthermore, Sentinel-3 enabled full spatial coverage of all 102 monitored water bodies providing GSA data at much higher spatial and temporal resolutions for good ecological status (GES) assessment compared to only 61 through in situ sampling. This underlines the potential of Sentinel-3 for substantially extending the geographical coverage of monitoring and assessment. However, the systematic over- and underestimation of Chl-a in shallow nutrient rich inner estuaries through Sentinel-3 requires further attention to enable routine application of the Sentinel-3 level 2 standard product in the operational Chl-a monitoring in Danish coastal waters. We provide methodological recommendations on how to improve the Sentinel-3 products' representation of in situ Chl-a conditions. Continued frequent in situ sampling remains important for monitoring as these measurements provide essential data for empirical calibration and validation of satellite based estimates to reduce possible systematic bias.

Prediction and Analysis of Chlorophyll-a Concentration in the Western Waters of Hong Kong Based on BP Neural Network

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of Chl-a concentrations. Together with hyperspectral remote sensing data, a BP neural network model was used to invert chlorophyll-a concentration, with remote sensing reflectance as the input factor. Given the presence of many bands in the hyperspectral data, selecting an appropriate band reflectance as the input factor is crucial to improving inversion accuracy. In this study, a Pearson correlation analysis method was proposed to select bands. A normality test was performed on the reflectance of each band of the Zhuhai-1 hyperspectral remote sensing data, and the significance index was p &lt; 0.05. The absolute kurtosis value was less than 10, and the absolute skewness value was less than 3, indicating that the Pearson method was applicable. Pearson correlation analysis was utilised to calculate the correlation coefficient between the reflectance data and chlorophyll-a concentration. Five reflectance data with high correlation were selected as the input factors, and chlorophyll-a concentration was adopted as the output factor. An error backpropagation network model was constructed to predict chlorophyll-a concentration, and a Garson function was added to clarify the connection weights of the input factors in the model construction process. Model 12 was determined as the optimal model on the basis of the criteria of the coefficient of determination, the average relative variance, and the minimum mean square error. The chlorophyll-a concentration was predicted for July and November 2020 in the study area, and the results showed that the predicted values had a small error compared with the measured values. The root-mean-square error and mean relative error of the chlorophyll-a concentration predicted and measured values were 2.12 μg/L and 9.66%, respectively. Significant spatial differences in the Chl-a concentration were observed in the study area due to the influence of islands and land; the Chl-a concentration in July was generally higher than that in November. The results of these studies provide a reference for monitoring the water environment in the study area.

Spatio-temporal structures of satellite-derived water quality indicators along the Korean South Coast

The structure of 9-year time series data for Sea Surface Temperature (SST), Chlorophyll a (Chl-a) and Total Suspended Solids (TSS), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), was examined in this study. Authors found that there exists strong seasonality among the three variables with spatial heterogeneity along the Korean South Coast (KSC). In specific, SST was in phase with Chl-a, but out of phase with TSS by six months. A strong inversed spectral power with six-month phase-lag was found between Chl-a and TSS. This could be attributed to different dynamics and environmental settings. For example, Chl-a concentration seemed to have strong positive correlation with SST indicating typical seasonality of marine biogeochemical processes such as primary production; while a strong negative correlation between TSS and SST might have been influenced by changes in physical oceanographic processes, such as stratification and monsoonal wind-driven vertical mixing. In addition, the strong east–west heterogeneity of Chl-a suggests that the marine coastal environments are predominantly governed by distinct local hydrological conditions and human activities associated with land cover and land use, while the east–west spatial pattern revealed in TSS timeseries was associated with the gradient of tidal forcings and topographical changes keeping tidally induced resuspension low eastward.

Occurrence and Distribution of Fin Whales (&lt;i&gt;Balaenoptera physalus&lt;/i&gt;) and Humpback Whales (&lt;i&gt;Megaptera novaeangliae&lt;/i&gt;) in the Antarctic Atlantic Sector

The occurrence and distribution of fin whales and humpback whales along the route of the R/V Akademik Mstislav Keldysh in the Antarctic Atlantic sector in January and February 2022 were studied. The features of the whales’ distribution were considered depending on the depth of the water area, the abundance and age of krill, and the Chl-a concentration in the water. The highest occurrence of fin whales was found in the deep waters of the Powell Basin and the Orkney Trench, where the highest concentrations of adult krill have been recorded. The highest occurrence of humpback whales was observed in the shallow Bransfield Sound, where small and young krill predominated. The dependence of the occurrence of whales on the concentration of chlorophyll was not confirmed.