Spatiotemporal Variability of Chlorophyll-a and Sea Surface Temperature, and Their Relationship with Bathymetry over the Coasts of UAE

The catastrophic implication of harmful algal bloom (HAB) events in the Arabian Gulf is a strong indication that the study of the spatiotemporal distribution of chlorophyll-a and its relationship with other variables is critical. This study analyzes the relationship between chlorophyll-a (Chl-a) and sea surface temperature (SST) and their trends in the Arabian Gulf and the Gulf of Oman along the United Arab Emirates coast. Additionally, the relationship between bathymetry and Chl-a and SST was examined. The MODIS Aqua product with a resolution of 1 × 1 km2 was employed for both chlorophyll-a and SST covering a timeframe from 2003 to 2019. The highest concentration of chlorophyll-a was seen in the Strait of Hormuz with an average of 2.8 mg m−3, which is 1.1 mg m−3 higher than the average for the entire study area. Three-quarters of the study area showed a significant correlation between the Chl-a and SST. The shallow (deep) areas showed a strong positive (negative) correlation between the Chl-a and SST. The results indicate the presence of trends for both variables across most of the study area. SST significantly increased in more than two-thirds of the study area in the summer with no significant trends detected in the winter.

2019‒2020 Australian bushfire air particulate pollution and impact on the South Pacific Ocean

During late 2019 and early 2020, Australia experienced one of the most active bushfire seasons that advected large emissions over the adjacent ocean. Herein, we present a comprehensive research on mixed atmospheric aerosol particulate pollution emitted by wildfires in the atmosphere and the ocean. Based on a wide range of physical and biochemical data, including the Aerosol Robotic Network, multi-satellite observations, and Argo floats, we investigated the spatio-temporal variations and mixed compositions of aerosol particles, deposition in the coastal waters of eastern Australia and the South Pacific Ocean, and biogeochemical responses in the water column. Four types of wildfire-derived mixed particles were classified by using the optical properties of aerosols into four types, including the background aerosols, mineral dust, wildfire smoke particles, and residual smoke. The coarse particles accounted for more than 60% of the mineral dust on 22 November 2019 in the Tasman Sea; afterwards, during the wildfire smoke episode from December 2019 to January 2020, the particles affected large areas of the atmosphere such as eastern Australia, the South Pacific Ocean, and South America. The maximum value of the aerosol optical depth reached 2.74, and the proportion of fine particles accounted for 98.9% in the smoke episode. Mineral dust and smoke particles from the fire emissions changed the particle composition in the surface ocean. Particle deposition accounted for increases in chlorophyll-a concentration (Chla) standardized anomaly up to maximum of 23.3 with a lag time of less than 8 days. In the vertical direction, float observations showed the impact of exogenous particles on the water column could up to 64.7 m deep, resulting in Chla of 1.85 mg/m3. The high Chla lasted for a minimum period of two months until it returned to normal level.

Remote Sensing of Dispersed Oil Pollution in the Ocean-The Role of Chlorophyll Concentration

In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies of changes on various physical and environmental factors, can be estimated using simulation techniques. Two models were combined to examine the influence of oceanic water type on the visibility of dispersed oil: the Monte Carlo radiative transfer model and the Lorenz-Mie model for spherical oil droplets suspended in seawater. Remote sensing reflectance, R_rs, was compared for natural ocean water models representing oligotrophic, mesotrophic and eutrophic environments (characterized by chlorophyll-a concentrations of 0.1, 1 and 10 mg/m³, respectively) and polluted by three different kinds of oils: biodiesel, lubricant oil and crude oil. We found out that dispersed oil usually increases R_rs values for all types of seawater, with the highest effect for the oligotrophic ocean. In the clearest studied waters, the absolute values of R_rs increased 2-6 times after simulated dispersed oil pollution, while R_rs band ratios routinely applied in bio-optical models decreased up to 80%. The color index, CI, was nearly double reduced by dispersed biodiesel BD and lubricant oil CL, but more than doubled by crude oil FL.

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b bp (555) (m-1). We developed a simple empirical algorithm for deriving b bp (555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R rs (λ) (sr-1), and the spectral particulate backscattering coefficients, b bp (λ). The LH metric used is defined as the magnitude of R rs (555) relative to a linear baseline drawn between R rs (490) and R rs (670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

A Spectral-Spatial Features Integrated Network for Hyperspectral Detection of Marine Oil Spill

Marine oil spills are one of the most serious problems of marine environmental pollution. Hyperspectral remote sensing has been proven to be an effective tool for monitoring marine oil spills. To make full use of spectral and spatial features, this study proposes a spectral-spatial features integrated network (SSFIN) and applies it for hyperspectral detection of a marine oil spill. Specifically, 1-D and 2-D convolutional neural network (CNN) models have been employed for the extraction of the spectral and spatial features, respectively. During the stage of spatial feature extraction, three consecutive convolution layers are concatenated to achieve the fusion of multilevel spatial features. Next, the extracted spectral and spatial features are concatenated and fed to the fully connected layer so as to obtain the joint spectral-spatial features. In addition, L2 regularization is applied to the convolution layer to prevent overfitting, and dropout operation is employed to the full connection layer to improve the network performance. The effectiveness of the method proposed here has firstly been verified on the Pavia University dataset with competitive classification experimental results. Eventually, the experimental results upon oil spill datasets demonstrate the strong capacity of oil spill detection by this method, which can effectively distinguish thick oil film, thin oil film, and seawater.

Variability in the Sea Surface Temperature Gradient and Its Impacts on Chlorophyll-a Concentration in the Kuroshio Extension

Sixteen years of satellite observational data in the Northwestern Pacific Ocean are used to describe the variability in the sea surface temperature (SST) gradient and its impact on chlorophyll-a concentrations (Chl-a). Spatially, a meridional dependence is identified in which the SST gradient increases to the north in association with elevated Chl-a. Temporally, the seasonal variability shows a large SST gradient and high Chl-a in winter and spring, while the SST gradient and Chl-a are much lower in summer. The seasonal variability in Chl-a leads the variability in the SST gradient by one month. A significant correlation between the SST gradient and Chl-a in the anomalous field is obtained only in the western section of the Kuroshio extension (KE) and the highest correlation is identified without any lags. An index for the section is defined as the proportion of the number of times that the SST gradient magnitude is anomalously large in each year, and the index is highly related to the stability of the KE and has a prominent influence on Chl-a in the region. An anomalously large positive (negative) SST gradient magnitude occurs when the KE is unstable (stable) and the corresponding Chl-a is high (low).

