Chl-a Concentration Research Articles

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Despite the limited coverage of coral reefs in the world’s oceans, they play a crucial role in global marine biodiversity and providing essential ecosystem services. This study explores the influence of chlorophyll-a (Chl-a) concentration in the water column on the estimation of bottom reflectance (rb) in coral reefs monitored by the Allen Coral Atlas coral reef monitoring system, using satellite imagery from a Sentinel-2 MSI sensor. We conducted a comprehensive analysis, considering Chl-a global distribution and variability, and its combined effect with water column depth over rb calculation. Our results demonstrated that the impact of Chl-a on rb estimation becomes significant when the water column depth exceeds 3 m. While suggesting the optionality of using regional Chl-a values, our study highlights potential overestimations of Chl-a in optically complex environments, such as along the Brazilian coast. This research contributes to refining coral reef monitoring systems and underscores the importance of accurate Chl-a assessments for robust environmental evaluations.

Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach

This study represents the first application of Sentinel-2 remote sensing imagery and model fusion techniques to assess the chlorophyll-a (Chla) concentration and turbidity in Nansi Lake, Shandong Province, China, from 2016 to 2022. First, we innovatively employed the stacking method to fuse eight fundamentally different Machine Learning (ML) models, each utilising 20 and 17 feature bands, resulting in the development of a robust algorithm for estimating the Chla concentration and turbidity in Nansi Lake. The results demonstrate that the Stacking Model has achieved outstanding theoretical generalisation capability. Second, the sensitivity of the model to extreme value data in the sample was quantified, and we found that compared with extreme gradient boosting (XGBoost), the optimal performance of the Stacking Model improved by 12%, to some extent, it solved the problem of high-value underestimation and low-value overestimation. The SHapley Additive exPlanations (SHAP) results revealed that features such as Three Bands, Enhanced Three, Rrs492/Rrs560, Rrs705/Rrs665 play a crucial role in estimating Chla concentration. For the turbidity estimation, the Normalized Difference Turbidity Index (NDTI), Rrs705+Rrs560, Rrs865-Rrs740 made significant contributions. Finally, we utilised the Stacking Model to create spatiotemporal maps of the Chla concentration and turbidity in Nansi Lake from 2016 to 2022. We analysed the causes of the water quality changes and explored the driving factors. Compared with previous studies, this paper provides a new idea for the monitoring of lake water quality parameters by using the high resolution of Sentinel-2 image and the high precision of model fusion technology, these results can provide a reference for similar water area research and decision-making support for environment-related departments.

Photosynthetic response of Chlamydomonas reinhardtii and Chlamydomonas sp. 1710 to zinc toxicity.

Zinc (Zn) is an essential trace element but can lead to water contamination and ecological deterioration when present in excessive amounts. Therefore, investigating the photosynthetic response of microalgae to Zn stress is of great significance. In this study, we assessed the photosynthetic responses of neutrophilic Chlamydomonas reinhardtii and acidophilic Chlamydomonas sp. 1710 to Zn exposure for 96 h. The specific growth rate (μ), chlorophyll-a (Chl-a) content, and chlorophyll fluorescence parameters were determined. The results demonstrated that Chlamydomonas sp. 1710 was much more tolerant to Zn than C. reinhardtii, with the half-maximal inhibitory concentration (IC50) values of 225.4 mg/L and 23.4 mg/L, respectively. The μ and Chl-a content of C. reinhardtii decreased in the presence of 15 mg/L Zn, whereas those of Chlamydomonas sp. 1710 were unaffected by as high as 100 mg/L Zn. Chlorophyll fluorescence parameters indicated that the regulation of energy dissipation, including non-photochemical quenching, played a crucial role in Zn stress resistance for both Chlamydomonas strains. However, in the case of C. reinhardtii, non-photochemical quenching was inhibited by 5 mg/L Zn in the first 48 h, whereas for Chlamydomonas sp. 1710, it remained unaffected under 100 mg/L Zn. Chlamydomonas sp. 1710 also exhibited a 20 times stronger capacity for regulating the electron transfer rate than C. reinhardtii under Zn stress. The light energy utilization efficiency (α) of Chlamydomonas sp. 1710 had the most highly non-linear correlation with μ, indicating the energy utilization and regulation process of Chlamydomonas sp. 1710 was well protected under Zn stress. Collectively, our findings demonstrate that the photosystem of Chlamydomonas sp. 1710 is much more resilient and tolerant than that of C. reinhardtii under Zn stress.

