Seasonal patterns and bloom dynamics of phytoplankton based on satellite-derived chlorophyll-a in the eastern yellow sea

Satellite-derived chlorophyll-a concentration (Chl-a) is essential for assessing environmental conditions, yet its application in the optically complex waters of the eastern Yellow Sea (EYS) is challenged. This study refines the Chl-a algorithm for the EYS employing a switching approach based on normalized water-leaving radiance at 555 nm wavelength according to turbidity conditions to investigate phytoplankton bloom patterns in the EYS. The refined Chl-a algorithm (EYS algorithm) outperforms prior algorithms, exhibiting a strong alignment with in situ Chl-a. Employing the EYS algorithm, seasonal and bloom patterns of Chl-a are detailed for the offshore and nearshore EYS areas. Distinct seasonal Chl-a patterns and factors influencing bloom initiation differed between the areas, and the peak Chl-a during the bloom period from 2018 to 2020 was significantly lower than the average year in both areas. Specifically, bimodal and unimodal peak patterns in Chl-a were observed in the offshore and nearshore areas, respectively. By investigating the relationships between environmental factors and bloom parameters, we identified that major controlling factors governing bloom initiation were mixed layer depth (MLD) and suspended particulate matter (SPM) in the offshore and nearshore areas, respectively. Additionally, this study proposed that the recent decrease in the peak Chl-a might be caused by rapid environmental changes such as the warming trend of sea surface temperature (SST) and the limitation of nutrients. For example, external forcing, phytoplankton growth, and nutrient dynamics can change due to increased SST and limitation of nutrients, which can lead to a decrease in Chl-a. This study contributes to understanding phytoplankton dynamics in the EYS, highlighting the importance of region-specific considerations in comprehending Chl-a patterns and bloom dynamics.

Temporal variability in the relationship between line height absorption and chlorophyll concentration: a case study from the Northern Gulf of Alaska

The Line Height Absorption (LHA) method uses absorption of light to estimate chlorophyll-a. While most users consider regional variability and apply corrections, the effect of temporal variability is typically not explored. The Northern Gulf of Alaska (NGA) was selected for this study because there was no published regional value and its large swings in temporal productivity would make it a good candidate to evaluate the effect of temporal variability on the relationship. The mean NGA value of 0.0114 obtained here should be treated with caution, as variation in the slope of the relationship (aLH*), and thus chlorophyll-a estimates, in the NGA region varied by ∼25% between spring (aLH* = 0.0109) and summer (aLH* = 0.0137). Results suggest that this change is driven by a shift in pigment packaging and cell size associated with changes in mixed layer depth and stratification. Consideration of how temporal variability may affect the accuracy of the LHA method in other regions is thus recommended.

Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (Rrs) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS Rrs, with R2, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 μg/L, 2.95 μg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R2 = 0.76, MAE = 2.12 μg/L, RMSE = 2.51 μg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

A straightforward approach for the rapid detection of red Noctiluca scintillans blooms from satellite imagery

Red Noctiluca scintillans (RNS), a prominent species of dinoflagellate known for its conspicuous size and ability to form blooms, exhibits heterotrophic behavior and functions as a microzooplankton grazer within the marine food web. In this study, a straightforward technique referred to as the blue-green index (BGI) has been introduced for the purpose of distinguishing and discerning RNS from neighboring waters, owing to its pronounced absorption in the blue-green spectral range. This method has been applied across a range of satellite imagery, encompassing both multi-spectral and hyperspectral sensors. The study delved into three instances of bloom occurrences caused by RNS: firstly, in November 2014 and April 2022 off the western coast of Guangdong, and secondly, in February 2021 within the Beibu Gulf. The notable bloom event in the Beibu Gulf during February 2021 extended across an expansive area totaling 6933.5 km2. The motion speed and direction of the RNS bloom patches were also derived from successive satellite images. The recently introduced BGI method demonstrates insensitivity to suspended sediment, though its successful application necessitates accurate atmospheric correction. Subsequent efforts will involve the quantification of RNS blooms in a more precise manner, utilizing hyperspectral satellite data grounded in optimized band configurations.

Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms. By integrating extensive in situ data and high-resolution satellite data, the proposed framework successfully generated high-resolution (0.25° × 0.25°) estimates of DO concentration with exceptional accuracy (R2 = 0.95, RMSE = 11.95 μmol/kg, and test number = 2805) for near-global sea surface areas from 2010 to 2018, uncertainty estimated to be ±13.02 μmol/kg. The resulting sea surface DO data set exhibits precise spatial distribution and reveals compelling correlations with prominent marine phenomena and environmental stressors. Leveraging its interpretability, our model further revealed the key influence of marine factors on surface DO and their implications for environmental issues. The presented machine-learning framework offers an improved DO data set with higher resolution, facilitating the exploration of oceanic DO variability, deoxygenation phenomena, and their potential consequences for environments.

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.

Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003-2019

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO2 achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 μatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003-2019, the annual average CO2 sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO2 concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO2 absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO2 uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO2 and air-sea CO2 flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review

Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations.

Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correction

The residual error was a critical indicator to measure the data quality of ocean color products, which allows a user to decide the valuable envisioned application of these data. To effectively remove the residual errors from satellite remote sensing reflectance (Rrs) using the inherent optical data processing system (IDAS), we expressed the residual error spectrum as an exponential plus linear function, and then we developed neural network models to derive the corresponding spectral slope coefficients from satellite Rrs data. Coupled with the neural network models-based spectral relationship, the IDAS algorithm (IDASnn) was more effective than an invariant spectral relationship-based IDAS algorithm (IDAScw) in reducing the effects of residual errors in Rrs on IOPs retrieval for our synthetic, field, and Chinese Ocean Color and Temperature Scanner (COCTS) data. Particularly, due to the improved spectral relationship of the residual errors, the IDASnn algorithm provided more accurate and smoother spatiotemporal ocean color product than the IDAScw algorithm for the open ocean. Furthermore, we could monitor the data quality with the IDASnn algorithm, suggesting that the residual error was exceptionally large for COCTS images with low effective coverage. The product effective coverage should be rigorously controlled, or the residual error should be accurately corrected before temporal and spatial analysis of the COCTS data. Our results suggest that an accurate spectral relationship of residual errors is critical to determine how well the IDAS algorithm corrects for residual error.

Operational modelling to assess advective harmful algal bloom development and its potential to impact aquaculture

A particle tracking model is described and used to explore the role of advection as the source of harmful algal blooms that impact the Shetland Islands, where much of Scotland's aquaculture is located. The movement of particles, representing algal cells, was modelled using surface velocities obtained from the 1.5 km resolution Atlantic Margin Model AMM15. Following validation of model performance against drifter tracks, the model results recreate previously hypothesised onshore advection of harmful algal cells from west of the archipelago during 2006 and 2013, when exceptional Dinophysis spp. abundances were measured at Shetland aquaculture sites. Higher eastward advection of Dinophysis spp. cells was also suggested during 2018. Wind roses explain this higher eastward advection during 2006, 2013 and 2018. The study suggests that the European Slope Current is important for the transport of harmful algal blooms, particularly those composed of dinoflagellates.

Phytoplanktonic community and bio-optical properties in coastal waters of an Argentinian Patagonian gulf

The influence of the phytoplankton community in the light absorption budget was quantified in coastal waters of the North region of the San Jorge Gulf (Argentinian Patagonia). The phytoplanktonic composition and their absorption spectra were determined. Nanoflagellates and diatoms were the dominant groups. The toxigenic dinoflagellate Dinophysis acuminata was recorded in all the sampling sites. The optical characterization of the particulate material showed that 60 % of the absorption at 443 nm and 88 % of absorption at 675 nm was due to phytoplankton. The contributions of phytoplankton to total absorption at 443 nm wavelengths reached 50 %. The absorption by chromophoric dissolved organic matter (CDOM) and non-algal particles (NAP) was predominant in turbulent waters (>60 %). This study shows the influence of submesoscale physical-biological interactions in the light absorption budget. The field absorption spectra of active optical components are of interest in the assessment and development of regional ocean color satellite algorithms.

Bayesian approach to a generalized inherent optical property model

Relationships between the absorption and backscattering coefficients of marine optical constituents and ocean color, or remote sensing reflectances R_rs(λ), can be used to predict the concentrations of these constituents in the upper water column. Standard inverse modeling techniques that minimize error between the modeled and observed R_rs(λ) break down when the number of products retrieved becomes similar to, or greater than, the number of different ocean color wavelengths measured. Furthermore, most conventional ocean reflectance inversion approaches, such as the default configuration of NASA's Generalized Inherent Optical Properties algorithm framework (GIOP-DC), require a priori definitions of absorption and backscattering spectral shapes. A Bayesian approach to GIOP is implemented here to address these limitations, where the retrieval algorithm minimizes both the error in retrieved ocean color and the deviation from prior knowledge, calculated using output from a mixture of empirically-derived and best-fit values. The Bayesian approach offers potential to produce an expanded range of parameters related to the spectral shape of absorption and backscattering spectra.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Open science resources from the Tara Pacific expedition across coral reef and surface ocean ecosystems

The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean and the ocean surface waters at 249 locations, resulting in the collection of nearly 58 000 samples. The expedition was designed to systematically study warm-water coral reefs and included the collection of corals, fish, plankton, and seawater samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide a complete description of the sampling methodology, and we explain how to explore and access the different datasets generated by the expedition. Environmental context data were obtained from taxonomic registries, gazetteers, almanacs, climatologies, operational biogeochemical models, and satellite observations. The quality of the different environmental measures has been validated not only by various quality control steps, but also through a global analysis allowing the comparison with known environmental large-scale structures. Such publicly released datasets open the perspective to address a wide range of scientific questions.

Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a

Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.

Increase in chlorophyll-a concentration in Lake Taihu from 1984 to 2021 based on Landsat observations

Lake Taihu, located in a densely populated and highly industrialized area in eastern China, has experienced dramatic changes in water quality since the reform and opening-up in the 1980s. Landsat data can be used to trace water quality changes over approximately 40 years. However, chlorophyll-a (Chla) estimation, which characterizes the trophic status, has not been thoroughly explored (especially in turbid water using wide bandwidth Landsat) due to the interference of suspended particulate matter (SPM) to Chla. In this study, we used Landsat TM/OLI for turbid water Chla inversion and to analyze the spatiotemporal variation of Chla in Lake Taihu for 38 years and its influencing factors. An optical classification algorithm based on R_rs(green)/R_rs(red) was used to exclude highly turbid waters dominated by SPM; Chla was estimated only in waters with low SPM. We constructed an exponential estimation model based on R_rs(NIR)/R_rs(red), and verified the accuracy of the model using the measured Chla synchronized with satellite data. The model was applied to Landsat images to calculate the Chla concentration in Lake Taihu during 1984-2021, and its spatiotemporal distribution patterns were further analyzed. Spatially, the Chla concentrations in the western and northern regions of Lake Taihu were higher than those in other regions, probably because these areas are estuaries with large exogenous pollutant discharge and more nutrients are imported from exogenous sources. Chla showed an overall significant upward trend from 1984 to 2021 probably because of temperature rise, wind speed reduction, and nutrient increase. The results of the spatial and temporal variation of Chla and the influencing factors in this study provide supporting data for eutrophication monitoring and management in Lake Taihu. The proposed Chla estimation method can be extended to assess the spatial and temporal distribution of eutrophication in other inland waters with similar optical properties.

A satellite-based hybrid model for trophic state evaluation in inland waters across China

Eutrophication is one of the major threats to the inland water ecosystem. Satellite remote sensing provides a promising way to monitor trophic state at large spatial scale in an efficient manner. Currently, most satellite-based trophic state evaluation approaches have focused on water quality parameters retrieval (e.g., transparency, chlorophyll-a), based on which trophic state was evaluated. However, the retrieval accuracies of individual parameter do not meet the demand for accurate trophic state evaluation, especially for the turbid inland waters. In this study, we proposed a novel hybrid model to estimate trophic state index (TSI) by integrating multiple spectral indices associated with different eutrophication level based on Sentinel-2 imagery. The TSI estimated by the proposed method agreed well with the in-situ TSI observations, with root mean square error (RMSE) of 6.93 and mean absolute percentage error (MAPE) of 13.77%. Compared with the independent observations from Ministry of Ecology and Environment, the estimated monthly TSI also showed good consistency (RMSE=5.91,MAPE=10.66%). Furthermore, the congruent performance of the proposed method in the 11 sample lakes (RMSE=5.91,MAPE=10.66%) and the 51 ungauged lakes (RMSE=7.16,MAPE=11.56%) indicated the favorable model generalization. The proposed method was then applied to assess the trophic state of 352 permanent lakes and reservoirs across China during the summers of 2016-2021. It showed that 10%, 60%, 28%, and 2% of the lakes/reservoirs are in oligotrophic, mesotrophic, light eutrophic, and middle eutrophic states respectively. Eutrophic waters are concentrated in the Middle-and-Lower Yangtze Plain, the Northeast Plain, and the Yunnan-Guizhou Plateau. Overall, this study improved the trophic state representativeness and revealed trophic state spatial distribution of Chinese inland waters, which has the significant meanings for aquatic environment protection and water resource management.

V, Vijayan NT, Subramanian M, Gauns MU. Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

Satellite-Based Monitoring of Eutrophication in the Earth's Largest Transboundary Lake

The world's large lakes and their life-supporting services are rapidly threatened by eutrophication in the warming climate during the Anthropocene. Here, MODIS-Aqua level 3 chlorophyll-a data (2018-2021) were used to monitor trophic state in our planet's largest lake, that is, the Caspian Sea that accounts for approximately 40% of the total lacustrine waters on Earth. We also used the in situ measurements of chlorophyll-a data (2009-2019) to further verify the accuracy of the data derived from the MODIS-Aqua and to explore the deep chlorophyll-a maxima (DCMs) in the south Caspian Sea. Our findings show an acceptable agreement between the chlorophyll-a data derived from the MODIS-Aqua and those measured in situ in the coast of Iran (coefficient of determination = 0.71). The oligotrophic, mesotrophic, and eutrophic states cover 66%, 20%, and 13% of the sea surface area, respectively. The DCMs are dominantly regulated by water transparency and they generally observe at depths of less than 20 and 30 m during the cold (autumn and winter) and warm (spring and summer) seasons, respectively. Our results suggest an ever-increasing chlorophyll-a in the shallow zones (i.e., coasts) and even in deep regions of the sea, mainly due to nutrient inputs from the Volga river delta. Alarming increase of chlorophyll-a in this transboundary lake can amplify eutrophication under the lens of global warming and further threaten the lake ecosystem's health, where almost all legal agreements have not yet been implemented to protect the lake environment and its rich resources.