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

Atmospheric correction over the ocean for hyperspectral radiometers using multi-angle polarimetric retrievals

We developed a fast and accurate polynomial based atmospheric correction (POLYAC) algorithm for hyperspectral radiometric measurements, which parameterizes the atmospheric path radiances using aerosol properties retrieved from co-located multi-wavelength multi-angle polarimeter (MAP) measurements. This algorithm has been applied to co-located spectrometer for planetary exploration (SPEX) airborne and research scanning polarimeter (RSP) measurements, where SPEX airborne was used as a proxy of hyperspectral radiometers, and RSP as the MAP. The hyperspectral remote sensing reflectance obtained from POLYAC is accurate when compared to Aerosol Robotic Network (AERONET), and Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. POLYAC provides a robust alternative atmospheric correction algorithm for hyperspectral or multi-spectral radiometric measurements for scenes involving coastal oceans and/or absorbing aerosols, where traditional atmospheric correction algorithms are less reliable.

Uncertainty assessment of unattended above-water radiometric data collection from research vessels with the Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC)

We used above- and below-water radiometry measurements collected during a research voyage in the eastern Indian Ocean to assess uncertainties in deriving the remote sensing reflectance, R_rs, from unattended above-water radiometric data collection with the In-Situ Marine Optics Pty. Ltd. (IMO) Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC). To achieve this, the R_rs values derived from using the latest version of this hyperspectral radiometer were compared to values obtained from two in-water profiling radiometer systems of rather general use in the ocean optics research community, i.e., the Biospherical Instruments Inc. Compact Optical Profiling System (C-OPS) and the Seabird HyperPro II. Our results show that unattended, carefully quality-controlled, DALEC measurements provide R_rs for wavelengths < 600 nm that match those derived from the in-water systems with no bias and a dispersion of about 8%, provided that the appropriate technique is used to quantify the contribution of sky light reflection to the measured signal. The dispersion is larger (25-50%) for red bands, which is expected for clear oligotrophic waters as encountered during the voyage, where ∼2 10^-5 < R_rs < ∼2 10^-4 sr^-1. For comparison, the two in-water systems provided R_rs in agreement within 4% for wavelengths < 600 nm.

Automatic Detection of Optical Signatures within and around Floating Tonga-Fiji Pumice Rafts Using MODIS, VIIRS, and OLCI Satellite Sensors

An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km2. The pumice’s far-reaching effects from its origin in the Tonga region to Fiji and the methods of automatic detection using satellite imagery are described, making it possible to track the westward drift of the pumice raft over 43 days. Level 2 Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Color Instrument (OLCI), and Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) imagery of sea surface temperature, chlorophyll-a concentration, quasi-surface (i.e., Rayleigh-corrected) reflectance, and remote sensing reflectance were used to distinguish consolidated and fragmented rafts as well as discolored and mesotrophic waters. The rafts were detected by a 1 to 3.5 °C enhancement in the MODIS-derived “sea surface temperature” due to the emissivity difference of the raft material. Large plumes of discolored waters, characterized by higher satellite reflectance/backscattering of particles in the blue than surrounding waters (and corresponding to either submersed pumice or associated white minerals), were associated with the rafts. The discolored waters had relatively lower chlorophyll-a concentration, but this was artificial, resulting from the higher blue/red reflectance ratio caused by the reflective pumice particles. Mesotrophic waters were scarce in the region of the pumice rafts, presumably due to the absence of phytoplanktonic response to a silicium-rich pumice environment in these tropical oligotrophic environments. As beach accumulations around Pacific islands surrounded by coral shoals are a recurrent phenomenon that finds its origin far east in the ocean along the Tongan trench, monitoring the events from space, as demonstrated for the 7 August 2019 eruption, might help mitigate their potential economic impacts.

A biological Indian Ocean Dipole event in 2019

The 2019 positive Indian Ocean Dipole (IOD) event in the boreal autumn was the most serious IOD event of the century with reports of significant sea surface temperature (SST) changes in the east and west equatorial Indian Ocean. Observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2020 are used to study the significant biological dipole response that occurred in the equatorial Indian Ocean following the 2019 positive IOD event. For the first time, we propose, identify, characterize, and quantify the biological IOD. The 2019 positive IOD event led to anomalous biological activity in both the east IOD zone and west IOD zone. The average chlorophyll-a (Chl-a) concentration reached over ~ 0.5 mg m^-3 in 2019 in comparison to the climatology Chl-a of ~ 0.3 mg m^-3 in the east IOD zone. In the west IOD zone, the biological activity was significantly depressed. The depressed Chl-a lasted until May 2020. The anomalous ocean biological activity in the east IOD zone was attributed to the advection of the higher-nutrient surface water due to enhanced upwelling. On the other hand, the dampened ocean biological activity in the west IOD zone was attributed to the stronger convergence of the surface waters than that in a normal year.

Spectral Characterization of Dissolved Organic Matter in Seawater and Sediment Pore Water from the Arctic Fjords (West Svalbard) in Summer

Fjords in the high Arctic, as aquatic critical zones at the interface of land-ocean continuum, are undergoing rapid changes due to glacier retreat and climate warming. Yet, little is known about the biogeochemical processes in the Arctic fjords. We measured the nutrients and the optical properties of dissolved organic matter (DOM) in both seawater and sediment pore water, along with the remote sensing data of the ocean surface, from three West Svalbard fjords. A cross-fjord comparison of fluorescence fingerprints together with downcore trends of salinity, Cl−, and PO43− revealed higher impact of terrestrial inputs (fluorescence index: ~1.2–1.5 in seawaters) and glaciofluvial runoffs (salinity: ~31.4 ± 2.4 psu in pore waters) to the southern fjord of Hornsund as compared to the northern fjords of Isfjorden and Van Mijenfjorden, tallying with heavier annual runoff to the southern fjord of Hornsund. Extremely high levels of protein-like fluorescence (up to ~4.5 RU) were observed at the partially sea ice-covered fjords in summer, in line with near-ubiquity ice-edge blooms observed in the Arctic. The results reflect an ongoing or post-phytoplankton bloom, which is also supported by the higher levels of chlorophyll a fluorescence at the ocean surface, the very high apparent oxygen utilization through the water column, and the nutrient drawdown at the ocean surface. Meanwhile, a characteristic elongated fluorescence fingerprint was observed in the fjords, presumably produced by ice-edge blooms in the Arctic ecosystems. Furthermore, alkalinity and the humic-like peaks showed a general downcore accumulation trend, which implies the production of humic-like DOM via a biological pathway also in the glaciomarine sediments from the Arctic fjords.