Water and sediment chemistry drivers of chlorophyll-a dynamics within a Ramsar declared floodplain pan wetland system.

Floodplain pans are hydrologically dynamic in nature and characterised by variables such as chlorophyll-a (chl-a), water, and sediment chemistry over their hydroperiods. The present study investigated the spatio-temporal variations in water and sediment physico-chemical, and chlorophyll-a concentration characteristics of six floodplain pans found in the Ramsar declared Makuleke wetlands, Kruger National Park, South Africa. The water and sediment physico-chemical variable values were generally elevated during the high-water period, whereas chlorophyll-a concentrations varied across pans and hydroperiod. Benthic chl-a concentration significantly varied across pans with concentrations ranging from 161 to 1036.2mg m2. The two-way ANOVA showed significant differences in benthic chl-a concentration among hydroperiods, and no significant differences were observed in pelagic chl-a across pans and hydroperiods. Generally, pelagic and benthic chl-a concentration increased as water and sediment chemistry variables increased. Furthermore, three sediment variables, i.e. pH, calcium, and magnesium, and water conductivity were found to be significant in structuring benthic chlorophyll-a dynamics in pans. However, none of the sediment and water variables had a significant effect on pelagic chl-a. Hydroperiod had a significant effect on influencing chl-a concentration, with high and low water level periods being characterised by low and high chl-a concentration, respectively. The n-MDS results showed strong overlaps in chl-a biomass among the Makuleke floodplain pans across hydroperiods. The increasing chl-a concentration in these floodplain pans due to potential bioturbation effects as a result of large mammals could potentially lead to eutrophication, which in turn could affect the system's primary productivity and aquatic biota. Therefore, it is important to establish a continuous monitoring programme on these pans to inform sound management decisions.

Assessment of aquatic ecosystem quality in Dharoi Reservoir using Sentinel-2 satellite imagery

Background: Dharoi Reservoir, located in Gujarat, India, is a vital freshwater resource supporting agriculture, industry, and local communities. Chl-a, a key indicator of water quality, reflects the trophic state and ecological balance of aquatic systems. Objective(s): This study aims to provide comprehensive insights into the water quality dynamics of Dharoi Reservoir, offering valuable information for environmental management and sustainable water resource planning. Methods: This study employs high-resolution Sentinel-2 satellite imagery to analyze Chl-a concentrations in the reservoir during October 2020. The Chl-a index, calculated by dividing Sentinel-2 bands B5 and B4, reveals a spatial distribution of Chl-a concentrations. Results: The Chl-a index ranges from 73.78 to 100. The mean Chl-a index is 91.6 with a standard deviation of 3.27, indicating elevated and variable Chl-a concentrations. Conclusions: The findings contribute to the understanding of the reservoir’s ecological health and assist in making informed decisions for water quality management. This research exemplifies the integration of remote sensing technology and environmental stewardship, promoting sustainable water management practices in the region. Policy recommendations: No policy recommendations are explicitly stated in the abstract, but they could be inferred from the conclusions. For example, one possible policy recommendation is to monitor and regulate the sources of nutrient inputs into the reservoir, such as agricultural runoff, sewage, and industrial effluents, to reduce the risk of eutrophication and algal blooms. Another possible policy recommendation is to implement adaptive management strategies that consider the seasonal and spatial variability of Chl-a concentrations and their impacts on water quality and availability.

Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Remote sensing is a cost-effective method for monitoring chlorophyll-a (Chl-a) concentration, an indicator of eutrophication, due to its spatiotemporal effectiveness and availability of historical data. However, its application in shallow, small water bodies poses challenges due to the need for high spatial and temporal resolutions, significant optical interference from other water constituents, and depth-related accuracy issues. This study assessed the performance of Landsat-8 and Sentinel-2 satellites in estimating Chl-a concentrations in Chitgar Lake, Tehran. Semi-empirical models, constructed with 73 and 122 common data pairs between observed data and Landsat-8 and Sentinel-2 reflectance data, respectively, yielded accurate estimates. Two Band (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE (root mean square error) = 1.12 µg.L-1 and NRMSE (normalized root mean square error) = 12.4 %) was identified as the best Landsat-8 based model. Both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands composition, R2 = 0.82, RMSE = 1.29 µg.L-1, NRMSE = 7.8 %) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L-1, and NRMSE = 7.7 %) were the best models for Sentinel-2 data, with a Power of 0.8. Overall, remote sensing data from both satellites demonstrated appropriate performance and can accurately estimate Chl-a concentrations in Chitgar Lake as a shallow freshwater body.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

