Physical drivers of Noctiluca scintillans (Dinophyceae) blooms outbreak in the northern Taiwan Strait: A numerical study.

The red Noctiluca scintillans (RNS) blooms often break out near Pingtan Island, in the northern Taiwan Strait from April to June. It is essential to gain insights into their formation mechanism to predict and provide early warnings for these blooms. Previous studies and observations showed that RNS blooms are the most likely to occur when winds are weak and shifting in direction. To explore this phenomenon further, we employed a high-resolution coastal model to investigate the hydrodynamics influencing RNS blooms around Pingtan Island from April to June 2022. The model results revealed that seawater exhibited weak circulation but strong stratification during RNS blooms. Residence time were examined through numerical experiments by releasing passive neutrally buoyant particles in three bays of Pingtan Island. The results showed a significantly longer residence time during RNS blooms, indicating reduced flushing capabilities within the bays, which could give RNS a stable environment to multiply and aggregate. This hydrodynamic condition provided a favorable basis for RNS blooms breakout near Pingtan Island. The shifts and weakening of the prevailing northeast wind contributed substantially to weakening the flow field around Pingtan Island and played a crucial role in creating the hydrodynamics conducive to RNS blooms. Our study offers fresh insights into the mechanisms underpinning RNS blooms formation near Pingtan Island, providing a vital framework for forecasting RNS blooms in this region.

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology.

Background: The spatiotemporal variation of observed trace gases (NO2, SO2, O3) and particulate matter (PM2.5, PM10) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM2.5 > 40 µg/m3) and complex pollution episodes (PM2.5 > 35 µg/m3 and O3 > 160 µg/m3) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined. Results: The highest annual mean concentrations of PM2.5, PM10, NO2 and O3 were found for 2019 over all the cities. The annual mean concentrations of PM2.5, PM10, and NO2 showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O3 levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China's National environmental monitoring program since 2013. However, a slight increase in annual O3 was observed in 2021. The highest overall means of PM2.5, PM10, SO2, and NO2 were observed over Hefei, whereas the highest O3 levels were found in Nanjing. Despite the strict control measures, PM2.5 and PM10 concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O3 and PM2.5 pollution days were higher in Hangzhou and Shanghai.The in situ data for SO2 and NO2 showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data. Conclusions: Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O3, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-022-00668-2.

Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data.

In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD550 nm) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003-2020 show a significant increase in AOD (by 0.006-0.014 year-1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.

Dynamics of euphotic zone depth in the Bohai Sea and Yellow Sea.

Euphotic zone depth (Zeu) plays an important role in studies of marine biogeochemical processes and ecosystems. Remote sensing techniques are ideal tools to investigate Zeu distributions because of their advanced observation ability with broad spatial coverage and frequent observation intervals. This study aims to develop a new approach that derives Zeu directly from remote sensing reflectance (Rrs(λ)) values rather than by using other intermediate variables and then reveals the dynamic characteristics of Zeu in the Bohai Sea (BS) and Yellow Sea (YS). To do this, in situ data collected from various seasons were first used to assess the ability of several spectral indicators of Rrs(λ) for deriving Zeu and the optimal spectral indicator was determined to build a Zeu retrieval model. This model was further applied to Geostationary Ocean Color Imager (GOCI) data to study the spatial and temporal variations in Zeu. The results showed that the new Zeu retrieval model performed well with R2, RMSE and MAPE values of 0.843, 4.42 m and 17.9%, respectively. High Zeu levels were generally observed during summer for both coastal and offshore waters while the lowest Zeu values were observed during winter. Changing concentrations of total suspended matter, which are often modulated by sediment resuspension and transportation, are probably the main factor responsible for the spatial and temporal variability of Zeu. These findings provide crucial information for modeling primary production, carbon flux, and heat transfer, etc., in the BS and YS, as well as contribute a useful alternative approach that will be easily implemented to study Zeu from satellite data for other water environments.

