Satellite observations of suspended particulate matter concentration in Lake Gaoyou in the past four decades.

Suspended Particulate Matter (SPM) concentration stands as a pivotal determinant of water quality within lake ecosystems. However, comprehension of the enduring dynamics of SPM within lakes is severely hindered due to a shortage of long-term records. Our research has developed a robust remote sensing algorithm to retrieve the SPM concentration in Lake Gaoyou, situated in the lower reaches of the Huai River basin in China. The algorithm demonstrates commendable performance, with an uncertainty of 28.68 %. Leveraging Landsat series sensors imagery, our investigation yields high spatial resolution SPM concentration maps, which first provide a four-decades record of the SPM distribution within Lake Gaoyou. Our findings unveil a significant annual reduction of 1.35 mg L-1 in SPM concentration over the past four decades. This notable decline is probably attributable to a series of ecological initiatives to enhancing the management of the eco-friendly within the basin. Furthermore, our research delineated the influence of environmental factors on the intra-annual SPM dynamics across distinct spatial domains, encompassing the natural inlet region, semi-obstructed inlet region and outlet areas within the lake The SPM concentration in the natural inlet region exhibits a conspicuous correlation with precipitation. Increased precipitation induces runoff within the basin, facilitating the transport of suspended solids and sediment into the lake, consequently augmenting SPM levels. Conversely, the semi-obstructed inlet and outlet areas are predominantly influenced by the wind field, with variations in SPM attributed to sediment resuspension caused by water mixing driven by wind forcing. Our research can be considered an important reference to the evaluation of the management of the lake over long periods.

Estimating four-decadal variations of seagrass distribution using satellite data and deep learning methods in a marine lagoon.

Seagrasses are marine flowering plants that inhabit shallow coastal and estuarine waters and serve vital ecological functions in marine ecosystems. However, seagrass ecosystems face the looming threat of degradation, necessitating effective monitoring. Remote-sensing technology offers significant advantages in terms of spatial coverage and temporal accessibility. Although some remote sensing approaches, such as water column correction, spectral index-based, and machine learning-based methods, have been proposed for seagrass detection, their performances are not always satisfactory. Deep learning models, known for their powerful learning and vast data processing capabilities, have been widely employed in automatic target detection. In this study, a typical seagrass habitat (Swan Lake) in northern China was used to propose a deep learning-based model for seagrass detection from Landsat satellite data. The performances of UNet and SegNet at different patch scales for seagrass detection were compared. The results showed that the SegNet model at a patch scale of 16 × 16 pixels worked well, with validation accuracy and loss of 96.3 % and 0.15, respectively, during training. Evaluations based on the test dataset also indicated good performance of this model, with an overall accuracy >95 %. Subsequently, the deep learning model was applied for seagrass detection in Swan Lake between 1984 and 2022. We observed a noticeable seasonal variation in germination, growth, maturation, and shrinkage from spring to winter. The seagrass area decreased over the past four decades, punctuated by intermittent fluctuations likely attributed to anthropogenic activities, such as aquaculture and construction development. Additionally, changes in landscape ecology indicators have demonstrated that seagrass experiences severe patchiness. However, these problems have weakened recently. Overall, by combining remote sensing big data with deep learning technology, our study provides a valuable approach for the highly precise monitoring of seagrass. These findings on seagrass area variation in Swan Lake offer significant information for seagrass restoration and management.

Remote sensing detection of seagrass distribution in a marine lagoon (Swan Lake), China.

Seagrass, a submerged flowering plant, is widely distributed in coastal shallow waters and plays a significant role in maintaining marine biodiversity and carbon cycles. However, the seagrass ecosystem is currently facing degradation, necessitating effective monitoring. Satellite remote sensing observations offer distinct advantages in spatial coverage and temporal frequency. In this study, we focused on a marine lagoon (Swan Lake), located in the Shandong Peninsula of China which is characterized by a large and typical seagrass population. We conducted an analysis of remote sensing reflectance of seagrass and other objectives using a comprehensive Landsat satellite dataset spanning from 2002 to 2022. Subsequently, we constructed Seagrass Index I (SSI-I) and Seagrass Index II (SSI-II), and used them to develop a stepwise model for seagrass detection from Landsat images. Validation was performed using in situ acoustic survey data and visual interpretation, revealing the good performance of our model with an overall accuracy exceeding 0.90 and a kappa coefficient around 0.80. The long-term analysis (2002-2022) of the seagrass distribution area in Swan Lake, generated from Landsat data using our model, indicated that the central area of Swan Lake sustains seagrass for the longest duration. Seagrass in Swan Lake exhibits a regular seasonal variation, including seeding in early spring, growth in spring-summer, maturation in the middle of summer, and shrinkage in autumn. Furthermore, we observed an overall decreasing trend in the seagrass area over the past 20 years, while occasional periods of seagrass restoration were also observed. These findings provide crucial information for seagrass protection, marine blue carbon studies, and related endeavors in Swan Lake. Moreover, our study offers a valuable alternative approach that can be implemented for seagrass monitoring using satellite observations in other coastal regions.

A global satellite observation of phytoplankton taxonomic groups over the past two decades.