Optimization of convolutional neural network with dual attention mechanism: Estimation of chlorophyll-a concentration in the Taiwan Strait using MODIS data

Chlorophyll-a (Chl-a) concentration plays a crucial role in monitoring marine phytoplankton, holding significant implications for marine ecosystems and human livelihoods. Remote sensing serves as a valuable method for directly estimating Chl-a concentration and facilitating marine monitoring activities. In order to accurately estimate Chl-a concentration and effectively monitor the marine ecological environment of the Taiwan Strait, this paper introduces a novel neural network model called Convolutional Neural Network with Dual Attention Mechanism Optimization (CNN-CBAM). Additionally, the spectral bands derived from MODIS 500 m imagery are subjected to various processing techniques to generate four distinct sets of feature data. These datasets are subsequently merged with in-situ measurements of Chl-a concentration obtained from buoy stations. Given the limited availability of buoy stations, the temporal continuity of data is leveraged to overcome the spatial distribution constraints. This enables the estimation of Chl-a concentration at various time intervals within the research area of the Taiwan Strait. To evaluate the performance of the model, metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 are employed. The results demonstrate that the CNN-CBAM model, utilizing MODIS temporal data, effectively retrieves Chl-a concentration in the research area of the Taiwan Strait with high accuracy. For the CNN-CBAM model, the RMSE is 0.34 μg L−1, MAPE is 22%, and R2 is 0.86. In comparison, the traditional empirical model using the Band Ratio (BR) model has an RMSE of 0.61 μg L−1, MAPE of 57%, and R2 of 0.31. The results indicate that the CNN-CBAM model has improved R2 by 0.55 compared to the traditional BR model. Additionally, the RMSE and MAPE have decreased by 0.27 μg L−1 and 35%, respectively. The superiority of the CNN-CBAM model over the traditional BR model is evident, showcasing a substantial enhancement in the accuracy of Chl-a concentration retrieval. Given the challenges posed by the intricate and ever-changing marine environment, particularly the scarcity of in-situ buoy stations, this study offers a practical solution for monitoring Chl-a concentration. It establishes a framework for leveraging satellite-based multispectral sensors to enable large-scale and multi-temporal remote sensing retrieval of Chl-a concentration in the research area of the Taiwan Strait.

A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

Spatial pattern of sea surface temperature and chlorophyll-a trends in relation to hydrodynamic processes in the Alborán Sea

Environmental conditions such as temperature, planktonic biomass, and ocean currents play an important role in the development and distribution of marine species. This work aims to estimate, in a high spatial resolution, the actual trends of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) and to assess the relationship with the local hydrodynamic conditions in the Alborán Sea. To investigate these objectives, time series of SST and Chl-a of satellite sensor data were analyzed during 20 years from January 2001 to December 2020, using the Seasonal-Trend-Loess (STL) decomposition method and the Mann-Kendall seasonality test. The results, obtained with a 95% of confidence, showed that the Alborán Sea basin is subject to sea surface warming evaluated at 0.027 ± 0.008 ° C per year, related to the warming of the Atlantic water mass, which contributes to a decrease of productivity evaluated at -0.0024 ± 0.0003 μg /l per year of Chl-a concentration. These trends are not homogeneous over the entire basin area but show a large regional variation between different parts of the Alborán Sea due to the hydrodynamic process of the Atlantic Jet - Western Alboran Gyre system (AJ-WAG), which is more active especially in summer/autumn seasons and contribute largely to these changes by mixing the waters of the Atlantic Ocean and the Mediterranean Sea.