Transfer model to determine the above-water remote-sensing reflectance from the underwater remote-sensing ratio

Remote-sensing reflectance, R_rs(λ, θ, Δϕ, θ_s), contains the spectral color information of the water body below the sea surface and is a fundamental parameter to derive satellite ocean color products such as chlorophyll-a, diffuse light attenuation, or inherent optical properties. Water reflectance, i.e., spectral upwelling radiance, normalized by the downwelling irradiance, can be measured under- or above-water. Several models to extrapolate this ratio from underwater "remote-sensing ratio", r_rs(λ), to the above-water R_rs, have been proposed in previous studies, in which the spectral dependency of water refractive index and off-nadir viewing directions have not been considered in detail. Based on measured inherent optical properties of natural waters and radiative transfer simulations, this study proposes a new transfer model to spectrally determine R_rs from r_rs for different sun-viewing geometries and environmental conditions. It is shown that, compared to previous models, ignoring spectral dependency leads to a bias of ∼2.4% at shorter wavelengths (∼400 nm), which is avoidable. If nadir-viewing models are used, the typical 40°-off nadir viewing geometry will introduce a difference of ∼5% in R_rs estimation. When the solar zenith angle is higher than 60°, these differences of R_rs have implications for the downstream retrievals of ocean color products, e.g., > 8% difference for phytoplankton absorption at 440 nm and >4% difference for backward particle scattering at 440 nm by the quasi-analytical algorithm (QAA). These findings demonstrate that the proposed r_rs-to-R_rs model is applicable to a wide range of measurement conditions and provides more accurate estimates of R_rs than previous models.

Monitoring of Harmful Algal Bloom (HAB) of Noctiluca scintillans (Macartney) along the Gulf of Mannar, India using in-situ and satellite observations and its impact on wild and maricultured finfishes

In the Gulf of Mannar, Noctiluca scintillans blooms have been observed three times in September 2019, September and October 2020, and October 2021. It was determined and measured how the bloom period affects ichthyo-diversity. Noctiluca cell density varied slightly from year to year, ranging from1.8433 × 10³ cells/L to 1.3824 x 10⁶cells/L. In surface and sea bottom waters, high ammonia levels and low dissolved oxygen levels were noted. During the bloom period a significant increase in chlorophyll concentration was found. The amount of chlorophyll in GOM was extremely high, according to remote sensing photos made using MODIS-Aqua 4 km data. Acute hypoxia caused the death of wild fish near coral reefs and also in fish reared in sea cages. The decay of the bloom resulted in significant ammonia production, a dramatic drop in the amount of dissolved oxygen in the water, and ultimately stress, shock, and mass mortality of fishes.

Coastal phytoplankton blooms expand and intensify in the 21st century

Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems1,2-yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly (P < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions.

Seasonality of phytoplankton biomass and composition on the Cape Canaveral shelf of Florida: Role of shifts in climate and coastal watershed influences

Seasonal patterns of phytoplankton biomass and composition in the inner continental shelf off Cape Canaveral on the east coast of Florida were examined for a 6-year period (2013–2019). In situ water samples were collected and analyzed for chlorophyll a, phytoplankton biomass and composition, along with water quality parameters. Regional satellite data on chlorophyll a, and temperature was also obtained from NASA. Average chlorophyll a values over the study period ranged from 0.63 ± 0.03 μg L−1 in the summer to 2.55 ± 0.10 μg L−1 in the fall. Phytoplankton community composition also showed seasonal differences, with persistent dominance by picoplanktonic cyanobacteria in the summer, but mixed dominance by picocyanobacteria and dinoflagellates in the fall. Seasonal differences were attributed to a shift in predominant seasonal wind directions, which drive water along the coast from the north in the fall and winter, but from the south in the spring and summer, including eddies and upwelling from the Gulf Stream. Water masses moving along the Florida coast from the north are influenced by nutrient and phytoplankton-enriched inputs from estuaries along the north coast of Florida, explaining the higher phytoplankton biomass levels on the Cape Canaveral shelf in the fall and winter. Seasonal patterns observed in this study demonstrate the importance of allochthonous influences on phytoplankton biomass and composition, and highlight the potential sensitivity of phytoplankton communities to continuing cultural eutrophication and future climate changes, including the frequency and intensity of tropical storms, and alterations in discharges from land.