Remote sensing estimation of colored dissolved organic matter (CDOM) from GOCI measurements in the Bohai Sea and Yellow Sea.

Colored dissolved organic matter (CDOM) is the main constituent of dissolved organic matter (DOM), also a key indicator of water quality conditions. Accurate estimation of CDOM is essential for understanding biogeochemical processes and ecosystems in marine waters. The use of remote sensing to derive the changes in CDOM is vital technology that can be used to dynamically monitor the marine environment and to document the spatiotemporal variations in CDOM over a large scale. In the present study, we develop a simple approach to estimate the CDOM concentrations based on the in situ datasets from four cruise surveys over the Bohai Sea (BS) and Yellow Sea (YS). Eight band combination forms (using Xi as a delegate, where i denotes the numerical order of band combination forms), including single bands, band ratios, and other band combinations by remote sensing reflectance, Rrs(λ), were trained to test the correlations with the CDOM concentrations. The obtained results indicated that X7, i.e., [Rrs(443) + Rrs(555)]/[Rrs(443)/Rrs(555)], was the optimal form, with correlation coefficient (R) values of 0.904 (p < 0.001). The X7-based fitting model was determined as the optimal model by the leave-one-out cross-validation method with relatively low estimation errors (mean relative error, MRE, 20%), and satellite match-up validation with in situ measurements indicated good performance MRE = 20.3%). Moreover, two spatial distribution patterns of CDOM in Jan. 2017 and Apr. 2018 (independent data) retrieved from Geostationary Ocean Color Imager (GOCI) data agreed well with those in situ observations. These results indicate that our proposed algorithm is feasible and robust for retrieving CDOM concentrations in this study region. In addition, we applied this method to GOCI data for the whole 2016 year in the BS and YS and produced the spatial distribution patterns from different temporal scales including monthly, seasonal, and annual scales. Overall, the findings of this study motivate the development and application of a simple but effective method of the CDOM estimation for those optically complex turbid coastal waters, like this study water areas.

Synoptic relationships to estimate phytoplankton communities specific to sizes and species from satellite observations in coastal waters.

Knowing variations of phytoplankton community characteristics is of great significance to many marine ecological and biogeochemical processes in oceanography and related fields research. Satellite remote sensing provides the only viable path for continuously detecting phytoplankton community characteristics in the large-scale spatial areas. However, remote sensing approaches are currently hindered by limited understanding on reflectance responses to variations from phytoplankton community compositions and further do not achieve a true application by satellite observations. Here we analyze in situ observation data sets from three cruises in a dynamic marine environment covering those coastal water areas in the marginal seas of the Pacific Northwest (Bohai Sea, Yellow Sea, and East China Sea). The size/species-specific phytoplankton assemblages can be quantitatively defined by the high-performance liquid chromatography (HPLC)-derived phytoplankton pigments and customized diagnostic pigment analysis, as well as a matrix factorization "CHEMTAX" program. Therein, note that a suit of updated weight values for diagnostic pigments are proposed with better performance than others. The above-mentioned size/species-specific phytoplankton assemblages include three size classes, i.e., micro-, nano-, and picoplankton, and eight species typically existing in the investigated water areas. Relationship analysis illustrates us that relatively close and robust models can be established to associate three size-specific and four dominant species-specific phytoplankton biomasses with the total chlorophyll a. Those models are then applied to the Geostationary Ocean Color Imager (GOCI) images for the whole 2015 year, which generated annual mean distributions of size/species-specific phytoplankton biomasses. The current study represents a meaningful attempt to achieve the satellite remote-sensing retrievals on the phytoplankton community composition, especially the species-specific phytoplankton biomass in the study region.

Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea.

Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages' dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE, are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately < 1 month) of phytoplankton to the changing external environment conditions is found by the wavelet analysis. This study concludes that phytoplankton community structures in the marginal seas remain stable and are year-independent over the past two decades, together with a short-term in-year cycle; this change rule need to be considered in future oceanographic studies.

