Chl-a Concentration Research Articles

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (Rrc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R2 = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R2 = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters.

Monitoring of chlorophyll-a and suspended sediment concentrations in optically complex inland rivers using multisource remote sensing measurements

In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change and human activities. Given the generally narrow width of rivers and current high spatial resolution satellites, which are limited by band settings, bandwidth, and the signal-to-noise ratio, UAVs with their exceptional spatiotemporal resolution can be used as a useful tool for river environmental monitoring and inversion uncertainty assessment. In this study, UAV-based hyperspectral (X20P) and multispectral (P4M) images, along with Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 data, were used to assess the uncertainty in retrieving chlorophyll-a (Chla) and suspended sediment (SS) concentrations in rivers. Chla and SS models based on UAV and satellite data were constructed using stepwise multiple regression and typical Chla and SS retrieval algorithms, respectively, and the performance of the models was the focus of our research. The results demonstrated that in the Chla concentration inversion, each sensor performed as follows: X20P > P4M > Landsat9 OLI2 > Sentinel-2 MSI > Landsat8 OLI, and the performance in the SS concentration inversion was as follows: X20P > Sentinel-2 MSI > P4M > Landsat9 OLI2 > Landsat8 OLI. In addition, the uncertainty of high spatial resolution satellite retrievals was analyzed with the assistance of the UAV-based model. Results showed that narrow bandwidths and finely tuned band settings are more essential for the Chla inversion. The typical Chla retrieval algorithm, NDCI, is only effective in certain bands (band 1 from 684 to 724 nm and band 2 from 660 to 680 nm). It is also noted that Landsat8 and Landsat9 lack some key band settings (e.g., the red-edge band of 700–710 nm), severely limiting practical application in relation to Chla. However, specific variances in different sensor bands have a relatively small impact on SS inversion, for example, the correlation between SS and the R/B (a typical SS retrieval algorithm) constructed by each sensor ranged from 0.68 to 0.77. Chla monitoring, on the other hand, necessitates a higher spatial resolution than SS monitoring. The accuracy decreased markedly when UAV images were resampled to 10 m and 30 m spatial resolution. However, it is not as crucial for the SS inversion, images with the original spatial resolution (RMSE<30cm = 6.28 mg/L) were resampled to 10 m resolution (RMSE10m = 5.85 mg/L) and 30 m resolution (RMSE30m = 4.08 mg/L) while using P4M for SS inversion, and the accuracy increased. Our results demonstrated and highlighted various options for future monitoring of Chla and SS, while exploiting the synergy between UAVs and satellites to achieve more precise observations at greater spatial and temporal scales, which will benefit aquatic environment management and protection.

A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data

Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.

Anomalous trend of chlorophyll-a as a response to mesoscale phenomena in a Brazilian oligotrophic ria

Saco do Mamanguá is a tropical Brazilian ria characterized by low nutrient availability and subject to seasonal thermocline, which is mainly influenced by the intrusion of South Atlantic Central Water (SACW) on the continental shelf. Sampling was performed in summer (2014, 2015) and spring (2017). The dissolved inorganic and organic nutrients (nitrogen [N] and phosphorus [P]), along with chlorophyll-a (Chl-a), were evaluated to characterize this area. As a result, the general trophic condition of Saco do Mamanguá is a meso-oligotrophic ria with low Chl-a concentrations. Due to the presence of an atmospheric phenomena that causes an extremely dry period during the summer of 2014, a strong thermocline was observed in the water column which was associated with high Chl-a concentrations (median of 6.14 mg m−3). These values were much higher than those observed during the summer of 2015 and in the spring of 2017 (median of 1.03 mg m−3). The dissolved inorganic nutrients presented low values in all studied periods with concentrations of nitrate (0.00–0.20 μmol L−1) and phosphate (0.00–0.20 μmol L−1). A wide range of dissolved organic compounds were also observed, as shown by the presence of dissolved organic carbon (DOC), which varied from 63 to 212 μmol L−1; dissolved organic nitrogen (DON), which varied from 2.97 to 12.92 μmol L−1; and dissolved organic phosphorus (DOP), which reached a maximum value of 0.63 μmol L−1. DON and DOP represented at least 50% total dissolved N and P. In this region, the biogeochemical cycles of N and P could ensure the availability of regenerated nutrients from organic sources to support primary production of phytoplankton.