Modified future diurnal variability of the global surface ocean CO2 system

Our understanding of how increasing atmospheric CO₂ and climate change influences the marine CO₂ system and in turn ecosystems has increasingly focused on perturbations to carbonate chemistry variability. This variability can affect ocean-climate feedbacks and has been shown to influence marine ecosystems. The seasonal variability of the ocean CO₂ system has already changed, with enhanced seasonal variations in the surface ocean pCO₂ over recent decades and further amplification projected by models over the 21st century. Mesocosm studies and CO₂ vent sites indicate that diurnal variability of the CO₂ system, the amplitude of which in extreme events can exceed that of mean seasonal variability, is also likely to be altered by climate change. Here, we modified a global ocean biogeochemical model to resolve physically and biologically driven diurnal variability of the ocean CO₂ system. Forcing the model with 3-h atmospheric outputs derived from an Earth system model, we explore how surface ocean diurnal variability responds to historical changes and project how it changes under two contrasting 21st-century emission scenarios. Compared to preindustrial values, the global mean diurnal amplitude of pCO₂ increases by 4.8 μatm (+226%) in the high-emission scenario but only 1.2 μatm (+55%) in the high-mitigation scenario. The probability of extreme diurnal amplitudes of pCO₂ and [H⁺ ] is also affected, with 30- to 60-fold increases relative to the preindustrial under high 21st-century emissions. The main driver of heightened pCO₂ diurnal variability is the enhanced sensitivity of pCO₂ to changes in temperature as the ocean absorbs atmospheric CO₂ . Our projections suggest that organisms in the future ocean will be exposed to enhanced diurnal variability in pCO₂ and [H⁺ ], with likely increases in the associated metabolic cost that such variability imposes.

Threshold indicators of primary production in the north-east Atlantic for assessing environmental disturbances using 21 years of satellite ocean colour

Primary production (PP) is highly sensitive to changes in the ecosystem and can be used as an early warning indicator for disturbance in the marine environment. Historic indicators of good environmental status of the north-east (NE) Atlantic and north-west (NW) European Seas suggested that daily PP should not exceed 2-3 g C m-2 d-1 during phytoplankton blooms and that annual rates should be <300 g C m-2 yr-1. We use 21 years of Copernicus Marine Service (CMEMS) Ocean Colour data from September 1997 to December 2018 to assess areas in the NE Atlantic with similar peak, climatology, phenology and annual PP values. Daily and annual thresholds of the 90th percentile (P90) of PP are defined for these areas and PP values above these thresholds indicate disturbances, both natural and anthropogenic, in the marine environment. Two case studies are used to test the validity and accuracy of these thresholds. The first is the eruption of the volcano Eyjafjallajökull, which deposited large volumes of volcanic dust (and therefore iron) into the NE Atlantic during April and May 2010. A clear signature in both PP and chlorophyll-a (Chl a) was evident from 28th April to 6th May and from 18th to 27th May 2010, when PP exceeded the PP P90 threshold for the region, which was comparatively more sensitive than Chl a P90 as an indicator of this disturbance. The second case study was for the riverine input of total nitrogen and phosphorus, along the Wadden Sea coast in the North Sea. During years when total nitrogen and phosphorus were above the climatology maximum, there was a lag signature in both PP and Chl a when PP exceeded the PP P90 threshold defined for the study area which was slightly more sensitive than Chl a P90. This technique represents an accurate means of determining disturbances in the environment both in the coastal and offshore waters in the NE Atlantic using remotely sensed ocean colour data.

Scheme to estimate water-leaving albedo from remotely sensed chlorophyll-a concentration

Water-leaving albedo (α_w(λ)) is an important component of the ocean surface albedo and is conventionally estimated based on remotely sensed chlorophyll-a concentration (Chl) (termed Chl-α_w). We show that estimated α_w(λ) by Chl-α_w could be significantly biased in global oceans, because there is no guarantee of closure between the modeled remote sensing reflectance (R_rs(λ)) from Chl-inferred inherent optical properties (IOPs) and the input R_rs(λ) that is used to derive Chl. We thus propose a simple and improved scheme, termed Chl-α_w_new, and show that the step to infer IOPs from Chl is not necessary, where α_w(λ) can be accurately estimated from satellite-measured R_rs(λ) and a Chl-based look-up-table (LUT) for the bidirectional relationships of angular R_rs(λ). Evaluations with both HydroLight simulations and satellite measurements show that Chl-α_w_new is equivalent to the recently developed α_w scheme based on IOPs (IOPs-α_w, [Remote Sens. Environ. 269, 112807]), where both schemes could significantly improve the estimation of α_w(λ) compared to Chl-α_w. Comparisons among Chl-α_w, Chl-α_w_new, and IOPs-α_w highlight that optical closure of R_rs(λ) is essential for accurate remote sensing of α_w(λ), while the model for R_rs(λ) bidirectionality has rather minor impacts. The impact of improved α_w(λ) estimations on the solar flux exchanges at the air-sea interface is preliminarily evaluated in this effort, where the use of Chl-α_w_new could increase the estimation of reflected solar radiation by over 68.7% in turbid waters compared to that using Chl-α_w, highlighting the necessity of incorporating accurate α_w schemes into the coupled ocean-atmosphere models, especially for regional models in coastal oceans.

