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This study proposes a novel hybrid prediction model for short-term Chl-a concentration prediction in the Chaohu Lake basin. The model adopts a two-layer decomposition, grouping prediction, and summation design, with an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) used for data decomposition and sample entropy (SampEn) for determining the complexity of the decomposed components. The component with the highest SampEn is then further decomposed using variational mode decomposition (VMD). In the grouping prediction part, a hybrid prediction model is utilized, based on bidirectional gate recurrent unit (BiGRU), temporal convolutional network (TCN), and attention mechanism (AM), to predict all components after the second decomposition separately. The final Chl-a concentration prediction is obtained by linearly summing the predicted values of each component. Through comparison with sensitivity analysis, point estimation, and interval estimation, this study finds that the proposed ICEEMDAN-VMD-BiGRU-TCN-AM model performs well in predicting the content of Chl-a concentration in lakeshore. The model has the highest fitting determination coefficient of 98.79%, the smallest root-mean-square error (RMSE) of only 0.0063 (ug/L), and the largest probability interval coverage percentage (PICP) of 97.57%. These results indicate that the IVBTA model has significant advantages in accuracy, precision, and stability compared to other models. This means that our method can more accurately predict the content of Chl-a in reservoirs, providing a new approach for water quality prediction and effective prevention and control of eutrophication in reservoirs. [ABSTRACT FROM AUTHOR]

The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns. However, cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss, which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes. If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency, highest accuracy, best applicability, and greatest robustness at different scales remains debatable to date. Therefore, in the present study, four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales. The experimental results revealed that in terms of spatial distribution, the chlorophyll-a concentration products generated by averaging method (Chl1-AV/AVW) and GSM model (Chl1-GSM) presented a relatively high data coverage frequency in Case I water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions, the chlorophyll-a concentration products generated by the Neural Network algorithm (Chl2) presented high data coverage frequency in the estuarine coastal zone Case 2 water regions. The chlorophyll-a concentration products generated by the OC5 algorithm (ChlOC5) presented high data coverage frequency in Case I water regions and the turbid Case II water regions. In terms of absolute precision, the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions, and the Chl2 product performed well only in Case II estuarine coastal zones, while presenting large errors in absolute precision in the Case I water regions. The ChlOC5 product presented a higher precision in Case I and Case II water regions, with a better and more stable performance in both regions compared to the other products. [ABSTRACT FROM AUTHOR]

Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) concentration variation. SST is also an important factor affecting marine fishery resources. Chl-a is closely related to the marine ecosystem and primary productivity. In this study, we analyzed the response of SST and Chl-a to Typhoon Lekima (2019) process. The result indicates that the response of temperature to typhoon decreases from the center to the outer edge, which has a good correlation with the location, path and influence area of the typhoon center. The mean SST in the study area (14°~40° N, 116°~136° E) decreased during the typhoon's passage, from 28.97 °C at the beginning (5 August) to 28.22 °C (15 August). The concentration of Chl-a was high in the northwest and coastal areas; its mean value in the study area decreased from 2 to 8 August (on 2 and 8 August, the concentration was 0.484 mg/m3 and 0.405 mg/m3, respectively). From 8 to 14 August, Chl-a decreased with the increase in SST, and 10 and 14 August were the two peak values of Chl-a (while SST was low). Chl-a concentration increased after the typhoon's landfall (from 15 to 31 August); the Chl-a trend was the same as that of SST. The stronger the typhoon and the longer the residence time, the greater the contribution to the increase in Chl-a concentration at sea surface. Typhoon-induced rainfall over the ocean surface, increased evaporation of seawater, enhanced mixing within the mixed layer and upwelling of the pycnocline resulted in an increase in Chl-a quantity. This study describes the spatial response of the upper ocean to typhoons. It provides a general method for the comprehensive assessment of typhoons in marginal seas and upper open oceans, which has wide applicability and good scientific application prospects. [ABSTRACT FROM AUTHOR]

Abstract Rivers remarkably contribute to the transfer of fresh water and nutrients between land and marine ecosystems, underscoring their essential ecological role. The Po River contributes approximately one-third of the total freshwater discharge into the Adriatic Sea. Despite its importance, there is a lack of information regarding the impact of Po River discharge on the physical and biological characteristics of specific regions within the Adriatic Sea, such as the Gulf of Manfredonia. By using ocean model results and satellite data for 2014, 2015, 2016, and 2017, this study attempts to assess how Po River discharge fluctuations affect the Gulf’s physical properties and chlorophyll-a concentrations. We demonstrate here that river discharge, physical characteristics, and chlorophyll-a interact in complex ways within the Gulf of Manfredonia. The Gulf's temperature (correlation coefficient r = −0.33) and salinity (r = −0.38) are inversely correlated with Po River discharge, whereas sea level (r = 0.59) and vertical velocity (r = 0.54) are directly correlated to the discharge. In the Gulf, a seasonal anticyclonic eddy exhibits varying strength and shape throughout the summer and autumn, influenced by the fluctuation in Po River discharge. In December 2014, chlorophyll-a concentrations increased remarkably (over 10 mg m−3) following a remarkable rise in Po River discharges. However, there is a one-month time lag between the discharge increase and the subsequent rise in chlorophyll-a levels in the gulf.