Marine phytoplankton fuel the oceanic biotic chain, determine the carbon sequestration levels, and are crucial for the global carbon cycle and climate change. In the present study, we show a near-two-decadal (2002-2022) spatiotemporal distribution of global phytoplankton abundance, proxy as dominant phytoplankton taxonomic groups (PTGs), with a newly developed remote sensing model. Globally, six chief PTGs, namely chlorophytes (~26%), diatoms (~24%), haptophytes (~15%), cryptophytes (~10%), cyanobacteria (~8%), and dinoflagellates (~3%), explain most of the variation (~86%) in phytoplankton assemblages. Spatially, diatoms generally dominate high latitudes, marginal seas, and coastal upwelling zones, whereas chlorophytes and haptophytes control the open oceans. Satellite observations reveal a gentle multi-annual trend of the PTGs in the major oceans, indicative of roughly "unchanged" conditions on the total biomass or compositions of the phytoplankton community. Jointly, "changed" status applies to a short-term (seasonal) timescale: (1) Fluctuations of PTGs exhibit different amplitudes among different subregions, together with a general rule-more intense vibration in the Northern Hemisphere and polar oceans than other zones; (2) diatoms and haptophytes vary more dramatically than other PTGs in a global-scale scope. These findings provide a clear picture of the global phytoplankton community composition and can improve our understanding of their state and further analysis of marine biological processes.

Satellite estimation of suspended particle types using a backscattering efficiency-based model in the marginal seas.

The particle composition of suspended matter provides crucial information for a deeper understanding of marine biogeochemical processes and environmental changes. Particulate backscattering efficiency (Qbbe(λ)) is critical to understand particle composition, and a Qbbe(λ)-based model for classifying particle types was proposed. In this study, we evaluated the applicability of the Qbbe(λ)-based model to satellite observations in the shallow marginal Bohai and Yellow Seas. Spatiotemporal variations of the particle types and their potential driving factors were studied. The results showed that the Qbbe(λ) products generated from Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua agreed well with the in situ measured values, with determination coefficient, root mean square error, bias, and mean absolute percentage error of 0.76, 0.007, 16.5%, and 31.0%, respectively. This result verifies the satellite applicability of the Qbbe(λ)-based model. Based on long-term MODIS data, we observed evident spatiotemporal variations of the Qbbe(λ), from which distinct particle types were identified. Coastal waters were often dominated by minerals, with high Qbbe(λ) values, though their temporal changes were also observed. In contrast, waters in the offshore regions showed clear changes in particle types, which shifted from organic-dominated with low Qbbe(λ) levels in summer to mineral-dominated with high Qbbe(λ) values in winter. We also observed long-term increasing and decreasing trends in Qbbe(λ) in some regions, indicating a relative increase in the proportions of mineral and organic particles in the past decades, respectively. These spatiotemporal variations of Qbbe(λ) and particle types were probably attributed to sediment re-suspension related to water mixing driven by wind and tidal forcing, and to sediment load associated with river discharge. Overall, the findings of this study may provide valuable proxies for better studying marine biogeochemical processes, material exchanges, and sediment flux.

Phytoplankton package effect in oceanic waters: Influence of chlorophyll-a and cell size.

In this study, the interaction between the packaging effect (Qa⁎) and total chlorophyll-a concentration (Ct) or total size index (SIt) was investigated to explore the potential bio-optical mechanism in phytoplankton cells in the global oceans. In addition, the long-term spatiotemporal characteristics of these interactions were necessary for grasping their variation. Numerous in situ surface measurements (phytoplankton pigment and absorption coefficients) from the global oceans were analyzed first, and then correlation and causality analyses were performed on the satellite-deduced Qa⁎, Ct, and SIt in the global oceans during 2002-2020. The results show a negative correlation between Qa⁎ and Ct or SIt in the low latitudes (30°S-30°N) and a positive correlation in the middle latitudes (30°S-55°S and 30°N-55°N). The causality analysis reveals a mutual and asymmetric cause-effect relationship between Qa⁎ and Ct or SIt in the low latitudes. The stabilization effect of Qa⁎ contributes to a 10%-50% variation in Ct and SIt, with 40%-60% uncertainty of Qa⁎ caused by Ct and SIt in the low latitudes, which is inverse in the middle latitudes. The remaining contribution to each variable mainly originates from long-term trends and noise. Combining the analysis between Qa⁎ and the irradiance, the balancing processes in phytoplankton cells are different in the low (phytoplankton-driving mode) and middle latitudes (irradiance-driving mode), which is related to photoacclimation and photoinhibition. The analyses provide insights into the quantitative interpretation of the relationship between Qa⁎ and Ct or SIt, which contribute knowledge that has not been previously reported.

Understanding optical absorption associated with phytoplanktonic groups in the marginal seas.

Marine phytoplankton absorption plays an important role in oceanic biological productivity and ecological environmental dynamics. Understanding the optical absorption variability associated with phytoplanktonic groups still remains a challenge. In this study, samples (n = 206) were collected for the marginal seas of the northwest Pacific Ocean from six cruise surveys that covered different seasons. Using in situ parameters, including phytoplankton absorption coefficients and concentrations of the phytoplanktonic groups derived from phytoplankton pigments collected with high-performance liquid chromatography (HPLC), we developed a Gaussian model to characterize the specific absorption spectra of eight phytoplanktonic groups, including diatoms, chlorophytes, cryptophytes, cyanobacteria, prymnesiophytes, prasinophytes, dinoflagellates, and chrysophytes, without the package effect. The model was established by accurately identifying for the numbers and locations of the Gaussian peaks and their corresponding half-wave widths. The proposed model produced promising results, and a leave-one-out cross validation generated R2 values exceeding 0.7 for the whole visible light range and above 0.85 (correspondingly MAPE <40%) for the simulated wave bands, excluding the range of 550-650 nm. Meanwhile, a comparison with several spectra observed in the lab showed a high degree of similarity, indicative of the superior performance of our model. Applying the documented specific absorption spectra to the investigated water bodies (whether water surface or profiles) enabled us to quantify the absorption coefficients from different phytoplanktonic groups and characterize their relative contributions to the total. The findings of this study support our understanding of the dynamics of phytoplankton community structure with optical data.

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.

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.