Ocean water quality monitoring using remote sensing techniques: A review

Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m² and c) SWIR bands are important for marine litter detection.

Deriving vertical profiles of chlorophyll-a concentration in the upper layer of seawaters using ICESat-2 photon-counting lidar

Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs<15%) and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data in the along-track direction (average R²>0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R_rs, is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R_rs (uc(R_rs)) at each pixel. Uncertainty in R_rs, in turn, is dependent on the propagation of various uncertainty sources through the R_rs retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R_rs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(R_rs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ R_rs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R_rs at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/m³) with valid uc(R_rs) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that uc(R_rs) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Remote sensing provides new insights on phytoplankton biomass dynamics and black pearl oyster life-history traits in a Pacific Ocean deep atoll

Thus far, no long-term in situ observation of planktonic biomass have been undertaken to optimize the black-lip pearl oyster aquaculture in the remote Tuamotu atolls. The feasibility of using data from the OLI sensor onboard Landsat-8 satellite to determine chlorophyll a concentrations (Chla) in a deep atoll, Ahe, was then assessed over the 2013-2021 period using 153 images. Validations with in situ observations were satisfactory, while seasonal and spatial patterns in Chla were evidenced within the lagoon. Then, a bioenergetic modelling exercise was undertaken to estimate oyster life-history traits when exposed to the retrieved Chla. The outputs provide spatio-temporal variations in pelagic larval duration (11.1 to 30.6 days), time to reach commercial size (18.8 to 45.3 months) and reproductive outputs (0.5 to 1.7 event year^-1). This first study shows the potential of using remote sensing to monitor the trophic status of deep pearl farming lagoons and help aquaculture management.

Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP

Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO.

Plankton Imagery Data Inform Satellite-Based Estimates of Diatom Carbon

Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite-based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging-in-flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment-based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite-based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.

An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002–2020 period are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh corrected TOA reflectances (RC). In each case, a NN architecture consisting of 3 layers of 15 neurons improved performance and data availability compared to current state-of-the-art algorithms used in the region. The NN operating on TOA reflectance outperformed BOA and RC versions. By operating on TOA reflectance data, the NN approach overcomes the common but difficult problem of atmospheric correction in coastal waters. Moreover, the NN provides data for regions which other algorithms often mask out for turbid water or low zenith angle flags. A distinguishing feature of the NN approach is generation of associated product uncertainties based on multiple resampling of the training data set to produce a distribution of values for each pixel, and an example is shown for a coastal time series in the North Sea. The final output of the NN approach consists of a best-estimate image based on medians for each pixel, and a second image representing uncertainty based on standard deviation for each pixel, providing pixel-specific estimates of uncertainty in the final product.

Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective

Accurate and robust measurements from ocean color satellites are critical to studying spatial and temporal changes of surface ocean properties. Satellite-derived Chlorophyll-a (Chl) is an important parameter to monitor phytoplankton blooms on synoptical scales, particularly in remote seas. However, the present NASA standard Chl algorithm tends to strongly underestimate the Chl in the Ross Sea. Based on a locally-tuned Chl algorithm in the Ross Sea and using the data record from MODIS between 2002 and 2020, here we investigated the spatial expansion of phytoplankton blooms in the Ross Sea. Our results show the geometric areas of the phytoplankton blooms could reach (7.20 ± 2.8) × 104 km2 on average, which was ~3.1 times that of those identified using the NASA default Chl algorithm. Spatially, blooms were frequently identified on the shelf of the Ross Sea polynya with a typical chance of ≥80%. In the context of climate change and global warming, the general decrease and interannual dynamics of sea ice cover tends to affect solar light penetration and surface seawater temperature, which were found to regulate the spatial expansion of the phytoplankton blooms over the years. Statistical analyses showed that the spatial coverages of the phytoplankton blooms were significantly correlated with sea surface temperature (Spearman correlation coefficient R = 0.55, at p < 0.05), sea surface wind speed (R = 0.42, at p < 0.05), and sea ice concentration (R = −0.84, at p < 0.05), yet without significant long-term (>10 years) trends over the study period. The stronger phytoplankton blooms than those previously observed may indicate larger carbon sequestration, which needs to be investigated in the future. More valid satellite observations under cloud covers will further constrain the estimates.

Satellite Observation of the Long-Term Dynamics of Particulate Organic Carbon in the East China Sea Based on a Hybrid Algorithm

The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas.