Detection of Coccolithophore Blooms With BioGeoChemical-Argo Floats

Coccolithophores (calcifying phytoplankton) form extensive blooms in temperate and subpolar oceans as evidenced from ocean-color satellites. This study examines the potential to detect coccolithophore blooms with BioGeoChemical-Argo (BGC-Argo) floats, autonomous ocean profilers equipped with bio-optical and physicochemical sensors. We first matched float data to ocean-color satellite data of calcite concentration to select floats that sampled coccolithophore blooms. We identified two floats in the Southern Ocean, which measured the particulate beam attenuation coefficient (c p) in addition to two core BGC-Argo variables, Chlorophyll-a concentration ([Chl-a]) and the particle backscattering coefficient (b bp). We show that coccolithophore blooms can be identified from floats by distinctively high values of (1) the b bp/c p ratio, a proxy for the refractive index of suspended particles, and (2) the b bp/[Chl-a] ratio, measurable by any BGC-Argo float. The latter thus paves the way to global investigations of environmental control of coccolithophore blooms and their role in carbon export.

Remote Sensing and Argo Float Observations Reveal Physical Processes Initiating a Winter-Spring Phytoplankton Bloom South of the Kuroshio Current Near Shikoku

BIO-Argo float (chlorophyll a (Chl-a), temperature, and salinity profiles) and remote sensing data (Chl-a, photosynthetic available radiation (PAR), and wind) located south of the Kuroshio current near Shikoku from September 2018 to May 2019 were used to study phytoplankton bloom and their mechanisms of development in open oceans. Results show that higher (lower) Chl-a concentrations are correlated with a deeper (shallower) mixed layer (RPearson = 0.77, Rcrit = 0.12 (alpha = 0.05, n = 263)) compared to the average of Chl-a and mixed layer depth (0.13 mg/m3 and 105 m). The average net accumulation rates (r) of phytoplankton were close to 0.08 d−1. An increasing r corresponds to a gradually increasing surface Chl-a (S (Chl-a): 0–20 m average Chl-a) and integrated Chl-a inventory (I (Chl-a): integrated Chl-a from surface to euphotic depth). These phenomena indicate that the mechanism of winter-spring phytoplankton blooms is consistent with the dilution-recoupling hypotheses (DRH). During the bloom formation, winter deep mixing and eddy-wind Ekman pumping are enhanced by a strong winter monsoon. The enhancement may disturb predator–prey interactions and dilute zooplankton in deep mixed layers. Moreover, winter deep mixing and eddy-wind Ekman pumping can cause the nutrients to be transported into the euphotic layer, which can promote the growth of phytoplankton and increase grazing. During the bloom extinction, the stratification strengthens and the intensity of light increases; this increases grazing and nutrient consumption, and decreases the phytoplankton bloom significantly (S (Chl-a) and I (Chl-a) increase by 0.3 mg/m3 and 27 mg/m2, respectively). The output from a biogeochemistry model shows that nutrients are consistent with the temporal distribution of S (Chl-a) and I (Chl-a). Our results suggest that physical processes (deep winter mixing and eddy-wind Ekman pumping) under the DHR framework are critical factors for winter-spring blooms in open oceans with an anticyclone eddy.

Integrating In Situ and Ocean Color Data to Evaluate Ecological Quality under the Water Framework Directive

The Water Framework Directive (WFD) aims at evaluating the ecological status of European coastal water bodies (CWBs). This is a rather complex task and first requires the use of long-term databases to assess the effect of anthropogenic pressure on biological communities. An in situ dataset was assembled using concomitant biological, i.e., chlorophyll a (Chl a) and environmental data, covering the years from 1995 to 2014, to enable a comprehensive assessment of eutrophication in the Western Iberia Coast (WIC). Given the temporal gaps in the dataset, especially in terms of Chl a, satellite observations were used to complement it. Positive relationships between Chl a 90th percentile and nitrogen concentrations were obtained. The Land-Uses Simplified Index (LUSI), as a pressure indicator, showed no relationship with Chl a, except in Galicia, but it highlighted a higher continental pressure in the Portuguese CWBs in comparison with Galician waters. In general terms, the trophic index (TRIX) showed that none of the CWBs were in degraded conditions. Nevertheless, the relatively high TRIX and LUSI values obtained for the water body in front of Tagus estuary suggest that this area should be subject to continued monitoring. Results highlighted the usefulness of satellite data in water quality assessments and set the background levels for the implementation of operational monitoring based on satellite Chl a. In the future, low uncertainty and harmonized satellite products across countries should be provided. Moreover, the assessment of satellite-based eutrophication indicators should also include metrics on phytoplankton phenology and community structure.

Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

Assessing the Trend of the Trophic State of Lake Ladoga Based on Multi-Year (1997-2019) CMEMS GlobColour-Merged CHL-OC5 Satellite Observations

The trophic state of Lake Ladoga was studied during the period 1997–2019, using the Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour-merged chlorophyll-a OC5 algorithm (GlobColour CHL-OC5) satellite observations. Lake Ladoga, in general, is mesotrophic but certain parts of the lake have been eutrophic since the 1960s due to the discharge of wastewater from industrial, urban, and agricultural sources. Since then, many ecological assessments of the Lake’s state have been made. These studies have indicated that various changes are taking place in the lake and continuous monitoring of the lake is essential to update the current knowledge of its state. The aim of this study was to assess the long-term trend in chl-a in Lake Ladoga. The results showed a gradual reduction in chl-a concentration, indicating a moderate improvement. Chl-a concentrations (minimum-maximum values) varied spatially. The shallow southern shores did not show any improvement while the situation in the north is much better. The shore areas around the functioning paper mill at Pitkäranta and city of Sortavala still show high chl-a values. These findings provide a general reference on the current trophic state of Lake Ladoga that could contribute to improve policy and management strategies. It is assumed that the present warming trend of surface water may result in phytoplankton growth increase, thus partly offsetting a decrease in nutrient load. Precipitation is thought to be increasing, but the influence on water quality is less clear. Future studies could assess the current chemical composition to determine the state of water quality of Lake Ladoga.