We estimated the concentration of Chlorophyll-a (Chl-a) along with physical and biological factors including sea surface temperature (SST), wind speed and concentration of nitrate and phosphate in the Western, Central and Eastern Equatorial Indian Oceans (WEIO, CEIO and EEIO) using data from the Copernicus Marine Service and ERA-5 for the years 1994 to 2019. Our results show that along with the concentration of Chl-a, nitrate and phosphate have decreased, and sea surface temperature has increased in the EEIO. Due to coastal upwelling off the coast of Somalia and offshore Ekman transport throughout the summer, the southwest monsoon caused a significant increase in concentrations of Chl-a, nitrate, phosphate and wind speed in WEIO during June-July-August-September (JJAS) and October-November-December (OND). SST decreased at this time and deeper water interacted with surface water, increasing biological production. In EEIO, the upwelling zone is primarily maintained by wind-induced divergence throughout the year, whereas stratification in winter prevented surface Chl-a increase. High wind speed and low SST led to increased productivity in WEIO. Comparison of EEIO with WEIO and CEIO demonstrates low levels of Chl-a, phosphate and nitrate. The trends signify that these oceanic zones are highly vulnerable to developing into ecological deserts in the near future. [ABSTRACT FROM AUTHOR]

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake. [ABSTRACT FROM AUTHOR]

Rivers remarkably contribute to the transfer of fresh water and nutrients between land and marine ecosystems, underscoring their essential ecological role. The Po River contributes approximately one-third of the total freshwater discharge into the Adriatic Sea. Despite its importance, there is a lack of information regarding the impact of Po River discharge on the physical and biological characteristics of specific regions within the Adriatic Sea, such as the Gulf of Manfredonia. By using ocean model results and satellite data for 2014, 2015, 2016, and 2017, this study attempts to assess how Po River discharge fluctuations affect the Gulf's physical properties and chlorophyll-a concentrations. We demonstrate here that river discharge, physical characteristics, and chlorophyll-a interact in complex ways within the Gulf of Manfredonia. The Gulf's temperature (correlation coefficient r = −0.33) and salinity (r = −0.38) are inversely correlated with Po River discharge, whereas sea level (r = 0.59) and vertical velocity (r = 0.54) are directly correlated to the discharge. In the Gulf, a seasonal anticyclonic eddy exhibits varying strength and shape throughout the summer and autumn, influenced by the fluctuation in Po River discharge. In December 2014, chlorophyll-a concentrations increased remarkably (over 10 mg m−3) following a remarkable rise in Po River discharges. However, there is a one-month time lag between the discharge increase and the subsequent rise in chlorophyll-a levels in the gulf. [ABSTRACT FROM AUTHOR]

Over the last three decades, ocean color satellites have recorded continuous change in phytoplankton concentration, and in doing so, the satellites have helped marine remote sensing to become an emerging discipline. However, ocean color satellite has provided the state of global phytoplankton only around midday whereas phytoplankton have significant diurnal variation characteristics over an entire day-night cycle, which would lead to large estimation uncertainty in downstream products. A large global field observations show that the diurnal variation curves can be constructed as the sum of an exponential and a polynomial function. After correction with our diurnal variation curves, the intermission difference between MODISA and MODIST phytoplankton product could be decreased from 7.57% to 6%, which is critical to monitor the long-term changing trend with multi-mission data. When the satellite-recorded chlorophyll-a concentration is simply equal to a daily average value, it results in overestimating uncertainties of 16% and 54% for the northern and southern hemispheric oceans, respectively. Moreover, as a typical example of downstream product of the chlorophyll-a, the coverage of oligotrophic waters can oscillate 76% from day to night simulated from satellite images with our diurnal variation curves, further confirming the importance of global synchronous observation. The geostationary satellite might be an efficient regional tool for diurnal observation, but it must account for its limitations under the weak light conditions of early morning and late afternoon. In the real world, the effects of diurnal variations in ocean properties reach far beyond chlorophyll-a in the global oceans. We anticipate these quantitatively findings to be a starting point for quantitatively re-examining the proper application of ocean color products in scientific communication.

Field observations were combined with a coupled hydrodynamic and water quality model to investigate the spatial and temporal variation in Chlorophyll-a (Chl-a) in Jinmeng Bay, China. The relatively high Chl-a values were distributed in the inshore waters, mainly due to the abundant nutrient inflow from the Tanghe River. The model's results indicate that the Chl-a concentration was much higher in seaweed beds surrounded by artificial islands and reefs under the southeasterly wind, largely due to the fact that pollutants are prone to accumulate in coastal areas where flow is attenuated by the presence of natural and artificial marine structures. It was also found that the southwesterly winds suppress the inflow of nutrients from the Tanghe River to the coastal areas, and, therefore, lower the Chl-a levels. River input and wind forcing are the major factors that influence Chl-a concentrations in the anthropogenically influenced bay. This finding provides useful guidance for the prediction and mitigation of green tides in Jinmeng Bay in the future. [ABSTRACT FROM AUTHOR]

The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, was tested for transitional and coastal waters of the Lithuanian Baltic Sea and the Ukrainian Black Sea during 2006–2019. Among three tested neural network-based processors (FUB-CSIRO, C2RCC, standard Level-2 data), the FUB-CSIRO applied to Sentinel-3 OLCI images was the most appropriate for the retrieval of chlorophyll-a in both seas (R2 = 0.81). Based on 147 combined MERIS and OLCI synoptic satellite images for the Baltic Sea and 234 for the Black Sea, it was shown that the CyaBI corresponds to the eutrophication patterns and trends over the open, coastal and transitional waters. In the Baltic Sea, the cyanobacteria blooms mostly originated from the central part and the outflow of the Curonian Lagoon. In the Black Sea, they occurred in the coastal region and shelf zone. The recent decrease in bloom presence and its severity were revealed in the areas with riverine influence and coastal waters. Intensive blooms significantly enhanced the short-term increase in sea surface temperature (mean ≤ 0.7 °C and max ≤ 7.0 °C) compared to surrounding waters, suggesting that EO data originating from thermal infrared sensors could also be integrated for the ecological status assessment. [ABSTRACT FROM AUTHOR]

Chlorophyll-a is an important parameter that is used to measure the water quality, and its concentrations indicate the degree of eutrophication. Taking the waters of Chaohu Lake as the research area, four remote sensing inversion models of the chlorophyll-a concentrations were constructed based on GF-1 WFV images using empirical and semiempirical/semianalytical methods. The optimal model was selected by evaluating the model validation accuracies, and this model was used to invert the spatial distributions of the chlorophyll-a concentrations in the waters of Chaohu Lake from May to July 2017. The results show that the NDCI model is the most suitable for inverting chlorophyll-a concentrations in the waters of Chaohu Lake based on GF-1 WFV images. This model has the highest validation accuracy, with a determination coefficient of 0.933, a Root mean square error of $2.85 ~\mu \text{g}$ /L and a mean absolute percentage error of 19.69%. The spatial distributions of the chlorophyll-a concentrations in the waters of Chaohu Lake from May to July 2017 all showed decreasing trends from Western Half Lake to Eastern Half Lake, the areas of higher chlorophyll-a concentrations in May and June were mainly concentrated in the northern waters of Western Half Lake, and the area with the highest chlorophyll-a concentrations in July was located in the northwestern waters of Middle Half Lake. The mean chlorophyll-a concentration in the waters of Chaohu Lake from May to July 2017 was $12.5 ~\mu \text{g}$ /L, which indicated slight eutrophication.