A simple and effective method for monitoring floating green macroalgae blooms: a case study in the Yellow Sea.

Several algorithms have been proposed to detect floating macroalgae blooms in the global ocean. However, some of them are difficult or even impossible to routinely apply by non-experts because of performing a sophisticated atmospheric correction scheme or due to the mismatch in spectral bands from one sensor to another. Here, a generic, simple and effective method, referred to as the Floating Green Tide Index (FGTI), was proposed to detect floating green macroalgae blooms (GMB). The FGTI was defined as the difference between greenness and wetness features extracted from digital number (DN) observation through Tasseled Cap Transformation analysis, providing the advantage of bypassing the atmospheric correction procedure. Through cross-index and cross-sensor comparisons, the FGTI showed similar performance to the existing VB-FAH (Virtual-Baseline Floating macroAlgae Height) and FAI (Floating Algae Index) algorithms but proved more robust than the traditional NDVI (Normalized Difference Vegetation Index) in terms of response to perturbations by environmental conditions, viewing geometry, sun glint, and thin cloud contamination. Given the requirement for spectral bands in the current and planned satellite sensors, the FGTI design can easily be extended to any satellite sensor, and therefore provide an excellent data resource for studying GMB in any part of the global ocean.

Variability of particulate backscattering ratio and its relations to particle intrinsic features in the Bohai Sea, Yellow Sea, and East China Sea.

Using two field cruise observations collected during September and December 2016 in the Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS), our study explores the variability of the particulate backscattering ratio (i.e., a ratio of particulate backscattering, bbp in m-1, to particulate scattering, bp in m-1, denoted as b˜bp, dimensionless). A large variation of b˜bp (using 550 nm as a delegate) in magnitude is observed in the study regions, ranging from 0.0004 to 0.043 (with an average of 0.015 ± 0.0082), which implies optically complex water conditions. Spectral variation in b˜bp is analyzed quantitatively by our proposed so-called "spectral dependence index," K, recommended as a standard way to determine quantitatively the spectral dependence of b˜bp in water bodies worldwide. The driving mechanism on the b˜bp variability in the study regions is researched for the first time, based on those synchronous data on particle intrinsic attributes, herein mainly referring to particle concentration (TSM, for the content of total suspended matter), composition (using a ratio of Chla/TSM as a surrogate, where Chla refers to the content of chlorophyll a), mean particle size (DA), and mean apparent density (ρa). The TSM, Chla/TSM, and DA cumulatively contribute most (97.8%) of the b˜bp variability, while other factors, such as the ρa, show a weak influence (0.04%). Meanwhile, we model b˜bp with direct linkages to TSM, Chla/TSM, and DA by using a linear regression method, with low estimation errors (such as mean absolute percentage error, MAPE, of about 14%). In short, our findings promote an understanding on the essence of the b˜bp in the BS, YS, and ECS, and are significantly beneficial to the comprehensive grasp of those complex features on suspended particles and those related to biogeochemical processes in marine waters.

Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea.

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) observations at 550 nm (AOD550) for the highest quality flag assurance (QA = 3) were obtained to analyze spatiotemporal variations of aerosol optical properties over the Yellow and the Bohai Sea from 2002 to 2017. Spectral AOD observations at 470 nm (AOD470) and 660 nm (AOD660) were obtained to calculate Angstrom Exponent (AE470-660) and classify the aerosol types including clean continental (CC), clean maritime (CM) biomass and urban industrial (BUI), dust (D), and mixed (MXD) aerosol types. Results showed a very distinct spatial pattern of AOD distribution over the Bohai Sea which looks suspicious, i.e., high aerosol loadings (AOD > 0.8) throughout the entire time period, whereas relative low AOD distribution was observed over the adjacent land pixels especially in autumn and winter, which suggested that the DT algorithm might be influenced by a large number of sediments located in the Bohai Sea. Significant differences in spatial distributions were found in different seasons in terms of area coverage as a maximum number of pixels were available during autumn, and regional high and low aerosol loadings were observed during autumn and summer, respectively. Trend analysis from 2002 to 2017 showed that AOD was increased up to 0.04 over the Bohai Sea and decreased up to 0.04 over the Yellow Sea, and this trend varies from month to month. Aerosol classification showed significant contributions of BUI and CC over the region, and contributions of CM, DUST, and MXD aerosols over the Yellow Sea were relatively high compared to the Bohai Sea.