Remote Sensing of Marine Phytoplankton Sizes and Groups Based on the Generalized Addictive Model (GAM)

Marine phytoplankton are the basis of the whole marine ecosystem, and different groups of phytoplankton play different roles in the biogeochemical cycle. Satellite remote sensing is widely used in the retrieval of marine phytoplankton over a wide range and long time series, but not yet for taxonomical composition. In this study, we used coincident in situ measurement data from high-performance liquid chromatography (HPLC) and remote sensing reflectance (Rrs) to investigate the empirical relationships between phytoplankton groups and satellite measurements. A nonparametric model, generalized additive model (GAM), is introduced to establish inversion models of various marine phytoplankton groups. Seven inversion models (two sizes classes among the microphytoplankton and nanophytoplankton and four groups among the diatoms, dinoflagellates, chrysophytes, and cryptophytes) are applied to the South China Sea (SCS) for 2020, and satellite images of phytoplankton sizes and groups are presented. Microphytoplankton prevails in the coastal and continental shelf, and nanophytoplankton prevails in oligotrophic oceans. Among them, the dominant contribution of microphytoplankton comes from diatoms, and nanophytoplankton comes from chrysophytes. Diatoms (nearshore) and chrysophytes (outside the continental shelf) are the dominant groups in the SCS throughout the year. Dinoflagellates only become dominant in some coastal areas, while cryptophytes rarely become dominant.

Introducing Two Fixed Platforms in the Yellow Sea and East China Sea Supporting Long-Term Satellite Ocean Color Validation: Preliminary Data and Results

Following the Aerosol Robotic Network-Ocean Color (AERONET-OC) network scheme and instrument deployment protocols, two fixed platforms (Muping and Dong’ou) in the Yellow Sea and East China Sea were implemented with the support of the China National Satellite Ocean Application Service. Optical radiometry instruments were established at the two sites to autonomously determine remote sensing reflectance (Rrs) and aerosol optical depth (AOD). Details about location selection, platform design, instrument deployment, and the associated data processing procedure are reported in this study. Rrs and AOD measured by independent instruments at the Muping site were compared and results showed that they were consistent, with a median relative percentage difference (MRPD) < 0.6% for AOD and <10% for Rrs. The spectral feature and temporal pattern of Rrs and AOD at the two sites were examined and compared with data from 14 AERONET-OC sites. Rrs and AOD data measured at the two sites were used to evaluate ocean color operational products of MODIS/Aqua (MODISA), OLCI/Sentinel-3A (OLCI-3A), and OLCI/Sentinel-3B (OLCI-3B). Comparison showed that the three satellite sensor-derived Rrs agreed well with in situ measurements, with an MRPD < 25% for MODISA, <30% for OLCI-3A, and <40% for OLCI-3B, respectively. Large uncertainties were observed in the blue bands for the three satellite sensors, particularly for OLCI-3B at 400 nm. AOD at 865 nm derived from the three satellite sensors also agreed well with in situ measurements, with an MRPD of 28.1% for MODISA, 30.6% for OLCI-3A, and 39.9% for OLCI-3B. Two commonly used atmospheric correction (AC) processors, the ACOLITE and SeaDAS, were also evaluated using in situ measurements at two sites and 20 m-resolution MSI/Sentinel-2A data. Close agreements were achieved for both AC processors, while the SeaDAS performed slightly better than ACOLITE. The optimal band selection in the AC models embedded in two AC processors was a combination of one near-infrared and one short-wave infrared band such as 865 and 1609 nm, shedding light on MSI data applications in the aquatic environment.

Estimates of Hyperspectral Surface and Underwater UV Planar and Scalar Irradiances from OMI Measurements and Radiative Transfer Computations

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

Rising dissolved organic carbon concentrations in coastal waters of northwestern Borneo related to tropical peatland conversion

Southeast Asia's peatlands are considered a globally important source of terrigenous dissolved organic carbon (DOC) to the ocean. Human disturbance has probably increased peatland DOC fluxes, but the lack of monitoring has precluded a robust demonstration of such a regional-scale impact. Here, we use a time series of satellite ocean color data from northwestern Borneo to show that DOC concentrations in coastal waters have increased between 2002 and 2021 by 0.31 μmol liter^-1 year^-1 (95% confidence interval, 0.18 to 0.44 μmol liter^-1 year^-1). We show that this was caused by a ≥30% increase in the concentration of terrigenous DOC and coincided with the conversion of 69% of regional peatland area to nonforest land cover, suggesting that peatland conversion has substantially increased DOC fluxes to the sea. This rise in DOC concentration has also increased the underwater light absorption by dissolved organic matter, which may affect marine productivity by altering underwater light availability.

Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland

As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months.

Microplankton life histories revealed by holographic microscopy and deep learning

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

Picture a glass of seawater. It looks clear and empty, but in reality, it contains one hundred million bacteria, about one hundred thousand other single-celled organisms, and a few microscopic animals. In fact, the majority of life in the ocean is microscopic and we know relatively little about it. Nevertheless, these microbes have a major impact on our lives. Microscopic algae known as phytoplankton, for example, produce half of the oxygen we breathe.