Evaluation of forward reflectance models and empirical algorithms for chlorophyll concentration of stratified waters

For waters with stratified chlorophyll concentration (Chl), numerical simulations were carried out to gain insight into the forward models of subsurface reflectance and empirical algorithms for Chl from the ocean color. It is found that the Gordon and Clark (1980) forward model for reflectance using an equivalent homogeneous water with a weighted average Chl (⟨Chl⟩) as the input works well, but depending on the contribution of gelbstoff, the difference in reflectance between stratified and the equivalent homogeneous water can be more than 10%. Further, the attenuation of upward light is better approximated as ∼1.5times that of the diffuse attenuation coefficient of downwelling irradiance. On the other hand, although the forward model for reflectance developed in Zaneveld et al. [Opt. Express13, 9052 (2005)] using equivalent homogeneous water with a weighted average of the backscattering to absorption ratio as the input also works well, this model cannot be used to obtain equivalent ⟨Chl⟩ for reflectance. Further, for empirical Chl algorithms designed for "Case 1" waters, it has been discovered that, for surface Chl in a range of ∼0.06-22.0mg/m³, the predictability of surface Chl is basically the same as that of ⟨Chl⟩ from the blue-green band ratio or the band difference of reflectance. Because ⟨Chl⟩ is wavelength and weighting-formula dependent, and it is required to have profiles of both Chl and the optical properties, these results emphasize that for empirical Chl algorithms, it is easier, less ambiguous, and certainly more straightforward and simple to use surface Chl for algorithm development and then its evaluation, rather than to use ⟨Chl⟩, regardless of whether or not the water is stratified.

Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing

The novel coronavirus pandemic (COVID-19) has brought countries around the world to a standstill in the early part of 2020. Several nations and territories around the world insisted their population stay indoors for practicing social distance in order to avoid infecting the disease. Consequently, industrial activities, businesses, and all modes of traveling have halted. On the other hand, the pollution level decreased ‘temporarily’ in our living environment. As fewer pollutants are supplied in to the hydrosphere, and human recreational activities are stopped completely during the lockdown period, we hypothesize that the hydrological residence time (HRT) has increased in the semi-enclosed or closed lake bodies, which can in turn increase the primary productivity. To validate our hypothesis, and to understand the effect of lockdown on primary productivity in aquatic systems, we quantitatively estimated the chlorophyll-a (Chl-a) concentrations in different lake bodies using established Chl-a retrieval algorithm. The Chl-a monitored using Landsat-8 and Sentinel-2 sensor in the lake bodies of Wuhan, China, showed an elevated concentration of Chl-a. In contrast, no significant changes in Chl-a are observed for Vembanad Lake in India. Further analysis of different geo-environments is necessary to validate the hypothesis.

Catch per Unit Effort Dynamic of Yellowfin Tuna Related to Sea Surface Temperature and Chlorophyll in Southern Indonesia

Tuna fisheries are the most valuable fisheries in the world, with an estimated market value of at least US$42 billion in 2018. Indonesia plays an important role in the global tuna fisheries and has committed to improve its fisheries management; therefore, a pilot of long-term spatial-temporal data bases was developed in 2012, however none have utilized data to have better understanding for management improvement. In this study, the annual and seasonal variation of large (≥10 kg) Yellowfin Tuna (YFT) catch per unit effort (CPUE) have been investigated and the influence of sea surface temperature (SST) and chlorophyll-a on these variables examined. We used fish landing data from West Nusa Tenggara recorded every month between 2012 and 2017 and analyzed using generalized linear models and generalized additive models. We found a seasonal and annual pattern of tuna abundance affected by SST and chlorophyll-a (chl a) and related to upwelling and El Nino event. These results also suggest that a two-month closure to fishing in August and September in southern Lombok is worth considering by the Government to maximize conservation of stocks due to a high abundance of juveniles emerging during the upwelling months from June to August.

Tropical Cyclone Landfall Frequency and Large-Scale Environmental Impacts along Karstic Coastal Regions (Yucatan Peninsula, Mexico)

Tropical cyclones (TCs) are natural systems that develop over ocean basins and are key components of the atmospheric activity during the warm season. However, there are still knowledge gaps about the combined positive and negative TC impacts on the structure and function of coastal socio-ecosystems. Using remote sensing tools, we analyzed the frequency, trajectory, and intensity of 1894 TCs from 1851–2019 to identify vulnerable “hotspots” across the Yucatan Peninsula (YP), Mexico. A total of 151 events hit the YP, with 96% of landings on the eastern coast. We focused on three major hurricanes (Emily and Wilma, 2005; Dean, 2007) and one tropical storm (Stan, 2005) to determine the impacts on cumulative precipitation, vegetation change, and coastal phytoplankton (Chl-a) distribution across the YP. Despite a short inland incursion, Wilma’s environmental damage was coupled to strong winds (157–241 km/h), slow motion (4–9 km/h), and heavy precipitation (up to 770 mm). Because of an extensive footprint, Wilma caused more vegetation damage (29%) than Dean (20%), Emily (7%), and Stan (2%). All TCs caused a Chl-a increase associated to submarine discharge and upwelling off the peninsula coastlines. Disaster risk along the coast underscores negative economic impacts and positive ecological benefits at the regional scale.

Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances

Current methods to retrieve optically relevant properties from ocean color observations do not explicitly make use of prior knowledge about property distributions. Here we implement a simplified Bayesian approach that takes into account prior probability distributions on two sets of five optically relevant parameters, and conduct a retrieval of these parameters using hyperspectral simulated water-leaving reflectances. We focus specifically on the ability of the model to distinguish between two optically similar phytoplankton taxa, diatoms and Noctiluca scintillans. The inversion retrieval gives most-likely concentrations and uncertainty estimates, and we find that the model is able to probabilistically predict the occurrence of Noctiluca scintillans blooms using these metrics. We discuss how this method can be expanded to include a priori covariances between different parameters, and show the effect of varying measurement uncertainty and spectral resolution on Noctiluca scintillans bloom predictions.