The coastal environment in the Saemangeum area has experienced persistent physical stresses owing to the irregular operation of the sluice gates and related artificial disturbances since seawall construction, which has led to restricted freshwater-seawater circulation. To understand the impacts of stress, we performed long-term (1999-2022, 24 years) in situ measurements of relevant biotic and abiotic parameters and employed the random forest (RF) technique to determine the phytoplankton community response to environmental disturbance. Specifically, we estimated chlorophyll-a (Chl-a) concentrations using an RF model based on various environmental factors such as sea surface temperature (SST), sea surface salinity (SSS), dissolved oxygen saturation (DO), dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphorus (DIP) as input variables. From the RF analysis, each environmental factor contributed to variation in Chl-a concentration as follows: SSS (42.91%), SST (17.88%), DIP (14.38%), DIN (13.36%), and DO (11.48%). In addition, we performed sensitivity experiments by altering the salinity, which was revealed to be the most influential environmental parameter. As a result, Chl-a concentration increased by approximately 1.79 times in lower salinity conditions (from 7 to 27 psu) compared to the normal salinity conditions prior to the seawall construction (from 12 to 32 psu) in both areas, including the inside and outside the seawall. More importantly, lower salinity conditions stimulated dinoflagellate blooms, that is, red tides, implying that restricted freshwaterseawater circulation could worsen coastal ecosystems. Thus, this study contributes to understanding the impacts of environmental changes caused by sluice gate manipulation on marine ecosystems, such as phytoplankton community dynamics. Moreover, this study recommends an ecologically suitable operation scheme for Saemangeum sluice gates to ensure a healthy coastal ecosystem. [ABSTRACT FROM AUTHOR]

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As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance ( R r s λ ) products. Firstly, the GOCI  R r s λ  correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.916, a root mean square error (RMSE) of 0.212 mg · m−3, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg · m−3. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg · m−3. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI's potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations. [ABSTRACT FROM AUTHOR]

The oceanic waters of the Southwest Tropical Pacific occupy a vast region including multiple Pacific Island Countries. The state of these waters is determinant for fisheries and the blue economy. Ocean color remote sensing is the main tool to survey the variability and long-term evolution of these large areas that are important for economic development but are affected by climate change. Unlike vast oligotrophic gyres, tropical waters are characterized by numerous archipelagos and islands, with deep and shallow lagoons subjected to the large impacts of the land. Strikingly large dendritic phytoplankton (Trichodesmium) blooms with high levels of chlorophyll, developing within archipelagos, as well as coastal enrichments from various origins may be observed. Algorithms to detect the presence of Trichodesmium have been developed or adapted, as well as algorithms to estimate the chlorophyll concentration ([Chl-a)]. Adapting existing [Chl-a] algorithms does not always yield high, i.e., sufficient, accuracy. A review of published regional bio-optical algorithms developed taking into account the specific phytoplankton composition and minimizing the adverse impacts of particles and the seabed bottom on [Chl-a] determination is presented, as well the bio-optical database that allowed their development. The interest of such algorithms for a variety of applications and scientific accomplishments is highlighted, with a view to further addressing the main biology and biogeochemistry questions, e.g., to determine the true impact of diazotrophs and assess lagoon [Chl-a] variability with the highest confidence. This work anticipates the use of future coarse and high-spatial-resolution and multi- and hyper-spectral satellite imagery in the Pacific. [ABSTRACT FROM AUTHOR]

This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.

Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) concentration variation. SST is also an important factor affecting marine fishery resources. Chl-a is closely related to the marine ecosystem and primary productivity. In this study, we analyzed the response of SST and Chl-a to Typhoon Lekima (2019) process. The result indicates that the response of temperature to typhoon decreases from the center to the outer edge, which has a good correlation with the location, path and influence area of the typhoon center. The mean SST in the study area (14°~40° N, 116°~136° E) decreased during the typhoon’s passage, from 28.97 °C at the beginning (5 August) to 28.22 °C (15 August). The concentration of Chl-a was high in the northwest and coastal areas; its mean value in the study area decreased from 2 to 8 August (on 2 and 8 August, the concentration was 0.484 mg/m3 and 0.405 mg/m3, respectively). From 8 to 14 August, Chl-a decreased with the increase in SST, and 10 and 14 August were the two peak values of Chl-a (while SST was low). Chl-a concentration increased after the typhoon’s landfall (from 15 to 31 August); the Chl-a trend was the same as that of SST. The stronger the typhoon and the longer the residence time, the greater the contribution to the increase in Chl-a concentration at sea surface. Typhoon-induced rainfall over the ocean surface, increased evaporation of seawater, enhanced mixing within the mixed layer and upwelling of the pycnocline resulted in an increase in Chl-a quantity. This study describes the spatial response of the upper ocean to typhoons. It provides a general method for the comprehensive assessment of typhoons in marginal seas and upper open oceans, which has wide applicability and good scientific application prospects.

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.

As one of the important lakes in the “One Lake and Two Seas” of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

This study, focusing on the Antarctic polynyas, performed the imputation of chlorophyll-a concentration (Chl-a) dataset, which is one of the ocean color products mainly used for estimating primary productivity, using the Data Interpolating Empirical Orthogonal Function method and constructed accurate time-series data that excludes as much uncertainty as possible in long-term variability studies due to missing data. The polynya regions were classified into a total of 23 zones through quantitative criterions, and the statistical accuracy of imputation performance was 0.89 for R2 and 0.42, 0.24, and 0.15 for root mean square error, mean squared error, mean absolute error, respectively, on average, showing the ability to perform generally accurate reconstruction. Finally, the reconstructed Chl-a data showed a relatively stable fluctuation compared with standard satellite Chl-a data, and tended to be underestimated due to the expansion of the observable regions. We expect that securing these relatively stable and accurate estimates will be significantly different from the time-series data composed of standard Chl-a estimates, enabling more accurate variability and trend analysis.