Potential fishing grounds of Skipjack tuna (Katsuwonus pelamis) in western water of Sumatera using remotely sensed data and maximum entropy model

The fishery of skipjack tuna (Katsuwonus pelamis) was collected from the vessel monitoring system (VMS) as well as satellite oceanographic data for salinity, chlorophyll-a (chl-a) concentrations, and sea surface temperature (SST) used to determine the effects of oceanographic factors on potential fishing grounds for skipjack tuna in the western water of Sumatera (WWS). Salinity, chl-a and SST data were downloaded from the INDESO project website. The relationship between these parameters and the potential skipjack fishing area was analyzed using a maximum entropy (maxent) model. The Maxent model demonstrated its potential to predict the spatial distribution of skipjack, expressed as the area under the 0.852 curve value. In addition, jackknife test results indicate that the spatial pattern of fishery potential was influenced mainly by chl-a (0.50), followed by salinity (0.43) and SST (0.10). The integration of multi-sensor remote sensing data and modelling methods provides an ingenious way to determine a potential skipjack catch in the western water of Sumatera.

Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

Accurate estimation of harmful algal blooms is imperative for the protection of surface water. Chlorophyll-a (Chl-a) which is used as a proxy for estimating the algal concentration, is affected by a wide range of weather and physicochemical factors that act at varying spatial and temporal scales. Deep learning (DL) based models such as Long-Short Term Memory (LSTM) and Convolution Neural Networks (CNNs) have shown promising results for hydrological and Chl-a simulations. Recently several variants of LSTM and CNNs have been developed which can model highly non-linear relationships between input and target data. Therefore, these advanced DL methods have the potential for long-term simulation of Chl-a. Previous DL-based studies on Chl-a simulation have developed site-dependent models. This indicates that the DL models were trained and evaluated using data from the same site, making it difficult to apply these models to other sites. Development of site-independent models requires a more robust training strategy which can result in DL models that can be evaluated in new novel situations. To address these issues, we propose a DL-based framework which can incorporate irregularly measured water quality observations, static physical features, and climate data measured at constant time steps. In this framework, we compared the performance of six state of the art DL methods which include (1) LSTM, (2) CNN, (3) Temporal Convolution Networks (TCN), (4) CNN-LSTM, (5) LSTM based autoencoder, and (6) input-attention LSTM (IA-LSTM). The IA-LSTM is an explainable DL method which can select important hydrologic, climatic and water quality parameters for Chl-a prediction. Our results indicate that the IA-LSTM exhibited the best performance, with an R2 value of 0.85 at the training site and 0.52 at the test site. We showed that attention-based deep learning models improve the prediction performance and make the black-box deep learning models interpretable and explainable. The attention-based deep learning models indicated that Chl-a concentration in the Nakdong River was strongly affected by climate factors during the previous three days. The proposed DL framework can be adopted to develop regional water quality models using deep learning.

Dynamics of environmental variables during the incidence of algal bloom in the coastal waters of Gujarat along the northeastern Arabian Sea.

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

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data

Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, we adopted four deep learning approaches, including Convolutional LSTM Network (ConvLSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Eidetic 3D LSTM (E3D-LSTM), and Self-Attention ConvLSTM (SA-ConvLSTM) models, to predict Chl-a over the Yellow Sea and Bohai Sea (YBS) in China. Furthermore, 14 environmental variables obtained from the remote sensing data of Moderate-resolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis v5 (ERA5) were utilized to predict the Chl-a concentrations in the study area. The results showed that all four models performed satisfactorily in predicting Chl-a concentrations in the YBS, with SA-ConvLSTM exhibiting a closer approximation to true values. Furthermore, we analyzed the impact of the Self-Attention Memory Module (SAM) on the prediction results. Compared to the ConvLSTM model, the SA-ConvLSTM model integrated with the SAM module better captured subtle large-scale variations within the study area. The SA-ConvLSTM model exhibited the highest prediction accuracy, and the one-month Pearson correlation coefficient reached 0.887. Our study provides an available approach for anticipating Chl-a concentrations over a large area of sea.