Biophysical coupling of seasonal chlorophyll-a bloom variations and phytoplankton assemblages across the Peninsula Front in the Bransfield Strait

Abstract. This study investigates the spatio-temporal variations in the chlorophyll-a (chl-a) blooms in the Bransfield Strait (BS) at a climatological scale (1998–2018). We propose that suitable monitoring of these blooms can be achieved through remotely sensed observations only if the BS is divided following the Peninsula Front (PF), which ultimately influences the phytoplankton assemblage. Our analysis is based on characterizing climatological fields of sea surface temperature (SST), air temperature, sea ice coverage, chl-a concentrations and wind stress, guided by synoptic novel and historical in situ observations which reveal two niches for phytoplankton assemblage: the Transitional Bellingshausen Water (TBW) and Transitional Weddell Water (TWW) pools. The TBW pool features stratified, less saline, warmer waters with shallow mixed layers, while the TWW pool features well-mixed, saltier, and colder waters. We identify that the 0.6 °C isotherm corresponds to the summertime climatological PF location, effectively dividing the BS into two different scenarios. Furthermore, the 0.5 mg m−3 chl-a isoline aligns well with the 0.6 °C isotherm, serving as a threshold for chl-a blooms of the highest concentrations around the South Shetland Islands. For the first time, these thresholds enable the monthly climatological descriptions of the two blooms developing in the BS on both sides of the PF. We think this approach underscores the potential of combining SST and chl-a data to monitor the year-to-year interplay of the chl-a blooms occurring in the TBW and TWW pools contoured by the PF.

Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites

A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned area) as a source, precipitation/drought representing transport dynamics, and lakes as the ultimate receptor. This identified 106 lakes worldwide that are likely prone to be impacted by wildfires via a terrestrial pathway. Satellite-derived chlorophyll-a (Chl-a) and turbidity variables were used as indicators to detect changes in lake water quality potentially induced by wildfires over a four-year period. The lakes with the largest catchment areas burned and characterized by regular annual fires were located in Africa. Evidence for a strong influence of wildfires was not found across the dataset examined, although clearer responses were seen for some individual lakes. However, among the hydro-morphological characteristics examined, lake depth was found to be significant in determining Chl-a concentration peaks which were higher in shallow and lower in deep lakes. Lake turbidity responses indicated a dependence on lake catchment and weather conditions. While wildfires are likely to contribute to the nutrient load of lakes as found in previous studies, it is possible that in many cases it is not a dominant pressure and that its manifestation as a signal in lake Chl-a or turbidity values depends to a large part on lake typology and catchment characteristics. Assessment of lake water quality changes six months after a fire showed that Chl-a concentrations either increased, decreased, or showed no changes in a similar number of lakes, indicating that a lake specific ecological and hydro-morphological context is important for understanding lake responses to wildfires.

Variability in the relationship between light scattering and chlorophyll a concentration in oligotrophic tropical regions of the Western Pacific Ocean.

It is important to determine the relationship between the concentration of chlorophyll a (Chla) and the inherent optical properties (IOPs) of ocean water to develop optical models and algorithms that characterize the biogeochemical properties and estimate biological pumping and carbon flux in this environment. However, previous studies reported relatively large variations in the particulate backscattering coefficient (bbp(λ)) and Chla from more eutrophic high-latitude waters to clear oligotrophic waters, especially in oligotrophic oceanic areas where these two variables have little covariation. In this study, we examined the variability of bbp(λ) and Chla in the euphotic layer in oligotrophic areas of the tropical Western Pacific Ocean and determined the sources of these variations by reassessment of in-situ measurements and the biogeochemical-argo (BGC-Argo) database. Our findings identified covariation of bbp(λ) and Chla in the water column below the deep Chla maximum (DCM) layer, and indicated that there was no significant correlation relationship between bbp(λ) and Chla in the upper layer of the DCM. Particles smaller than 3.2 µm that were in the water column above the DCM layer had a large effect on the bbp(λ) in the vertical profile, but particles larger than 3.2 µm and smaller than 10 µm had the largest effect on the bbp(λ) in the water column below the DCM layer. The contribution of non-algal particles (NAPs) to backscattering is up to 50%, which occurs in the water depth of 50 m and not consistent with the distribution of Chla. Phytoplankton and NAPs were modeled as coated spheres and homogeneous spherical particles to simulate the bbp(λ) of the vertical profile by Aden-Kerker method and Mie theory, and the results also indicated that the backscattering caused by particles less than 20 µm were closer to the measured data when they were below and above the DCM layer, respectively. This relationship also reflects the bbp(λ) of particles in the upper water was significantly affected particle size, but bbp(λ) in the lower water was significantly affected by Chla concentration. This effect may have relationship with phytoplankton photoacclimation and the relationship of a phytoplankton biomass maximum with particle size distribution in the water column according to the previous relevant studies. These characteristics also had spatial and seasonal variations due to changes of Chla concentration at the surface and at different depths. There was mostly a linear relationship between Chla and bbp(700) during winter. During other seasons, the relationship between these two variables was better characterized by a power function (or a logarithmic function) in the lower layer of the DCM. The spatial and vertical relationships between the bbp(λ) and Chla and the corresponding variations in the types of particles described in this study provide parameters that can be used for accurate estimation of regional geochemical processes.