For animals, birds and other large organisms in the ocean, we have a good understanding of who eats who and where the material ends up. However, for phytoplankton and other microbes, we depend on bulk measurements and averages of large groups. Bachimanchi et al. developed a method to follow individual microbes living in seawater and to observe how they move, grow, consume each other and reproduce.

The team combined holographic microscopy with artificial intelligence to follow multiple planktons, diatoms and other microbes throughout their life span and continuously measured their three-dimensional location and mass. This made it possible to estimate how fast the organisms were growing and moving, and to observe what they ate. The experiments revealed new insights into how micro-zooplankton, diatoms and other microbes in the ocean interact with each other.

This new method may be useful for researchers who would like to track the movements and whereabouts of microscopic planktons, bacteria or other microbes for extended periods of time. It is also a rapid method for counting, sizing, and weighing cells in suspension. The hardware used in this method is relatively cheap, and Bachimanchi et al. have shared all the computer code with examples and demonstrations in a public database to enable other researchers to use it.

Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, Chl_BS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Improved shadow correction for the marine optical buoy, MOBY

A 3-D instrument self-shading correction has been developed for the MOBY upwelling radiance measurements. This correction was tested using the 23 year time series of MOBY measurements, at the Lanai, Hawaii site. The correction is small (less than 2%) except when the sun and collectors are aligned within 20° azimuth on opposite sides of the main MOBY structure. Estimates of the correction uncertainty were made with a Monte-Carlo method and the variation of the model input parameters at this site. The correction uncertainty was generally less than 1%, but increased to 30% of the correction in the strongest shadow region.

Assessment of potential variability of cadmium and copper trace metals using hindcast estimates

Trace metals are vital to primary productivity and play an essential role as main components in regulating oceanic biogeochemical cycles. Dissolved and particulate trace metals within the water column may vary due to primary production, temperature, and nutrient changes, factors that may also vary spatially and temporally. Furthermore, assessment of trace metals mainly relies on in situ observation, and so wide-area investigation of trace-metal concentration may be challenging and subject to technical constraints. A specific approach is therefore necessary that combines biogeochemical proxies, satellite data, and trace-metal linear correlation. This study aims to assess the potential spatio-temporal variability of sea surface cadmium (Cd) and copper (Cu) concentrations in Indonesian seas and surrounding areas. The correlations of Cd and Cu concentrations with primary production and nutrient data were used to convert hindcast satellite data into estimates of the metals' concentrations. The potential variability of trace metals can be determined by overlaying both data. Indonesia's Fisheries Management Areas (FMAs) were used for data clustering and analysis. The results show that Cd and Cu trace metals have similar distribution patterns throughout the year. However, dissolved Cu has a more diverse coverage area than dissolved Cd, including within the Halmahera, Seram, and Maluku Seas (FMAs 716 and 717), the Makassar Strait (FMA 717), and the Java-Sumatra upwelling area (FMA 573). Both Cd and Cu concentrations in the Java-Sumatra upwelling region follow the periodic upwelling pattern. Overall, both Cd and Cu show a declining trend in concentration from 2012 to 2019. It is estimated that dissolved Cd concentration declined from 1500-2000 pmol/kg in 2012 to 1000-1500 pmol/kg in 2019 for all locations. Dissolved Cu concentration decreased from 30-35 nmol/kg in 2012 to 25-30 nmol/kg in 2019. Estimated dissolved Cd and Cu follow the linear functions of silicate (SiO₄), nitrate (NO₃), and primary productivity. The fluctuation of anthropogenic activities and global warming are likely to indirectly impact the decline in metal concentrations by affecting nutrients and primary productivity.

Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea)

While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.

Monitoring Cyanobacterial Blooms during the COVID-19 Pandemic in Campania, Italy: The Case of Lake Avernus

Cyanobacteria are ubiquitous photosynthetic microorganisms considered as important contributors to the formation of Earth’s atmosphere and to the process of nitrogen fixation. However, they are also frequently associated with toxic blooms, named cyanobacterial harmful algal blooms (cyanoHABs). This paper reports on an unusual out-of-season cyanoHAB and its dynamics during the COVID-19 pandemic, in Lake Avernus, South Italy. Fast detection strategy (FDS) was used to assess this phenomenon, through the integration of satellite imagery and biomolecular investigation of the environmental samples. Data obtained unveiled a widespread Microcystis sp. bloom in February 2020 (i.e., winter season in Italy), which completely disappeared at the end of the following COVID-19 lockdown, when almost all urban activities were suspended. Due to potential harmfulness of cyanoHABs, crude extracts from the “winter bloom” were evaluated for their cytotoxicity in two different human cell lines, namely normal dermal fibroblasts (NHDF) and breast adenocarcinoma cells (MCF-7). The chloroform extract was shown to exert the highest cytotoxic activity, which has been correlated to the presence of cyanotoxins, i.e., microcystins, micropeptins, anabaenopeptins, and aeruginopeptins, detected by molecular networking analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data.