Subtle bacterioplankton community responses to elevated CO2 and warming in the oligotrophic South Pacific gyre

Bacterioplankton play a critical role in primary production, carbon cycling, and nutrient cycling in the oligotrophic ocean. To investigate the effect of elevated CO₂ and warming on the composition and function of bacterioplankton communities in oligotrophic waters, we performed two trace-metal clean deck board incubation experiments during the New Zealand GEOTRACES transect of the South Pacific gyre (SPG). High-throughput amplicon sequencing of the 16S rRNA gene revealed that bacterioplankton community composition was distinct between the fringe and ultra-oligotrophic centre of the SPG and changed consistently in response to elevated CO₂ at the ultra-oligotrophic centre but not at the mesotrophic fringe of the SPG. The combined effects of elevated CO₂ and warming resulted in a high degree of heterogeneity between replicate communities. Community-level protein synthesis rates (³ H-Leucine incorporation) and bacterioplankton abundance were not affected by elevated CO₂ alone or in combination with warming at the fringe or ultra-oligotrophic centre of the SPG. These data suggest bacterioplankton community responses to elevated CO₂ may be modulated by nutrient regimes in open ocean ecosystems and highlight the need for further investigation in expanding oligotrophic subtropical gyres.

Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats

The vertical distribution of irradiance in the ocean is a key input to quantify processes spanning from radiative warming, photosynthesis to photo-oxidation. Here we use a novel dataset of thousands local-noon downwelling irradiance at 490 nm (Ed(490)) and photosynthetically available radiation (PAR) profiles captured by 103 BGC-Argo floats spanning three years (from October 2012 to January 2016) in the world’s ocean, to evaluate several published algorithms and satellite products related to diffuse attenuation coefficient (Kd). Our results show: (1) MODIS-Aqua Kd(490) products derived from a blue-to-green algorithm and two semi-analytical algorithms show good consistency with the float-observed values, but the Chla-based one has overestimation in oligotrophic waters; (2) The Kd(PAR) model based on the Inherent Optical Properties (IOPs) performs well not only at sea-surface but also at depth, except for the oligotrophic waters where Kd(PAR) is underestimated below two penetration depth (2zpd), due to the model’s assumption of a homogeneous distribution of IOPs in the water column which is not true in most oligotrophic waters with deep chlorophyll-a maxima; (3) In addition, published algorithms for the 1% euphotic-layer depth and the depth of 0.415 mol photons m−2 d−1 isolume are evaluated. Algorithms based on Chla generally work well while IOPs-based ones exhibit an overestimation issue in stratified and oligotrophic waters, due to the underestimation of Kd(PAR) at depth.

Satellite-measured water properties in high altitude Lake Tahoe

It has been difficult in satellite remote sensing to derive accurate water optical, biological, and biogeochemical products over high-altitude inland waters due to issues in satellite data processing (i.e., atmospheric correction). In this study, we demonstrate that accurate normalized water-leaving radiance spectra nL_w(λ) can be derived for a high-altitude lake, Lake Tahoe, using improved Rayleigh radiance computations (Wang, M., Opt. Express, 24, 12414-12429, 2016) which accurately account for water surface altitude effects in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to evaluate and validate satellite-derived nL_w(λ) spectra, and to quantitatively characterize water properties in the lake. Results show that VIIRS-derived nL_w(λ) spectra are quite comparable with those from the in situ measurements. Subsequent retrievals of water biological and biogeochemical products show that chlorophyll-a (Chl-a) concentration and Secchi depth (SD) are reasonably well-estimated, and captured normal seasonal variations in the lake, e.g., the annual highest Chl-a and SD normally occur in the winter while the lowest occur in the summer, which is consistent with in situ measurements. Interannual variability of these water quality parameters is also observed. In particular, Lake Tahoe experienced a significant environmental anomaly associated with an extreme weather condition event in 2017, showing considerably decreased nL_w(λ) at the spectral bands of 410, 443, and 486 nm, and significantly reduced SD values in the entire lake. The low SD measurements from VIIRS are consistent with in situ observations. Following the event in the 2017-2018 winter, Lake Tahoe recovered and returned to its typical conditions in spring 2018.

Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions

The first geostationary ocean color satellite mission (geostationary ocean color imager, or GOCI) has provided eight hourly observations per day over the western Pacific region since June 2010. GOCI imagery has been widely used to track the short-term dynamics of coastal and inland waters. Few studies have been performed to comprehensively assess the advantages of GOCI images in obtaining valid observations and estimating diurnal changes within the water column. Using the entire mission dataset between 2011 and 2017, these knowledge gaps were filled by comparing the daily percentages of valid observations (DPVOs) between GOCI and MODIS Aqua (MODISA) and by examining the diurnal changes in Chl-a over the East China Sea. The mean DPVOs of GOCI was 152.6% over the clear open ocean, suggesting that a daily valid coverage could be expected with GOCI. The GOCI DPVOs were ~26 times greater than the MODISA DPVOs; this pronounced difference was caused by the combined effects of their different observational frequencies and the more conservative quality flag system for MODISA. Diurnal changes in the GOCI-derived Chl-a were also found, with generally higher Chl-a in the afternoon than the morning and pronounced heterogeneities in the temporal and spatial domains. However, whether such diurnal changes are due to the real dynamics of the oceanic waters or artifacts of the satellite retrievals remains to be determined. This study provides the first comprehensive quantification of the unparalleled advantages of geostationary ocean color missions over polar orbiters, and the results highlights the importance of geostationary ocean color missions in studying coastal and inland waters.