Field observations were combined with a coupled hydrodynamic and water quality model to investigate the spatial and temporal variation in Chlorophyll-a (Chl-a) in Jinmeng Bay, China. The relatively high Chl-a values were distributed in the inshore waters, mainly due to the abundant nutrient inflow from the Tanghe River. The model’s results indicate that the Chl-a concentration was much higher in seaweed beds surrounded by artificial islands and reefs under the southeasterly wind, largely due to the fact that pollutants are prone to accumulate in coastal areas where flow is attenuated by the presence of natural and artificial marine structures. It was also found that the southwesterly winds suppress the inflow of nutrients from the Tanghe River to the coastal areas, and, therefore, lower the Chl-a levels. River input and wind forcing are the major factors that influence Chl-a concentrations in the anthropogenically influenced bay. This finding provides useful guidance for the prediction and mitigation of green tides in Jinmeng Bay in the future.

The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, was tested for transitional and coastal waters of the Lithuanian Baltic Sea and the Ukrainian Black Sea during 2006–2019. Among three tested neural network-based processors (FUB-CSIRO, C2RCC, standard Level-2 data), the FUB-CSIRO applied to Sentinel-3 OLCI images was the most appropriate for the retrieval of chlorophyll-a in both seas (R2 = 0.81). Based on 147 combined MERIS and OLCI synoptic satellite images for the Baltic Sea and 234 for the Black Sea, it was shown that the CyaBI corresponds to the eutrophication patterns and trends over the open, coastal and transitional waters. In the Baltic Sea, the cyanobacteria blooms mostly originated from the central part and the outflow of the Curonian Lagoon. In the Black Sea, they occurred in the coastal region and shelf zone. The recent decrease in bloom presence and its severity were revealed in the areas with riverine influence and coastal waters. Intensive blooms significantly enhanced the short-term increase in sea surface temperature (mean ≤ 0.7 °C and max ≤ 7.0 °C) compared to surrounding waters, suggesting that EO data originating from thermal infrared sensors could also be integrated for the ecological status assessment.

Tropical regions experience a diverse range of dense clouds, posing challenges for the daily reconstruction of chlorophyll-a concentration data. This underscores the pressing need for a practical method to reconstruct daily-scale chlorophyll-a concentrations in such regions. While traditional data reconstruction methods focus on single variables and rely on specific factors to infer missing data at specific locations, these single-variable methods may falter when applied to tropical oceans due to the scarcity of available data. Fortunately, all oceanographic variables undergo similar atmospheric and marine dynamic processes, creating internal relationships between them. This allows for the reconstruction of missing data through correlations between variables. Thus, this study introduces a multivariate reconstruction approach using the extended data interpolating empirical orthogonal function (ExDINEOF) method to reconstruct missing daily-scale chlorophyll-a concentration data. The ExDINEOF method considers the simultaneous relationships among multiple variables for data reconstruction in tropical oceans. To verify the method’s robustness, missing data were reconstructed during the formation and passage of severe tropical cyclone Hudhud through the Bay of Bengal. The results demonstrate that ExDINEOF outperforms traditional data reconstruction methods, exhibiting favorable spatial distribution and enhanced accuracy within the dynamic tropical marine environment. Furthermore, an assessment of marine physical environmental factors associated with chlorophyll-a concentration data provides additional evidence for the ExDINEOF method’s accuracy. Notably, the ExDINEOF method offers comprehensive spatial distribution aligned with underlying physical mechanisms governing phytoplankton distribution patterns, detailed phytoplankton growth, bloom, extinction variations in time series, satisfactory accuracy, and comprehensive local-level details.

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

Chlorophyll-a concentration is among the most important parameters used to investigate the primary production in marine ecosystems. Chlorophyll-a concentration is influenced by climatic/environmental factors, including Sea Surface Temperature (SST). Satellite sensors are able to provide reliable products of chlorophyll-a concentration and SST data with an adequate spatiotemporal resolution. In this study, level 3 (resolution 4 km) MODIS monthly products of chlorophyll-a concentration and SST from year 2003 to 2018 of the Persian Gulf and the Oman Sea were used. The data were transformed into a raster format and the values of each pixel were extracted. Several gaps were observed in MODIS products of chlorophyll-a concentration. The Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm was used to reconstruct these gaps. Pearson correlation procedure was applied to analyze the correlation between chlorophyll-a concentration and SST. Results showed that during the study period, the correlation between the two parameters was negative (-0.44 to -0.67) and significant in the most part of the study area. The correlation coefficients in December were negative (-0.67 to -0.05) and significant in the Oman Sea and the most areas of the Persian Gulf, while in July those relationships were mostly positive (0.13 to 0.64) and significant.

Material exchange between oceans and currents is crucial for biogeochemical processes in marginal seas, such as the Kuroshio intrusion in the Luzon Strait, which contributes to nutrient and phytoplankton growth in the adjacent water. Therefore, it is necessary to understand changes in major current systems and their possible effects on marine ecology. In this study, we applied an edge-detection method to satellite-derived sea-surface temperature (SST) images to identify the SST front as the index of Kuroshio intrusion during 1985–2017. Chlorophyll-a (Chl-a) concentrations and geostrophic currents (determined from satellite observations after 1993) were examined by comparing long-term variations related to the Kuroshio front using empirical-mode decomposition analysis. The Kuroshio intrusion into the Luzon Strait gradually decreased after the 1990s, as did the Chl-a concentrations and current speed in the Strait. In contrast, the speed of the Kuroshio Current in the eastern Philippine tended to increase, indicating that the northward Kuroshio Current from the eastern Philippines became stronger, curtailing the westward intrusion of the branch current into the Luzon Strait. The speed of the Kuroshio Current correlated with the bifurcation of the North Equatorial Current (NEC), which is upstream of the Kuroshio Current starting from the southeast Philippines. Therefore, the NEC bifurcation latitude may have shifted more southward, which strengthened the water mass and current speed of the Kuroshio, resulting in decreased westward motion of the Kuroshio through the Luzon Strait. Consequently, the weakening Kuroshio intrusion may have caused a decreasing trend of biogeochemical processes in the South China Sea.