Impact of tropical Indian Ocean warming on the surface phytoplankton biomass at two significant coastal upwelling zones in the Arabian Sea

The Sea Surface Temperature (SST) of the Arabian Sea undergoes large seasonal variations owing to the monsoonal forcing and upwelling. Warming of the ocean adversely affects its biological productivity. The present study examined the role of the rapid warming of the tropical Indian Ocean and associated changes in physical forcing parameters on phytoplankton biomass in the Arabian Sea. SST during the summer monsoon period (June-September) of 1971–2020 showed an increasing trend of 0.6 °C in the southeastern Arabian Sea (SEAS) and 1.6 °C in the northwestern Arabian Sea. During the recent summer monsoon period of 1998–2020, surface chlorophyll-a (chl-a) concentration showed a decreasing trend of – 0.32 mg m−3 in the SEAS and an increasing trend of 0.56 mg m−3 in the northwestern Arabian Sea. High-resolution satellite and reanalysis data of physical forcing parameters such as surface winds, SST, Sea Level Anomaly (SLA) that influence the surface chl-a concentration at two distinct upwelling locations in the Arabian Sea, were analysed for the recent two decades (1998–2020). Multiple linear regression (MLR) analysis showed that SLA was the most important parameter that determines the surface chl-a variability in the SEAS, whereas alongshore wind stress was dominant in the northwestern Arabian Sea. An epochal analysis showed that in the most recent decade, SLA in the SEAS became less favourable for upwelling, whereas summer monsoon winds became increasingly favourable for upwelling in the northwestern Arabian Sea. These differences corroborate the contrasting trends in surface chl-a in the two locations in the Arabian Sea. The present study has shown that the inconsistent response of surface chl-a at distinct locations within the Arabian Sea depends on the relative strength of the influencing physical forcing mechanisms.

Influence of Denitrification on Cyanobacterial Blooms Trends in Lake Taihu, China

Denitrification is a major process in aquatic ecosystems, and it competes with cyanobacterial growth for nitrogen. However, the effect of denitrification on cyanobacterial blooms under the background of climate change remains unclear. This study explored the interaction between lake denitrification and formation of cyanobacterial blooms, using the historical water quality monitoring data of North Lake Taihu over five years from 2017 to 2021 and via incubation experiments of cyanobacteria and sediment denitrification. The monitoring data showed that algal biomass (Chla as a proxy) primarily peaked during summer and autumn. The seasonal variations in total N concentration showed a completely opposite trend than that of algal biomass, which peaked in winter and spring. Nitrate was the major component of dissolved inorganic nitrogen, and the nitrate concentration was approximately zero in summer and autumn. The total phosphorus concentration varied in the same way as the Chla concentration. The experimental results showed that Cyanobacteria did not grow when the temperature was below 20℃. In comparison, denitrification showed a significant linear relationship with temperatures between 10-25℃ (R2=0.99) and reached the maximum value of (62.98±21.36) μmol·(kg·h)-1 in Lake Taihu at 25℃. Additionally, the nitrate concentration threshold at the maximum denitrification rate was 4 mg·L-1. Cyanobacteria assimilate nitrate for growth, thereby reducing the concentration of nitrate required for denitrification. This study indicated that the advance in lake temperature warming due to climate change may result in earlier growth of cyanobacteria, thereby leading to large amounts of N being assimilated by algae before denitrification, further affecting the dynamics of cyanobacterial blooms. The present results are scientifically important for explaining the mechanism of cyanobacterial bloom rebound in Lake Taihu under the background of recent climate changes.

Vertical distributions of organic matter components in sea ice near Cambridge Bay, Dease Strait, Canadian Archipelago

Ice algae thriving within sea ice play a crucial role in transferring energy to higher trophic levels and influencing biogeochemical processes in polar oceans; however, the distribution of organic matter within the ice interior is not well understood. This study aimed to investigate the vertical distribution of organic matter, including chlorophyll a (Chl-a), particulate organic carbon and nitrogen (POC and PON), carbohydrates (CHO), proteins (PRT), lipids (LIP), and food material (FM), within the sea ice. Samples were collected from the bottom, middle, and top sections of the sea ice column near Cambridge Bay during the spring of 2018. Based on the δ13C signature, biochemical composition, and POC contribution of biopolymeric carbon (BPC), the organic substances within the sea ice were predominantly attributed to marine autotrophs. While the highest concentrations of each parameter were observed at the sea ice bottom, notable concentrations were also found in the upper sections. The average sea ice column-integrated Chl-a concentration was 5.05 ± 2.26 mg m−2, with the bottom ice section contributing 59% (S.D. = ± 10%) to the total integration. The column-integrated concentrations of FM, BPC, POC, and PON were 2.05 ± 0.39, 1.10 ± 0.20, 1.47 ± 0.25, and 0.09 ± 0.03 g m−2, respectively. Contributions of the bottom ice section to these column-integrated concentrations varied for each parameter, with values of 20 ± 6, 21 ± 7, 19 ± 5, and 28 ± 7%, respectively. While the bottom ice section exhibited a substantial Chl-a contribution in line with previous studies, significantly higher contributions of the other parameters were observed in the upper sea ice sections. This suggests that the particulate matter within the interior of the sea ice could potentially serve as an additional food source for higher trophic grazers or act as a seeding material for a phytoplankton bloom during the ice melting season. Our findings highlight the importance of comprehensive field measurements encompassing the entire sea ice section to better understand the distribution of organic carbon pools within the sea ice in the Arctic Ocean.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