A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

Information content of absorption spectra and implications for ocean color inversion

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Evaluating semi-analytical algorithms for estimating inherent optical properties in the South China Sea

Using large amounts of bio-optical data collected in the South China Sea (SCS) from 2003 to 2016, this study checks the consistency between well-known semi-analytical algorithms (SAAs)-the quasi-analytical algorithm (QAA) and the default generalized inherent optical property (GIOP-DC)-in retrieving the non-water absorption coefficient (a_nw(λ)), phytoplankton absorption coefficient (a_ph(λ)) and particulate backscattering coefficient (b_bp(λ)) from remote-sensing reflectance (R_rs(λ)) data at 412, 443, 490, 531, and 555 nm. The samples from the SCS are further separated into oligotrophic and mesotrophic water types for the comparison of the SAAs. Several findings are made: First, the values of an_w(λ) derived from the two SAAs deliver similar performance, with R² values ranging from 0.74 to 0.85 and 0.74 to 0.87, implying absolute percent error differences (APDs) from 37.93% to 74.88% and from 32.32% to 71.75% for the QAA and GIOP-DC, respectively. The QAA shows a value of R² between 0.64 and 0.91 and APDs between 43.57% to 83.53%, while the GIOP-DC yields R² between 0.76 to 0.89 and APDs between 44.65% to 79.46% when estimating a_ph(λ). The values of b_bp(λ) derived from the QAA are closer to the in-situ b_bp(λ) values, as indicated by the low values of the normalized centered root-mean-square deviation and normalized standard deviation, which are close to one. Second, a regionally tuned estimation of a_ph(λ) is proposed and recommended for the SCS. This consistency check of inherent optical properties products from SAAs can serve as reference for algorithm selection for further applications, including primary production, carbon, and biogeochemical models of the SCS, and can provide guidance for improving a_ph(λ) estimation.

Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R² value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m³). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m³, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m³. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Water quality control and management in water resources are important for providing clean and safe water to the public. Due to their large area, collection, analysis, and management of a large amount of water quality data are essential. Water quality data are collected mainly by manual field sampling, and recently real-time sensor monitoring has been increasingly applied for efficient data collection. However, real-time sensor monitoring still relies on only a few parameters, such as water level, velocity, temperature, conductivity, dissolved oxygen (DO), and pH. Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and nitrogen (N) still need to be further developed and improved for field applications. The utilization of information and communications technology (ICT) with sensor technology shows great potential for the monitoring, transmission, and management of field water-quality data and thus for developing effective water quality management. This paper presents a review of the recent advances in ICT and field applicable sensor technology for monitoring water quality, mainly focusing on water resources, such as rivers and lakes, and discusses the challenges and future directions.

The Sensitivity of Multi-spectral Satellite Sensors to Benthic Habitat Change

Coral reef ecosystems are under stress due to human-driven climate change and coastal activities. Satellite-based monitoring approaches offer an alternative to traditional field sampling measurements for detecting coral reef composition changes, especially given the advantages in their broad spatial coverage and high temporal frequency. However, the effect of benthic composition changes on water-leaving reflectance remains underexplored. In this study, we examined benthic change detection abilities of four representative satellite sensors: Landsat-8, Sentinel-2, Planet Dove and SkySat. We measured the bottom reflectance of different benthic compositions (live coral, bleached coral, dead coral with algal cover, and sand) in the field and developed an analytical bottom-up radiative transfer model to simulate remote sensing reflectance at the water surface for different compositions at a variety of depths and in varying water clarity conditions. We found that green spectral wavelengths are best for monitoring benthic changes such as coral bleaching. Moreover, we quantified the advantages of high spatial resolution imaging for benthic change detection. Together, our results provide guidance as to the potential use of the latest generation of multi-spectral satellites for monitoring coral reef and other submerged coastal ecosystems.

Deriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors

A methodology is developed for deriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors that have slightly different sensor spectral characteristics. Specifically, the required coefficients for algorithm modifications are obtained using the hyperspectral in situ optical measurements from the Marine Optical Buoy (MOBY) in the water off Hawaii. It is demonstrated that using the proposed approach for modifying ocean biological and biogeochemical algorithms, satellite-derived ocean property data over the global open ocean are consistent from multiple satellite sensors, although their corresponding sensor-measured normalized water-leaving radiance spectra nLw(λ) are different. Therefore, the proposed approach allows satellite-derived ocean biological and biogeochemical products to be consistent and can therefore be routinely merged from various satellite ocean color sensors. The proposed approach can be applied to any satellite algorithms that use the input of sensor-measured nLw(λ) spectra.

Correcting non-photochemical quenching of Saildrone chlorophyll-a fluorescence for evaluation of satellite ocean color retrievals

In vivo chlorophyll fluorescence (ChlF) can serve as a reasonable estimator of in situ phytoplankton biomass with the benefits of efficiently and affordably extending the global chlorophyll (Chl) data set in time and space to remote oceanic regions where routine sampling by other vessels is uncommon. However, in vivo ChlF measurements require correction for known, spurious biases relative to other measures of Chl concentration, including satellite ocean color retrievals. Spurious biases affecting in vivo ChlF measurements include biofouling, colored dissolved organic matter (CDOM) fluorescence, calibration offsets, and non-photochemical quenching (NPQ). A more evenly distributed global sampling of in vivo ChlF would provide additional confidence in estimates of uncertainty for satellite ocean color retrievals. A Saildrone semi-autonomous, ocean-going, solar- and wind-powered surface drone recently measured a variety of ocean and atmospheric parameters, including ChlF, during a 60-day deployment in mid-2018 in the California Current region. Correcting the Saildrone ChlF data for known biases, including deriving an NPQ-correction, greatly improved the agreement between the drone measurements and satellite ocean color retrievals from MODIS-Aqua and VIIRS-SNPP, highlighting that once these considerations are made, Saildrone semi-autonomous surface vehicles are a valuable, emerging data source for ocean and ecosystem monitoring.

Long-term dynamics and drivers of coral and macroalgal cover on inshore reefs of the Great Barrier Reef Marine Park

Quantifying the role of biophysical and anthropogenic drivers of coral reef ecosystem processes can inform management strategies that aim to maintain or restore ecosystem structure and productivity. However, few studies have examined the combined effects of multiple drivers, partitioned their impacts, or established threshold values that may trigger shifts in benthic cover. Inshore fringing reefs of the Great Barrier Reef Marine Park (GBRMP) occur in high-sediment, high-nutrient environments and are under increasing pressure from multiple acute and chronic stressors. Despite world-leading management, including networks of no-take marine reserves, relative declines in hard coral cover of 40-50% have occurred in recent years, with localized but persistent shifts from coral to macroalgal dominance on some reefs. Here we use boosted regression tree analyses to test the relative importance of multiple biophysical drivers on coral and macroalgal cover using a long-term (12-18 yr) data set collected from reefs at four island groups. Coral and macroalgal cover were negatively correlated at all island groups, and particularly when macroalgal cover was above 20%. Although reefs at each island group had different disturbance-and-recovery histories, degree heating weeks (DHW) and routine wave exposure consistently emerged as common drivers of coral and macroalgal cover. In addition, different combinations of sea-surface temperature, nutrient and turbidity parameters, exposure to high turbidity (primary) floodwater, depth, grazing fish density, farming damselfish density, and management zoning variously contributed to changes in coral and macroalgal cover at each island group. Clear threshold values were apparent for multiple drivers including wave exposure, depth, and degree heating weeks for coral cover, and depth, degree heating weeks, chlorophyll a, and cyclone exposure for macroalgal cover, however, all threshold values were variable among island groups. Our findings demonstrate that inshore coral reef communities are typically structured by broadscale climatic perturbations, superimposed upon unique sets of local-scale drivers. Although rapidly escalating climate change impacts are the largest threat to coral reefs of the GBRMP and globally, our findings suggest that proactive management actions that effectively reduce chronic stressors at local scales should contribute to improved reef resistance and recovery potential following acute climatic disturbances.