As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance (Rrsλ) products. Firstly, the GOCI Rrsλ correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.916, a root mean square error (RMSE) of 0.212 mg·m−3, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg·m−3. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg·m−3. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI’s potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations.

When it is deposited in the ocean, volcanic ash has the potential to release iron and other nutrients into surface water to stimulate ocean productivity. In the western South Pacific Ocean (SPO), one of the most important volcanic ash deposition regions, occasional widespread transport of volcanic ash may supply the nutrients not only locally around source islands but also within the wider the western SPO, accompanied by phytoplankton response. Through a comparative analysis of satellite and reanalysis data for the past 19 years (2004–2022), this study reveals that four explosive volcanic eruptions, Rabaul volcano, Papua New Guinea (October, 2006), Ambae volcano, Vanuatu (July, 2018), Ulawun volcano, Papua New Guinea (June, 2019), and Hunga volcano, Tonga (January, 2022), had the most strong stratospheric injection (>15 km) and mass loading of volcanic materials over the wider the western SPO (covering an area of >765,000 km2). The transport of 2006, 2018, 2019 volcanic emissions, was not likely associated with significant ash deposition over the western SPO. However, the Hunga eruption led to the deposition of ash-laden volcanic plumes over a wide area (~2,000 km from source), and was followed by the increase in chlorophyll-a concentrations (Chl-a) in the region (~70% increase). Minor changes related to other nutrient sources (e.g., hydrothermal input) suggest a link between the increase in Chl-a and 2022 Hunga ash falls over the western SPO. Our results indicate that volcanic ash deposition has implications for phytoplankton productivity in the western SPO, and highlights the need for further research into understanding how nutrient supply alleviated limitations of phytoplankton at the community level.

Chlorophyll-a concentration is among the most important parameters used to investigate the primary production in marine ecosystems. Chlorophyll-a concentration is influenced by climatic/environmental factors, including Sea Surface Temperature (SST). Satellite sensors are able to provide reliable products of chlorophyll-a concentration and SST data with an adequate spatiotemporal resolution. In this study, level 3 (resolution 4 km) MODIS monthly products of chlorophyll-a concentration and SST from year 2003 to 2018 of the Persian Gulf and the Oman Sea were used. The data were transformed into a raster format and the values of each pixel were extracted. Several gaps were observed in MODIS products of chlorophyll-a concentration. The Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm was used to reconstruct these gaps. Pearson correlation procedure was applied to analyze the correlation between chlorophyll-a concentration and SST. Results showed that during the study period, the correlation between the two parameters was negative (-0.44 to -0.67) and significant in the most part of the study area. The correlation coefficients in December were negative (-0.67 to -0.05) and significant in the Oman Sea and the most areas of the Persian Gulf, while in July those relationships were mostly positive (0.13 to 0.64) and significant. [ABSTRACT FROM AUTHOR]

We evaluated the temporal and spatial trends of the hydrological (temperature and sea ice) and biochemical (chlorophyll-a concentration) characteristics in springtime in the Baltic Sea. Both are strongly affected by climate change, resulting in a decrease in the duration of sea-ice melting in the previous decade. A new regime of sea ice began in 2008 and in all basins of the Baltic Sea, a rapid warming during spring could be detected. Using satellite data, the temporal and spatial variations in spring bloom were analysed during severe and warmer winters. Using a coupled hydrodynamic-biogeochemical model, we tested the response of spring bloom to the changing ice conditions. The results of the modelling indicated that the presence of ice significantly influences the predicted chlorophyll-a concentration values in the Baltic Sea. Therefore, it is necessary that any coupled model system has a realistic ice model to ensure the best simulation results for the lower trophic food web as well.

• The global day-night cycle can be denoted as an exponential plus polynomial function. • The satellite can lead to huge overestimation of Chla due to diurnal variations. • The diurnal variations impacts on data quality of downstream satellite products. Over the last three decades, ocean color satellites have recorded continuous change in phytoplankton concentration, and in doing so, the satellites have helped marine remote sensing to become an emerging discipline. However, ocean color satellite has provided the state of global phytoplankton only around midday whereas phytoplankton have significant diurnal variation characteristics over an entire day-night cycle, which would lead to large estimation uncertainty in downstream products. A large global field observations show that the diurnal variation curves can be constructed as the sum of an exponential and a polynomial function. After correction with our diurnal variation curves, the intermission difference between MODISA and MODIST phytoplankton product could be decreased from 7.57% to 6%, which is critical to monitor the long-term changing trend with multi-mission data. When the satellite-recorded chlorophyll-a concentration is simply equal to a daily average value, it results in overestimating uncertainties of 16% and 54% for the northern and southern hemispheric oceans, respectively. Moreover, as a typical example of downstream product of the chlorophyll-a, the coverage of oligotrophic waters can oscillate 76% from day to night simulated from satellite images with our diurnal variation curves, further confirming the importance of global synchronous observation. The geostationary satellite might be an efficient regional tool for diurnal observation, but it must account for its limitations under the weak light conditions of early morning and late afternoon. In the real world, the effects of diurnal variations in ocean properties reach far beyond chlorophyll-a in the global oceans. We anticipate these quantitatively findings to be a starting point for quantitatively re-examining the proper application of ocean color products in scientific communication. [ABSTRACT FROM AUTHOR]