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images.

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (Rrc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.

Retrievals of phytoplankton community structures from in situ fluorescence measurements by HS-6P.

Phytoplankton community is an important organism indicator of monitoring water quality, and accurately estimating its composition and biomass is crucial for understanding marine ecosystems and biogeochemical processes. Identifying phytoplankton species remains a challenging task in the field of oceanography. Phytoplankton fluorescence is an important biological property of phytoplankton, whose fluorescence emissions are closely related to its community. However, the existing estimation approaches for phytoplankton communities by fluorescence are inaccurate and complex. In the present study, a new, simple method was developed for determining the Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes based on the fluorescence emission spectra measured from the HOBI Labs Hydroscat-6P (HS-6P) in the Bohai Sea, Yellow Sea, and East China Sea. This study used single bands, band ratios, and band combinations of the fluorescence signals to test their correlations with the six dominant algal species. The optimal band forms were confirmed, i.e., X1 (i.e., fl(700), which means the fluorescence emission signal at 700 nm band) for Chlorophytes, Cryptophytes, Dinoflagellates, and Prymnesiophytes (R = 0.947, 0.862, 0.911, and 0.918, respectively) and X7 (i.e., [fl(700) + fl(550)]/[fl(550)/fl(700)], where fl(550) denotes the fluorescence emission signal at 550 nm band) for Chrysophytes and Diatoms (R = 0.893 and 0.963, respectively). These established models here show good performances, yielding low estimation errors (i.e., root mean square errors of 0.16, 0.02, 0.06, 0.36, 0.18, and 0.03 for Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes, respectively) between in situ and modeled phytoplankton communities. Meanwhile, the spatial distributions of phytoplankton communities observed from both in situ and fluorescence-derived results agreed well. These excellent outputs indicate that the proposed method is to a large extent feasible and robust for estimating those dominant algal species in marine waters. In addition, we have applied this method to three vertical sections, and the retrieved vertical spatial distributions by this method can fill the gap of the common optical remote sensing approach, which usually only detects the sea surface information. Overall, our findings indicate that the proposed method by the fluorescence emission spectra is a potentially promising way to estimate phytoplankton communities, in particular enlarging the profiling information.

Variations of transparency derived from GOCI in the Bohai Sea and the Yellow Sea.

Secchi disk depth (Zsd), represents water transparency which is an intuitive indicator of water quality and can be used to derive inherent optical properties, chlorophyll-a concentrations, and primary productivity. In this study, the Zsd was derived from the Geostationary Ocean Color Imager (GOCI) data over the Bohai Sea (BHS) and the Yellow Sea (YS) using a regional tuned model. To validate the GOCI derived Zsd observations, in situ data, were collected for the BHS and YS regions. Results showed a good agreement between the GOCI derived Zsd observations and in situ measurements with a determination coefficient of 0.90, root mean square error of 2.17 m and mean absolute percent error of 24.56%. Results for diurnal variations showed an increasing trend of Zsd at the first and then decreasing, and all the maxima of Zsd in the central areas of the BHS and YS were found in the midday. For seasonal variations, higher values of Zsd, both in range and intensity, were observed in summer compared with those in winter. The reasons to explain the variations of Zsd have also been explored. Solar zenith angle (SOLZ) has an impact on the daily dynamics of Zsd, due to the influence of SOLZ on the attenuation of light radiation in water. The influence level of SOLZ on Zsd is largely determined by the water bodies' composition. The significant seasonal variations are mainly controlled by the stability of the water column stratification, because it can lead to the sediment resuspension and influence the growth and distribution of phytoplankton. Runoff and sediment discharge are not the main factors that impact the seasonal dynamics of Zsd. Tidal currents and mean currents may have influences on the variations of Zsd. However, due to the lack of in situ measurements to support, further studies are still needed.