ABSTRACTInland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Multi-omics integration analysis of long-distance drifting process of green tides in the Yellow Sea simulated in a large-volume flowing water system

The drifting process of U. prolifera were simulated in a large-volume flowing water system with conditions similar to the field in the Yellow Sea. Biomass and chl-a content per unit of U. prolifera were monitored in the flowing water system by simulating nutrients and temperature variations of seawaters from starting place to terminus of U. prolifera in the South Yellow Sea. According to the variations of nutrients during the drifting process, the floating process can be divided into three stages. Differentially expressed genes and differential metabolites in the three stages of U. prolifera drifting process were identified, which are mainly related to glycometabolism, nitrogen metabolism, and selenium compound metabolism. The process from Stage I to Stage II are mainly related to the translation and molecular function of biological processes, and the main differential metabolites are primary metabolites, whereas, from Stage II to Stage III, secondary metabolites start to increase, indicating that U. prolifera resisted environmental stress by increasing lipids and producing secondary metabolites. It will provide some guidance for the comprehensive interpretation of the biological basis and ecological mechanisms of the large-scale U. prolifera green tides in the Yellow Sea.

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and ‘SMOTE and cluster-based under-sampling technique’ (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.

Variability of the phytoplankton biomass in the southwestern Bay of Bengal from 2003 to 2018

The Bay of Bengal (BoB) has distinctive hydrological characteristics, and various physical mechanisms strongly influence the growth of phytoplankton in this region. In this work, we investigated the monthly and interannual variations of phytoplankton biomass (PB) in the southwestern (SW) BoB (78–88°E, 0–15°N) utilizing satellite data from 2003 to 2018. Bivariate wavelet coherence (BWC) and multiple wavelet coherence (MWC) analyses were conducted to assess the temporal fluctuations in various physical oceanographic parameters. The findings revealed that wind speed (WSP) and sea surface temperature (SST) were the main drivers for the monthly variations of chlorophyll-a (Chl-a). The southwest monsoon promoted the formation of upwelling by offshore Ekman transport via the southwest monsoon current (SMC) and the upward Ekman pumping induced by the strong positive wind stress curl, which brought nutrient-rich, cold waters from the subsurface to the surface, stimulating phytoplankton growth. The BWC analysis results identified WSP as the most effective individual factor accounting for the interannual fluctuations in Chl-a concentrations. The MWC analysis results unraveled that the combinations of WSP–SST, WSP–Niño 3.4, WSP–EPV, and WSP–DMI exhibited the strongest relationships with the changes in Chl-a in the SW BoB. The PB in 2015 demonstrated abnormally low values. The El Niño event in 2015 substantially weakened the southwest monsoon and current, possibly weakening the Ekman transport on the southern coast of Sri Lanka and India. Moreover, an abnormally intensified anticyclonic eddy occurred in this region. These processes disrupted the vertical exchange of nutrients, obstructing the vertical transport of nutrient-enriched subsurface waters and leading to abnormally decreased levels of Chl-a in 2015.

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

Eleven level-2 Sentinel 3A OLCI images that were acquired between 29 March 2017 and 11 December 2017 were used to assess their ability to retrieve oceanic Chl-a concentrations in South Africa’s Algoa and St Francis Bays. This was done by applying a 7-colour gradient pallet in the SNAP software to produce oceanic Chl-a concentration maps on a scale ranging from 0.1–30 mg/m3. The validation of Sentinel’s Chl-a’s retrieval potentials was based on temporally corresponding in-situ data from eight stations. Comparative analysis of the image-based and in-situ Chl-a concentrations revealed statistically significant correlations (r 0.609–r 0.899, ᾶ 0.05) at five stations out of the eight that were used as sources of reference data. This finding is helpful as an objectively premised source of insights on how to sustainably utilise the oceanic resources at our disposal. It is also useful because it verifiably demonstrates that Sentinel images can be reliably used to retrieve usable information on Chl-a concentrations in lieu of the costly sea-surface-based in-situ measurements at appropriate temporal and spatial scales.

Remote sensing-based spatiotemporal variation and driving factor assessment of chlorophyll-a concentrations in China’s Pearl River Estuary

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.