During the summer monsoon, the Somali region undergoes a significant upwelling phenomenon that enhances plankton productivity, thereby benefiting fisheries. Wind and coastal dynamics initially drive this upwelling, but eventually, eddy flows influence it. Our study explores the interplay between ocean currents, eddies, and chlorophyll-a concentrations using the backward Finite-Size Lyapunov Exponents (bFSLEs) technique. We also delve into the specific role of Ekman transport in distributing chlorophyll-a across the region. The Great Whirl (GW), an anticyclonic eddy, predominantly causes strong downwelling, interrupting the summer monsoon upwelling along the Somali coast longitudinally. Despite the GW's significant impact on moving upwelled water offshore, the influence of downwelling diminishes northward. As a result, the northern Somali coast, especially around 9°N and 10°N, showcases the most extensive offshore upwelling, reaching as far as 55°E. Our findings highlight a robust connection between chlorophyll-a levels and oceanic dynamics, influenced by both currents and eddies, as evidenced by bFSLEs, and by cross-shore Ekman transport, particularly within chlorophyll-a concentrations ranging from 0.2 to 0.6 mg m−3. The data suggests that Ekman transport-induced upwelling primarily drives coastal phytoplankton biomass. Furthermore, bFSLEs analysis underlines the supportive role of ocean currents and eddies in the offshore distribution of chlorophyll-a, especially near the coast. Further examination of lagged correlations reveals a temporal lag between peak concentrations of chlorophyll-a and Ekman transport; the lag increases offshore and is at least 9 days near the coast. [ABSTRACT FROM AUTHOR]

The oceanic waters of the Southwest Tropical Pacific occupy a vast region including multiple Pacific Island Countries. The state of these waters is determinant for fisheries and the blue economy. Ocean color remote sensing is the main tool to survey the variability and long-term evolution of these large areas that are important for economic development but are affected by climate change. Unlike vast oligotrophic gyres, tropical waters are characterized by numerous archipelagos and islands, with deep and shallow lagoons subjected to the large impacts of the land. Strikingly large dendritic phytoplankton (Trichodesmium) blooms with high levels of chlorophyll, developing within archipelagos, as well as coastal enrichments from various origins may be observed. Algorithms to detect the presence of Trichodesmium have been developed or adapted, as well as algorithms to estimate the chlorophyll concentration ([Chl-a)]. Adapting existing [Chl-a] algorithms does not always yield high, i.e., sufficient, accuracy. A review of published regional bio-optical algorithms developed taking into account the specific phytoplankton composition and minimizing the adverse impacts of particles and the seabed bottom on [Chl-a] determination is presented, as well the bio-optical database that allowed their development. The interest of such algorithms for a variety of applications and scientific accomplishments is highlighted, with a view to further addressing the main biology and biogeochemistry questions, e.g., to determine the true impact of diazotrophs and assess lagoon [Chl-a] variability with the highest confidence. This work anticipates the use of future coarse and high-spatial-resolution and multi- and hyper-spectral satellite imagery in the Pacific.

The relationship between environmental conditions and early life-history traits of Sardinella aurita are investigated using material collected in two sites of the Central Mediterranean Sea. Individual mean daily growth during the planktonic phase has been determined by using otolith microstructure analysis, while Lagrangian simulation models allowed to estimate the daily position in space and time of each specimen from the hatching to the catch. Generalized Additive Mixed Models (GAMMs) have been implemented to explore the impact of environmental conditions at time t, t-1 day and t-2 days on the mean daily growth rate occurring at time t. Spatial analysis evidenced a wide dispersion of eggs and larvae in the coastal area of both sampling sites in correspondence to relatively warmer and chlorophyll-a enriched waters. Lagrangian simulations detected a complementary larval dispersal pathway able to transport larvae to a known retention area. Temperature at time t was the most important driver affecting the mean daily larval growth, followed by the food availability. On the other hand, models performed on lagged environmental covariates (t-1 and t-2) did not show any significant effect on the growth rate at time t. In addition to the sub-linear positive correlation between temperature and mean daily larval growth, model highlighted a decrease in the otolith core width at higher temperature that can be linked to an earlier stage of ontogeny at hatching. This study provided a useful methodological approach that takes advantage of available remote sensing data to perform ecological studies in support to fisheries management.

The chlorophyll-a concentration (CHL) data observed with the ocean color sensors have been widely used in the various studies related to phytoplankton. However, irregularly distributed missing data induced by clouds, a unique nature of optics, can cause large uncertainty, and a solution to this missing issue has been continuously demanded until now. We investigated the applicability of the data interpolating empirical orthogonal function and evaluated the reconstruction results in the southwestern East Sea. A total of 311 decomposed modes were used, showing a coefficient of determination of about 0.86 and a root mean square error of 0.37 mg m−3, compared to the truth data. Overall, it was confirmed that the observed CHL was overestimated compared to the reconstructed CHL when the spatio-temporal average was taken.

Using satellite observations from 2003 to 2020 and cruise observations in 2019 and 2021, this study reveals an unexpected minor role of upwelling in seasonal and interannual variations in chlorophyll-a (Chl-a) concentrations in the coastal upwelling region east of Hainan Island (UEH) in the northwestern South China Sea (NWSCS). The results show strong seasonal and interannual variability in the Chl-a concentration in the core upwelling area of the UEH. Different from the strongest upwelling in summer, the Chl-a concentration in the UEH area reaches a maximum of 1.18 mg m-3 in autumn and winter, with a minimum value of 0.74 mg m-3 in summer. The summer Chl-a concentration increases to as high as 1.0 mg m-3 with weak upwelling during El Niño years, whereas the maximum Chl-a concentration in October increases to 2.5 mg m-3 during La Niña years. The analysis of environmental factors shows that compared to the limited effects of upwelling, the along-shelf coastal current from the northern shelf and the increased precipitation are crucially important to the Chl-a concentration variation in the study area. These results provide new insights for predicting marine productivity in upwelling areas, i.e., multiple mechanisms, especially horizontal advection, should be considered in addition to the upwelling process. [ABSTRACT FROM AUTHOR]