Estimation of phytoplankton taxonomic groups in the Arctic Ocean using phytoplankton absorption properties: implication for ocean-color remote sensing.

Knowledge on the phenology and distribution of phytoplankton taxonomic groups (PTGs) represent valuable information when studying marine ecosystem, especially in the Arctic Ocean where rapid warming has drastic effects on sea-ice dynamics, which affect the marine food web. Taxonomic groups of phytoplankton can be discriminated based on their pigment signatures, which, in turn, impact their absorption spectra, given that different pigments have different absorption windows in the visible. Using concurrent measurements of phytoplankton diagnostic pigments and absorption spectra (aph) collected in the Bering and Chukchi Seas, a novel and direct approach was designed for simultaneously estimating the biomass concentrations of several PTGs (Ci) as well as their specific absorption coefficient. The chemotaxonomic tool CHEMTAX was applied to twelve diagnostic pigments measured by high-performance liquid chromatography (HPLC). Their results revealed that the phytoplankton community composition was made of nine groups, from which six dominant were identified: diatoms, dinoflagellates, c3-flagellate, haptophytes type 7, two types of prasinophytes. Out of 117 samples, twenty pairs of Ci derived by CHEMTAX and measured aph were randomly selected and used in a linear unmixing model to extract the specific absorption spectral of each group. This step was repeated 1000 times to provide the mean specific absorption of a given phytoplankton group. These specific absorption spectra were used to reconstruct total aph, which was consistent with the measured aph (R2 from 0.8 to 0.95) at all visible wavelengths (400-700 nm). The derived specific absorption spectra were further used with the measured aph(λ) at ten Moderate Resolution Imaging Spectroradiometer (MODIS) wavebands in a linear unmixing model to test the ability to retrieve the concentrations of PTGs from satellite remote sensing. A comparison between estimated and measured Ci showed that the approach used in this study performed best when retrieving five groups (i.e., dinoflagellates, c3-flagellate, haptophytes, two types of prasinophytes) from the nine initially identified using CHEMTAX with a mean absolute percentage error (MAPE) <35%, except for diatoms with a MAPE value of about 45%. Our approach provides a practical basis for estimation of PTGs using aph(λ) derived from satellite observations and field measurements.

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements.

Knowledge of phytoplankton community structures is important to the understanding of various marine biogeochemical processes and ecosystem. Fluorescence excitation spectra (F(λ)) provide great potential for studying phytoplankton communities because their spectral variability depends on changes in the pigment compositions related to distinct phytoplankton groups. Commercial spectrofluorometers have been developed to analyze phytoplankton communities by measuring the field F(λ), but estimations using the default methods are not always accurate because of their strong dependence on norm spectra, which are obtained by culturing pure algae of a given group and are assumed to be constant. In this study, we proposed a novel approach for estimating the chlorophyll a (Chl a) fractions of brown algae, cyanobacteria, green algae and cryptophytes based on a data set collected in the East China Sea (ECS) and the Tsushima Strait (TS), with concurrent measurements of in vivo F(λ) and phytoplankton communities derived from pigments analysis. The new approach blends various statistical features by computing the band ratios and continuum-removed spectra of F(λ) without requiring a priori knowledge of the norm spectra. The model evaluations indicate that our approach yields good estimations of the Chl a fractions, with root-mean-square errors of 0.117, 0.078, 0.072 and 0.060 for brown algae, cyanobacteria, green algae and cryptophytes, respectively. The statistical analysis shows that the models are generally robust to uncertainty in F(λ). We recommend using a site-specific model for more accurate estimations. To develop a site-specific model in the ECS and TS, approximately 26 samples are sufficient for using our approach, but this conclusion needs to be validated in additional regions. Overall, our approach provides a useful technical basis for estimating phytoplankton communities from measurements of F(λ).

Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea.

In this paper, a new daytime sea fog detection algorithm has been developed by using Geostationary Ocean Color Imager (GOCI) data. Based on spectral analysis, differences in spectral characteristics were found over different underlying surfaces, which include land, sea, middle/high level clouds, stratus clouds and sea fog. Statistical analysis showed that the Rrc (412 nm) (Rayleigh Corrected Reflectance) of sea fog pixels is approximately 0.1-0.6. Similarly, various band combinations could be used to separate different surfaces. Therefore, three indices (SLDI, MCDI and BSI) were set to discern land/sea, middle/high level clouds and fog/stratus clouds, respectively, from which it was generally easy to extract fog pixels. The remote sensing algorithm was verified using coastal sounding data, which demonstrated that the algorithm had the ability to detect sea fog. The algorithm was then used to monitor an 8-hour sea fog event and the results were consistent with observational data from buoys data deployed near the Sheyang coast (121°E, 34°N). The goal of this study was to establish a daytime sea fog detection algorithm based on GOCI data, which shows promise for detecting fog separately from stratus.

Variability in the backscattering efficiency of particles in the Bohai and Yellow Seas and related effects on optical properties.

The backscattering efficiency of particles is a crucial factor that relates light backscattering with biogeochemical properties. In this study, based on in situ measurements of the backscattering coefficient (bbp(λ)), particle biogeochemical variables and remote sensing reflectance (Rrs(λ)) in two typical shallow and semi-enclosed seas, namely the Bohai Sea (BS) and Yellow Sea (YS) during the late spring, late summer and late autumn, we examined particulate pseudo-backscattering efficiency variability at 640 nm (P_Qbbe(640)) and related optical effects. The results show that the P_Qbbe(640) levels varied by nearly two orders for all of the samples examined. This high degree of P_Qbbe(640) variability significantly affected bbp(640) and the mass-specific backscattering coefficient (bbp*(640)), showing that approximately 63.7% and 20.8% of the variability in the bbp*(640) and bbp(640) was attributed to the P_Qbbe(640), respectively. More importantly, consistent with the observations of Wang et al. [J. Geophys. Res.: Oceans 121, 3955 (2016)], the P_Qbbe(640) results clearly showed two clusters and this clustering changed the relationships between bbp*(640), bbp(640) and Rrs(640) with the biogeochemical variables. However, we confirm that P_Qbbe(640) clustering generally remained intact across seasons. Therefore, a simple scheme based on a threshold of the P_Qbbe(640) data is proposed for the classification of particle types. With this classification, impacts of P_Qbbe(640) on bbp*(640) and bbp(640) were clearly reduced, and co-variation trends of bbp*(640), bbp(640) and Rrs(640) with biogeochemical variables can be in turn more accurately described. Overall, this study provides general information on P_Qbbe(640) variability in the BS and the YS and consequent effects on optical properties. The scheme for particle type classification may also provide a useful basis for better modeling marine biogeochemical processes related to particulate backscattering and for the development of ocean color algorithms.

Innovative GOCI algorithm to derive turbidity in highly turbid waters: a case study in the Zhejiang coastal area.

An innovative algorithm is developed and validated to estimate the turbidity in Zhejiang coastal area (highly turbid waters) using data from the Geostationary Ocean Color Imager (GOCI). First, satellite-ground synchronous data (n = 850) was collected from 2014 to 2015 using 11 buoys equipped with a Yellow Spring Instrument (YSI) multi-parameter sonde capable of taking hourly turbidity measurements. The GOCI data-derived Rayleigh-corrected reflectance (R(rc)) was used in place of the widely used remote sensing reflectance (R(rs)) to model turbidity. Various band characteristics, including single band, band ratio, band subtraction, and selected band combinations, were analyzed to identify correlations with turbidity. The results indicated that band 6 had the closest relationship to turbidity; however, the combined bands 3 and 6 model simulated turbidity most accurately (R(2) = 0.821, p<0.0001), while the model based on band 6 alone performed almost as well (R(2) = 0.749, p<0.0001). An independent validation data set was used to evaluate the performances of both models, and the mean relative error values of 42.5% and 51.2% were obtained for the combined model and the band 6 model, respectively. The accurate performances of the proposed models indicated that the use of R(rc) to model turbidity in highly turbid coastal waters is feasible. As an example, the developed model was applied to 8 hourly GOCI images on 30 December 2014. Three cross sections were selected to identify the spatiotemporal variation of turbidity in the study area. Turbidity generally decreased from near-shore to offshore and from morning to afternoon. Overall, the findings of this study provide a simple and practical method, based on GOCI data, to estimate turbidity in highly turbid coastal waters at high temporal resolutions.

Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms.

A new scheme has been proposed by Lee et al. (2014) to reconstruct hyperspectral (400 - 700 nm, 5 nm resolution) remote sensing reflectance (Rrs(λ), sr-1) of representative global waters using measurements at 15 spectral bands. This study tested its applicability to optically complex turbid inland waters in China, where Rrs(λ) are typically much higher than those used in Lee et al. (2014). Strong interdependence of Rrs(λ) between neighboring bands (≤ 10 nm interval) was confirmed, with Pearson correlation coefficient (PCC) mostly above 0.98. The scheme of Lee et al. (2014) for Rrs(λ) re-construction with its original global parameterization worked well with this data set, while new parameterization showed improvement in reducing uncertainties in the reconstructed Rrs(λ). Mean absolute error (MAERrs(λi)) in the reconstructed Rrs(λ) was mostly < 0.0002 sr-1 between 400 and 700nm, and mean relative error (MRERrs(λi)) was < 1% when the comparison was made between reconstructed and measured Rrs(λ) spectra. When Rrs(λ) at the MODIS bands were used to reconstruct the hyperspectral Rrs(λ), MAERrs(λi) was < 0.001 sr-1 and MRERrs(λi) was < 3%. When Rrs(λ) at the MERIS bands were used, MAERrs(λi) in the reconstructed hyperspectral Rrs(λ) was < 0.0004 sr-1 and MRERrs(λi) was < 1%. These results have significant implications for inversion algorithms to retrieve concentrations of phytoplankton pigments (e.g., chlorophyll-a or Chla, and phycocyanin or PC) and total suspended materials (TSM) as well as absorption coefficient of colored dissolved organic matter (CDOM), as some of the algorithms were developed from in situ Rrs(λ) data using spectral bands that may not exist on satellite sensors.

Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: a case study in Lake Dianchi.

Using remote sensing reflectance (R(rs)(λ), sr(-1)) and phycocyanin (PC, mg m(-3)) pigment data as well as other bio-optical data collected from two cruises in September and December 2009 in Lake Dianchi (a typical plateau lake of China), we developed a practical approach to estimate PC concentrations that could be applied directly to Landsat measurements. The visible and near-IR bands as well as their band ratios of simulated Landsat data were used as inputs to the algorithms, where the algorithm coefficients for each Landsat sensor were determined through multivariate regressions. The coefficients of determination (R(2)) between the R(rs)-modeled and measured PC were all > 0.97 for the spectral bands corresponding to Landsat 8 OLI, Landsat 7 ETM + , Landsat 5 TM, and Landsat 4 TM, with mean absolute percentage errors (MAPE) < 10% for PC ranging between ~80 and 700 mg m(-3) (n = 14). The algorithms were further evaluated using an independent data set (n = 14), yielding larger but still acceptable MAPE (~30%) for PC ranging between ~80 and 500 mg m(-3). Application of the approach to Landsat 8 measurements over Lake Dianchi suggests potential use of the approach for periodical assessment of the lake's bloom conditions, yet its empirical nature together with the lack of specific narrow bands on Landsat sensors to explicitly account for the PC absorption around 625 nm calls for extra caution when applied to other eutrophic lakes.