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.

Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements

For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties

Role of Kuroshio Current in fish resource variability off southwest Japan

Western boundary currents in the subtropics play a pivotal role in transporting warm water from the tropics that contribute to development of highly diverse marine ecosystem in the coastal regions. As one of the western boundary currents in the North Pacific, the Kuroshio Current (hereafter the Kuroshio) exerts great influences on biological resource variability off southwest Japan, but few studies have examined physical processes that attribute the coastal fish resource variability to the basin-scale Kuroshio variability. Using the high-quality fish catch data and high-resolution ocean reanalysis results, this study identifies statistical links of interannual fish resource variability off Sukumo Bay, Shikoku island of Japan, to subsurface ocean temperature variability in the Kuroshio. The subsurface ocean temperature variability off the south of Sukumo Bay exhibits vertically coherent structure with sea-surface height variability, which originates from the westward-propagating oceanic Rossby waves generated through surface wind anomalies in the Northwest Pacific. Although potential sources of the atmospheric variability remain unclarified, the remotely-induced oceanic Rossby waves contribute to fish resource variability off Sukumo Bay. These findings have potential applications to other coastal regions along the western boundary currents in the subtropics where the westward-propagating oceanic Rossby waves may contribute to coastal ocean temperature variability.

On the Adequacy of Representing Water Reflectance by Semi-Analytical Models in Ocean Color Remote Sensing

Deterministic or statistical inversion schemes to retrieve ocean color from space often use a simplified water reflectance model that may introduce unrealistic constraints on the solution, a disadvantage compared with standard, two-step algorithms that make minimal assumptions about the water signal. In view of this, the semi-analytical models of Morel and Maritorena (2001), MM01, and Park and Ruddick (2005), PR05, used in the spectral matching POLYMER algorithm (Steinmetz et al., 2011), are examined in terms of their ability to restitute properly, i.e., with sufficient accuracy, water reflectance. The approach is to infer water reflectance at MODIS wavelengths, as in POLYMER, from theoretical simulations (using Hydrolight with fluorescence and Raman scattering) and, separately, from measurements (AERONET-OC network). A wide range of Case 1 and Case 2 waters, except extremely turbid waters, are included in the simulations and sampled in the measurements. The reflectance model parameters that give the best fit with the simulated data or the measurements are determined. The accuracy of the reconstructed water reflectance and its effect on the retrieval of inherent optical properties (IOPs) is quantified. The impact of cloud and aerosol transmittance, fixed to unity in the POLYMER scheme, on model performance is also evaluated. Agreement is generally good between model results and Hydrolight simulations or AERONET-OC values, even in optically complex waters, with discrepancies much smaller than typical atmospheric correction errors. Significant differences exist in some cases, but having a more intricate model (i.e., using more parameters) makes convergence more difficult. The trade-off is between efficiency/robustness and accuracy. Notable errors are obtained when using the model estimates to retrieve IOPs. Importantly, the model parameters that best fit the input data, in particular chlorophyll-a concentration, do not represent adequately actual values. The reconstructed water reflectance should be used in bio-optical algorithms. While neglecting cloud and aerosol transmittances degrades the accuracy of the reconstructed water reflectance and the retrieved IOPs, it negligibly affects water reflectance ratios and, therefore, any variable derived from such ratios.

Analysis of the Optimal Wavelength for Oceanographic Lidar at the Global Scale Based on the Inherent Optical Properties of Water

Understanding the optimal wavelength for detecting the water column profile from a light detection and ranging (lidar) system is important in the design of oceanographic lidar systems. In this research, the optimal wavelength for detecting the water column profile using a lidar system at the global scale was analyzed based on the inherent optical properties of water. In addition, assuming that the lidar system had a premium detection characteristic in its hardware design, the maximum detectable depth using the established optimal wavelength was analyzed and compared with the mixed layer depth measured by Argo data at the global scale. The conclusions drawn are as follows: first, the optimal wavelengths for the lidar system are between the blue and green bands. For the open ocean, the optimal wavelengths are between 420 and 510 nm, and for coastal waters, the optimal wavelengths are between 520 and 580 nm. To obtain the best detection ability using a lidar system, the best configuration is to use a lidar system with multiple bands. In addition, a 490 nm wavelength is recommended when an oceanographic lidar system is used at the global scale with a single wavelength. Second, for the recommended 490 nm band, a lidar system with the 4 attenuating length detection ability can penetrate the mixed layer for 80% of global waters.

Impacts of Physical and Biological Processes on Spatial and Temporal Variability of Particulate Organic Carbon in the North Pacific Ocean during 2003-2017

The North Pacific Ocean is a significant carbon sink region, but little is known about the dynamics of particulate organic carbon (POC) and the influences of physical and biological processes in this region at the basin scale. Here, we analysed high-resolution surface POC data derived from MODIS-Aqua during 2003-2017, together with satellite-derived sea surface chlorophyll and temperature (SST). There are large spatial and temporal variations in surface POC in the North Pacific. Surface POC is much lower in the subtropical region (<50 mg m-3) than in the subarctic region (>100 mg m-3), primarily resulting from the south-to-north variability in biological production. Our analyses show significant seasonal and interannual variability in surface POC. In particular, there is one peak in winter-spring in the western subtropical region and two peaks in late spring and fall in the western subarctic region. Surface POC is positively correlated with chlorophyll (r = ~1) and negatively correlated with SST (r = ~-0.45, P < 0.001) south of 45°N, indicating the strong influence of physically driven biological activity on the temporal variability of POC in the subtropical region. There is a significantly positive but relatively lower correlation coefficient (0.6-0.8) between POC and chlorophyll and an overall non-significantly positive correlation between POC and SST north of 45°N, reflecting the reduction in the POC standing stock due to the fast sinking of large particles. The climate modes of the Pacific Decadal Oscillation, El Niño-Southern Oscillation and North Pacific Gyre Oscillation have large impacts on POC in various seasons in the subtropical region and weak influences in the subarctic region. Surface POC was anomalously high after 2013 (increased by ~15%) across the basin, which might be the result of complex interactions of physical and biological processes associated with an anomalous warming event (the Blob).