The chlorophyll-a concentration (CHL) is an essential climate variable. Extremes of CHL events directly reflect the condition of marine ecosystems. Here, we applied the statistical framework for defining marine heatwaves to study the extremes of winter CHL blooms off the Luzon Strait (termed as LZB), northeastern South China Sea (SCS), from a set of remote sensing data. The application was enabled by a recent gap-free CHL dataset, the SCSDCT data. We present the basic properties and the long-term trends of these LZB events, which had become fewer but stronger in recent years. We further statistically analyze the LZB events' controlling factors, including the submesoscale activity quantified by a heterogeneous index or surface temperature gradients. It was revealed that the submesoscale activity was also a vital modulating factor of the bloom events in addition to the well-understood wind and upwelling controls. This modulation can be explained by the stratification introduced by submesoscale mixed-layer instabilities. In the winter, the intensified winter monsoon provides a background front and well-mixed upper layer with replenished nutrients. During the wind relaxation, submesoscale baroclinic instabilities developed, leading to rapid stratification and scattered submesoscale fronts. Such a scenario is favorable for the winter blooms. For the first time, this study identifies the bloom events in a typical marginal sea and highlights the linkage between these events and submesoscale activity. Furthermore, the method used to identify extreme blooms opens up the possibility for understanding trends of multiple marine extreme events under climate change. [ABSTRACT FROM AUTHOR]

Indonesia territorial waters are very vulnerable to the impacts of climate change. Research about the variations of sea surface temperature and chlorophyll-a concentration in the southern waters of Java has been undertaken by several researchers. However, the research is still in the scope of regional scale south of Java. This research was conducted to determine the impact of climate change on sea surface temperature and chlorophyll-a concentration in Sukabumi waters. The data used consisted of IOD index anomalies, sea surface temperature and monthly chlorophyll-a concentrations from December 2002 to November 2020. Descriptive analysis was used to determine seasonal and inter-annual variability and linear regression method was used to analyze trends in changes in sea surface temperature (SST) and chlorophyll-a concentration. The results showed that the seasonal variation was influenced by the monsoon, while the interannual variation was influenced by the Indian Ocean Dipole. Climate change causes SST and chlorophyll-a concentrations to increase. In the 2003-2020 time period, SPL increased by 0.08 °C and an increase in chlorophyll-a concentration by 0.03 mg/ m3. ABSTRAK Perairan selatan Sukabumi yang berhadapan langsung dengan samudera Hindia sangat rentan terhadap dampak perubahan iklim. Penelitian mengenai variasi suhu permukaan laut dan konsentrasi klorofil-a di perairan selatan Jawa sudah dilakukan oleh beberapa peneliti sebelumnya. Namun penelitian tersebut masih dalam lingkup skala regional selatan Jawa. Penelitian ini dilakukan untuk mengetahui dampak perubahan iklim terhadap suhu permukaan laut dan konsentrasi klorofil-a di Perairan selatan Sukabumi. Data-data yang digunakan terdiri dari indeks IOD, suhu permukaan laut dan konsentrasi klorofil-a bulanan periode Desember 2002 hingga November 2020. Metode yang digunakan dalam penelitian ini adalah analisis deskripsi dan regresi linier. Hasil penelitian menunjukkan bahwa variasi musiman suhu permukaan laut dan konsentrasi klorofil-a di perairan ini dipengaruhi oleh monsun, sedangkan variasi antar tahunan dipengaruhi oleh Indian Ocean Dipole. Perubahan iklim menyebabkan suhu permukaan laut dan konsentrasi klorofil-a mengalami kenaikan. Dalam periode waktu 2003-2020 suhu permukaan laut mengalami kenaikan sebesar 0,08°C dan kenaikan konsentrasi klorofil-a sebesar 0,03 mg/m3.

In-situ fluorescence is a widely used method to estimate the chlorophyll-a (Chla) concentration, a proxy of the phytoplankton biomass. With the emergence of autonomous platforms such as BioGeoChemical-Argo (BGC-Argo) profiling floats, its use has expanded to global scale observations. However, the relationship between in-situ fluorescence and Chla may vary significantly, leading to major discrepancies between oceanic regions. This study aims to investigate the main sources of the natural variability in the in-situ fluorescence signal in the global open ocean, specifically the influence of the phytoplankton community composition. In this view, we analyzed a combination of three datasets comprising concomitant measurements of in-situ fluorescence, pigment concentrations and phytoplankton absorption spectra. Two datasets cover several contrasted bioregions of the global ocean whereas the third one consists of a regional time series in the northwestern Mediterranean Sea, which allows to examine the effect of phytoplankton community composition on the fluorescence signal on the global, seasonal and vertical scales. We studied the variability of the two major drivers of the natural variability of the fluorescence process, i.e. the light absorption and the fluorescence quantum yield of phytoplankton, in regards of the variability of the pigment composition of the communities. The community composition correlates substantially with the Chla-to-fluorescence ratio, with high fluorescence values associated with phytoplankton communities dominated by large cells. This trend may be explained by the combined effects of the community composition on the phytoplankton absorption coefficient and the fluorescence quantum yield, and is consistently observed globally, seasonally and vertically. Non-photosynthetic pigments also appear to play a critical role in oligotrophic surface waters, leading to a reduction of the quantum yield of fluorescence. The results indicate that the phytoplankton community composition plays a key role in the relationship between the in-situ fluorescence signal and Chla concentration. Therefore, we suggest that taking into account the composition of phytoplankton communities in the retrieval of the Chla concentration from current in-situ fluorometers, those mounted on BGC-Argo floats in particular, would lead to a better estimation of the phytoplankton biomass on a wide range of spatial and temporal scales.

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

Seasonal and inter-annual absorption properties of phytoplankton and non-algal particulate matter were studied in relation to phytoplankton biomass, as indexed by chlorophyll-a concentration, and presence of diatoms, as indexed by fucoxanthin concentration, using a 20-year time series of in situ data collected in the Northwest Atlantic. We found significant differences in the spatiotemporal variations of the bio-optical properties for three oceanic regimes: mesotrophic (Scotian Shelf), oligotrophic (Northwest Atlantic Basin, NAB), and subartic (Labrador Sea). The Scotian Shelf and NAB exhibited similar phenology with the spring and autumn blooms associated with low phytoplankton specific absorption, while only relatively high fucoxanthin concentration occurred in spring. The NAB showed a smaller seasonal variation than the Scotian Shelf in agreement with its oceanic conditions. The Labrador Sea showed a single phytoplankton bloom in spring followed by a continuous decrease in biomass the rest of the year. The relationship between phytoplankton absorption coefficient at 443 nm and chlorophyll-a concentration was consistent with other studies with coefficients that were region-dependent. Absorption by non-algal particulate matter remained between 5% and 60% of phytoplankton absorption with a mean of 15%. The slope of the non-algal particulate absorption varied with seasons and regions and appeared to depend on the trophic status with high values (i.e., up to 0.04) occurring during bloom conditions. We also introduced a new index, the phytoplankton apparent absorption wavelength (PAAW), a wavelength-weighted sum of absorption expressed in nanometers that provides information on the phytoplankton biomass and assemblage in a simple manner. Time series analysis of the PAAW revealed a decrease of this property in spring on the Scotian Shelf, NAB, and Labrador Sea and an increase in autumn on the Scotian Shelf and NAB, suggesting a shift in these ecosystems.