Lagged effect of Southern Annular Mode on chlorophyll-a in the mid-latitude South Pacific and Indian Oceans

This study investigates the influence of the Southern Annular Mode (SAM) on chlorophyll-a (Chl-a) concentrations and the underlying mechanisms governing their associated environmental variations in the mid-latitude (35–50° S) ocean from 1998 to 2021. The intensification of westerly winds during positive SAM phases influences meridional water transport and mixed layer depth (MLD), which are both critical factors that affect surface nutrient availability. A marked contrast in the relationship between the meridional current anomaly and the SAM was observed, with reduced northward transport of nutrient-rich water in regions north of 50° S during positive SAM phases. This reduction could be attributed to the poleward migration of the westerly winds, which impeded the meridional current from reaching the mid-latitudes. The relationship between SAM and MLD south of 50° S was positive whereas that in the mid-latitude eastern (60–110° E) South Indian Ocean and eastern (90–140° W) South Pacific Ocean was negative or weak. The immediate effect of a more positive SAM on Chl-a in the mid-latitude ocean was reduced productivity caused by enhanced nutrient depletion. However, in the mid-latitude eastern South Pacific Ocean, the northward migration of the zonal mean meridional current anomaly closely aligned with the lagged correlation pattern between SAM variability and Chl-a over time, suggesting that the delayed northward transport of nutrient-rich waters may partially counterbalance the immediate effects of the SAM on ocean productivity. This mechanism was not present in the mid-latitude eastern South Indian Ocean, implying that future climate change may variably affect these regions. Our findings emphasize the importance of considering regional differences and temporal lags when evaluating the influence of SAM variability on ocean productivity and nutrient dynamics in the context of climate change.

Effects of two typical quinolone antibiotics in the marine environment on Skeletonema costatum

This study investigated the effects of levofloxacin (LEV) and norfloxacin (NOR) on Skeletonema costatum, focusing on cell growth, chlorophyll a (Chla) content, maximal quantum yield of PSII (Fv/Fm), protein content, enzyme activities of superoxide dismutase (SOD), glutathione reductase (GR), and glutathione peroxidase (GSH-PX), and the membrane lipid peroxidation product malondialdehyde (MDA) content were conducted to analyze the responses of S. costatum under LEV and NOR exposure. Cell growth, Chla content, Fv/Fm, protein content, enzyme activities, and MDA content were assessed to elucidate physiological changes. Both LEV and NOR inhibited S. costatum growth, except for 10 mg/L NOR, which promoted growth. Algal cells exhibited higher sensitivity to LEV, with 96h-IC50 values of 14.770 mg/L for LEV and 44.250 mg/L for NOR. Low NOR concentration (10 mg/L) increased Chla content, while high antibiotic concentrations (>20 mg/L for LEV, >100 mg/L for NOR) decreased Chla content and Fv/Fm, indicating an impact on photosynthesis. Elevated LEV and NOR levels reduced protein and MDA content but increased GR, SOD, and GSH activities, indicating induced oxidative stress. The study provides a comprehensive analysis of LEV and NOR effects on marine microalgae growth and underlying physiological mechanisms, shedding light on potential ecological risks posed by antibiotics in marine ecosystems.

Deconstructing co-occurring marine heatwave and phytoplankton bloom events in the Arkona Sea in 2018