Influence of season-depending ecological variables on biomarker baseline levels in mussels (Mytilus trossulus) from two Baltic Sea subregions

For reliable mussel monitoring programmes based on biomarkers, regionally relevant reference values and their natural variability need to be known. The Baltic Sea exhibits high inter-regional and seasonal variability in physical factors such as salinity, temperature and primary production. The aim of this pilot study is to depict the effects of season-related environmental factors in a selected battery of biomarkers in two environmentally different subregions of the Baltic Sea to help establishing reference data for biochemical, cellular and tissue-level biomarkers. In order to achieve that, mussels were collected from reference sites in Kiel (Germany) and Tvärminne (Finland) during three seasons: summer and autumn 2016, and spring 2017. Finally, in order to characterize the ecological situation, analysis of the chemical tissue burden was performed and chlorophyll‑a and particulate organic carbon concentration and temperature changes were analyzed at each sampling locality using satellite remote sensing images. An integrated biomarker response index was performed to summarize the biomarker responses of each locality and season. The biochemical endpoints showed seasonal variability regulated by temperature, food supply and reproductive cycle, while among the cellular endpoints only lipofuscin accumulation and lysosomal structural changes showed slight seasonal variation. Seasonal changes in tissue level biomarkers were observed only at the northern Baltic Sea site Tvärminne, dictated by the demanding energetic trade-off caused by reproduction. In conclusion, the characterization of the ecological variables and physico-chemical conditions at each site, is crucial to perform a reliable assessment of the effects of a hypothetical pollution scenario in the Baltic Sea. Moreover, reference levels of biomarkers and their responses to natural environmental conditions must be established.

Phytoplankton decline in the eastern North Pacific transition zone associated with atmospheric blocking

Global climate change can significantly influence oceanic phytoplankton dynamics, and thus biogeochemical cycles and marine food webs. However, associative explanations based on the correlation between chlorophyll-a concentration (Chl-a) and climatic indices is inadequate to describe the mechanism of the connection between climate change, large-scale atmospheric dynamics, and phytoplankton variability. Here, by analyzing multiple satellite observations of Chl-a and atmospheric conditions from National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis datasets, we show that high-latitude atmospheric blocking events over Alaska are the primary drivers of the recent decline of Chl-a in the eastern North Pacific transition zone. These blocking events were associated with the persistence of large-scale atmosphere pressure fields that decreased westerly winds and southward Ekman transport over the subarctic ocean gyre. Reduced southward Ekman transport leads to reductions in nutrient availability to phytoplankton in the transition zone. The findings describe a previously unidentified climatic factor that contributed to the recent decline of phytoplankton in this region and propose a mechanism of the top-down teleconnection between the high-latitude atmospheric circulation anomalies and the subtropical oceanic primary productivity. The results also highlight the importance of understanding teleconnection among atmosphere-ocean interactions as a means to anticipate future climate change impacts on oceanic primary production.

A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

In this study, a low-altitude remote sensing (LARS) observation system was employed to observe a rapidly changing coastal environment-owed to the regular opening of the sluice gate of the Saemangeum seawall-off the west coast of South Korea. The LARS system uses an unmanned aerial vehicle (UAV), a multispectral camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) module to acquire geometry information. The UAV system can observe the coastal sea surface in two dimensions with high temporal (1 s−1) and spatial (20 cm) resolutions, which can compensate for the coarse spatial resolution of in-situ measurements and the low temporal resolution of satellite observations. Sky radiance, sea surface radiance, and irradiance were obtained using a multispectral camera attached to the LARS system, and the remote sensing reflectance (Rrs) was accordingly calculated. In addition, the hyperspectral radiometer and in-situ chlorophyll-a concentration (CHL) measurements were obtained from a research vessel to validate the Rrs observed using the multispectral camera. Multi-linear regression (MLR) was then applied to derive the relationship between Rrs of each wavelength observed using the multispectral sensor on the UAV and the in-situ CHL. As a result of applying MLR, the correlation and root mean square error (RMSE) between the remotely sensed and in-situ CHLs were 0.94 and ~0.8 μg L−1, respectively; these results show a higher correlation coefficient and lower RMSE than those of other, previous studies. The newly derived algorithm for the CHL estimation enables us to survey 2D CHL images at high temporal and spatial resolutions in extremely turbid coastal oceans.

An Integrated Web-Based System for the Monitoring and Forecasting of Coastal Harmful Algae Blooms: Application to Shenzhen City, China

Harmful algal blooms (HABs) cause environmental problems worldwide. Continuous monitoring and forecasting of harmful algal blooms are necessary for marine resources managers to detect the intensity and spatial extent of HABs and provide early warnings to the public. In this study, we introduce an integrated web-based system for the monitoring and forecasting of coastal HABs. The system is named the Harmful Algal Blooms Monitoring and Forecasting System (HMFS). HMFS integrates in situ observations, a remote-sensing-based model, hydrodynamic and water quality model and Web-Based Geographic Information System (GIS) techniques into one environment. The in situ sensors and remote sensing model provide automatic and continuous monitoring of the coastal water conditions. The numerical models provide short-term prediction and early warning of HAB of up to 5 days. The overall forecast accuracy is more than or equal to 50% for the major coastal areas of Shenzhen in 2018. By leveraging a web-based GIS technique and Service-Oriented Architecture (SOA), the web portal of HMFS provides a graphic interface for users and mangers to view real-time in situ measurements and remote sensing maps, explore numerical model forecasts and get early warning information. HMFS was applied to Shenzhen, which is a rising megacity in Southern China. The application study demonstrated the applicability and effectiveness of HMFS for monitoring and predicting HABs.

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.