Predator–prey interactions are a key ecological process which can be modified by environmental conditions over a range of spatial scales. Through two complementary short-term experiments, we assessed how local and large-scale environmental conditions affect a subtropical intertidal predator–prey interaction. At a local scale, we evaluated the effects of the degree of exposure to wave action and prey density on consumption rate and interaction strength using a whelk–barnacle system. Consumption rate decreased with wave exposure at experimentally reduced prey density but did not change at ambient density. Such an interactive effect occurred due to shifts in the whelk's feeding behaviour, likely linked to encounter rate and stress amelioration underpinned by prey density. Per capita interaction strength of the whelk on the barnacle weakened along the wave exposure gradient, but to a greater degree at reduced compared to ambient prey density. This confirms that environmental harshness can decrease the importance of predators, but the magnitude of change may be modified by density-dependent effects. A large-scale experiment did not reveal spatial patterns in the whelk–barnacle interaction, nor relationships to chlorophyll-a concentration or the minor change in sea temperature across the study area. Patterns in the size of consumed barnacles along the chlorophyll-a gradient suggest changes in food choice related to prey quality and size. We conclude that disentangling the effects of wave exposure and prey density revealed important potential mechanisms driving species locally. Large-scale variation in the whelk–barnacle interaction appeared to be linked to species' traits shaped by the environmental context. [ABSTRACT FROM AUTHOR]

Satellite observations are an integral component of long-term Arctic Ocean monitoring and help identifying changes resulting from climate warming. A Self-Organizing Maps (SOM) approach was applied to four-day composite satellite images of the Eastern Beaufort Sea (EBS) acquired by the MODerate resolution Imaging Spectroradiometer over the period 2003–2019. Using sea-surface temperature (SST), suspended particulate matter concentration (SPM) and chlorophyll-a concentration (Chl-a) as input the EBS was partitioned into six biogeochemical regions. The SOM approach revealed region-specific mean conditions and seasonal cycles for all properties, particularly for SPM and Chl-a. Three of the six regions, located on the continental shelf, had the highest SST, SPM and Chl-a with earlier maxima compared to the remaining three regions which comprised the shelf edge, Canada Basin and Amundsen Gulf. While mean and maximum SST did not exhibit significant trends over the 17 years of observations, the annual maximum SST in Amundsen Gulf was reached significantly earlier in recent years compared to the start of the time series. With the exception of Amundsen Gulf, sea-ice concentration (SIC) derived from microwave satellites declined throughout the study area; monthly trends showed dramatic SIC declines in regions on the shelf during May and June, and in Canada Basin during August. Correlation analysis of properties within and between regions showed that SST and SIC were driven by large scale processes while SPM and Chl-a showed regional features. SST and Chl-a in the regions nearest the Mackenzie River showed a strong relationship during seasonal warming. The SOM approach, applied to 17 years of satellite data, revealed spatially distinct marine units with unique characteristics, emphasizing the need for regional considerations when assessing the impact of climate warming in the Arctic Ocean.

Asian dust deposition is an important source of nutrients to the Pacific Ocean, when aerosol dust is deposited into the ocean, it will affect the biological productivity and hence climate. In this paper, we analyzed a dust process that occurred in the Taklimakan Desert during 21–25 May 2019 by employing multi-sensor satellite observations and the WRF-Chem model. It is found that dust aerosols rise in the Taklimakan Desert, moving eastward at high altitudes under the role of the westerly winds, passing over the downwind regions, and deposition in the Pacific Ocean. Dust aerosol deposition results in an increase of chlorophyll-a (Chl-a) concentrations and particulate organic carbon (POC) after 2 days, Chl-a concentrations and POC increase by 175 and 873%, respectively. Moreover, the values of Chl-a concentrations and POC are 256 and 644% higher than the 5-years average during the same period.

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. • The CNN-CBAM model demonstrates exceptional performance in estimating Chl-a concentration. • The CNN-CBAM model excels in mining non-linear data compared to traditional empirical models. • CNN-CBAM outperforms other machine learning models in extracting high-dimensional feature data. • The incorporation of the CBAM module enhances the performance of the CNN model. [ABSTRACT FROM AUTHOR]

We measured the monthly egg production rate (EPR) of Paracalanus parvus s. l. (P. parvus) at a fixed station in Busan Harbor from January 2020 to December 2021 to elucidate the seasonal variation in the EPR and the factors involved. Over the 2 years, the monthly EPR ranged from 0.5 to 47.0 eggs female−1 d−1 (average 13.3 eggs female−1 d−1). The seasonal mean EPR was high in spring (April to June) and summer (July to September) and low in autumn (October to December) in both years. Egg hatching success was high (average 94%). Monthly nauplii production in the P. parvus population ranged from 9 to 37,123 ind. m−3 d−1 (average 4605 ind. m−3 d−1), with a peak in March and May in 2020 and in April in 2021. The monthly EPR of P. parvus was more dependent on chlorophyll-a concentration than on water temperature. Nauplii production was dependent on the abundance of adult females, while the chlorophyll-a concentration had a positive effect on nauplii production. In this study, while there was no clear seasonal variation in the abundance of adult females, the EPR showed a clear pattern of seasonal variation. These results imply that for monitoring potential environmental changes in Busan Harbor, measuring the EPR of copepods may be a better indicator than copepod abundance.

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments. [ABSTRACT FROM AUTHOR]