Between May and August 2018, two separate marine heatwaves (MHWs) occurred in the Arkona Sea in the western Baltic Sea. These heatwaves bookended an extended period of phytoplankton growth in the region. Data from the Ocean and Land Colour Instrument (OLCI) on board the European Sentinel-3 satellite revealed an eddy-like structure containing high chlorophyll a (Chl-a) concentrations (ca. 25 mg.m-3) persisting for several days at the end of May in the Arkona Sea. Combining ocean colour observations, a coupled bio-optical ocean model and a particle tracking model, we examined the three dimensional relationship between these co-occurring MHW and phytoplankton bloom events. We find that the onset of the MHW in May provided the optimal conditions for phytoplankton growth, i.e. sufficient light and nutrients. Wind-driven surface eddy circulation, geostrophic eddy stirring and transient submesoscale dynamics along the edges of the eddy provided a transport path for nutrient fluxes and carbon export, and helped to sustain the phytoplankton bloom. The bloom may have indirectly had an enhancing effect on the MHW, through the impact of water constituent-induced heating rates on air-sea energy fluxes. The subsurface signature of the MHW plays a critical role in de-coupling surface and subsurface dynamics and terminating the phytoplankton bloom. Subsurface temperature anomalies of up to 8°C between 15 and 20 m depth are found to persist up to 15 days after the surface signature of the MHW has disappeared. The study reveals how surface and subsurface dynamics of MHWs and phytoplankton blooms are connected under different environmental conditions. It extends our knowledge on surface layer processes obtained from satellite data.

A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro Hyper-Spectrometer.

The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350-950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum's relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment.

Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study

Ocean monitoring systems are designed for continuous monitoring of the seas and oceans to track their evolution and thus anticipate environmental issues. However, they are often based on Internet of Things (IoT) systems that are expensive, hard to maintain, and offer little spatial coverage. An emerging alternative is Satellite Remote Sensing (SRS) systems that offer good geographical coverage but, as a reliable and real-time monitoring system, they also face several challenges such as environmental conditions, spatio-temporal granularity, terrain oleography, etc. This paper introduces an easy-to-use software tool to crawl water-quality data from up to 6 satellite instruments (i.e. Sentinel 3A, Sentinel-3B, NOAA VIIRS, SNPP VIIRS, Aqua MODIS, Terra MODIS) of the European Space Agency's (ESA) Coopernicus system and NASA's Marine Environmental Monitoring Service. We also provide an in-depth analysis in terms of reliability and data coverage for the chlorophyll-a (Chl-a) in a highly anthropised local/regional context like the Mar Menor lagoon (Murcia, Spain), where serious socio-environmental issues are arising due to the eutrophication process. Our results show a good linear correlation between in situ data, obtained by several Spanish public institutions since 2016, and data generated by the IRTM-NN (Inverse Radiative Transfer Model-Neural Network) algorithm of Sentinel 3 in general, reaching coefficient correlation values close to 0.9. They also show that organic matter inputs from ephemeral streams are relevant in determining Chl-a concentrations. Moreover, the temporal coverage using a single instrument is rather limited, as no data were recorded for 80% of the days studied. This figure decreases to 30% when data from the 6 satellite instruments are combined, increasing the temporal granularity of the measurements from approximately 5 to 1.5 days, suggesting the need for a combination of these systems for a robust SRS system.

Competition between ocean thermal structure and tropical cyclone characteristics modulates ocean environmental responses in the Yellow and Bohai Seas

To study the environmental responses of tropical cyclones (TCs) in continental shelf regions, TCs passing over the Yellow Sea and Bohai Sea (YBS) during 2002–2020 were investigated, with a special focus on how competition between ocean thermal structure and TC characteristics modulates ocean surface changes. The spatial distributions of the climatic mixed layer depth (MLD), accumulated wind forcing power index (WPi), accumulated sea surface temperature (SST) changes and accumulated chlorophyll (Chl-a) changes in the YBS were calculated. The linear regressions indicate that both the TC-induced SST cooling and TC-induced Chl-a increase are correlated with the TC wind speed rather than the translation speed, especially when the TC forcing depth (Zmixing) is greater than the MLD. Otherwise, both the changes in SST and Chl-a are correlated with the TC translation speed when Zmixing is shallower than the MLD. Further study has shown that whether TCs can break the MLD is also a key condition for oceanic responses. In the southern YBS, which has a deep-sea basin and MLD, the TC wind speed is the major factor affecting SST cooling and Chl-a increase, as TCs need more strength to reach the MLD. However, in the northern YBS, which has the shallowest sea basin and MLD, even weak TCs can easily break the MLD and reach the seabed; thus, ocean surface changes are associated mainly with the TC translation speed. The composite results reveal that both the maximum SST cooling center (1.64 °C) and the maximum Chl-a increasing center (0.14 log10(mg/m3)) are located on the right and behind the TC center, respectively. In addition, TC-induced SST cooling and Chl-a increase were initiated two days prior to TC passage and then reached their maximum values after 1 day. It takes approximately 7–8 days for the Chl-a concentration to recover, but it takes a much longer time (>15 days) for the SST to recover.