A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Bayesian approach to a generalized inherent optical property model

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

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

Climate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard's index: 0.87 ± 0.07; Dice index: 0.93 ± 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Niño events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.

Seasonal bias in global ocean color observations

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (Rrs(λ);sr-1) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs(λ), and we recommend more simulations to discern these influence Rrs(λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.

Particulate Backscattering in the Global Ocean: A Comparison of Independent Assessments

How well do we know the particulate backscattering coefficient (b_bp) in the global ocean? Satellite lidar b_bp has never been validated globally and few studies have compared lidar b_bp to b_bp derived from reflectances (via ocean color) or in situ observations. Here, we validate lidar b_bp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color b_bp at the same locations and times to assess performance. Lidar b_bp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ±50%.

Drivers of spectral optical scattering by particles in the upper 500 m of the Atlantic Ocean

Optical models have been proposed to relate spectral variations in the beam attenuation (c_p) and optical backscattering (b_bp) coefficients to marine particle size distributions (PSDs). However, due to limited PSD data, particularly in the open ocean, optically derived PSDs suffer from large uncertainties and we have a poor empirical understanding of the drivers of spectral c_p and b_bp coefficients. Here we evaluated PSD optical proxies and investigated their drivers by analyzing an unprecedented dataset of co-located PSDs, phytoplankton abundances and optical measurements collected across the upper 500 m of the Atlantic Ocean. The spectral slope of c_p was correlated (r>0.59) with the slope of the PSD only for particles with diameters >1 µm and also with eukaryotic phytoplankton concentrations. No significant relationships between PSDs and the spectral slope of b_bp were observed. In the upper 200 m, the b_bp spectral slope was correlated to the light absorption by particles (a_p; r<-0.54) and to the ratio of cyanobacteria to eukaryotic phytoplankton. This latter correlation was likely the consequence of the strong relationship we observed between a_p and the concentration of eukaryotic phytoplankton (r=0.83).

Particle Size Parameters of Particulate Matter Suspended in Coastal Waters and Their Use as Indicators of Typhoon Influence

The power law particle size distribution (PSD) slope parameter is commonly used to characterize sediment fluxes, resuspension, aggregates, and settling rates in coastal and estuarine waters. However, particle size distribution metrics are also very useful for understanding sediment source and dynamic processes. In this study, a method was proposed to employ the particle size parameters commonly used in sedimentary geology (average particle size (ø), sorting, skewness, and kurtosis) as indicators of changes in sediment dynamic processes, and MODIS images were used to estimate these parameters. The particle size parameters were estimated using a Mie scattering model, Quasi-Analytical Algorithm (QAA) analysis algorithm, and least squares QR decomposition (LSQR) solution method based on the relationship between the power law distribution of the suspended particles and their optical scattering properties. The estimates were verified by field measurements in the Yellow Sea and Bohai Sea regions of China. This method provided good estimates of the average particle size (ø), sorting, and kurtosis. A greater number of wavebands (39) was associated with more accurate particle size distribution curves. Furthermore, the method was used to monitor changes in suspended particulate matter in the vicinity of the Heini Bay of China before and after the passage of a strong storm in August 2011. The particle size parameters represented the influence of a strong typhoon on the distribution of the near-shore sediment and, together with the PSD slope, comprehensively reflected the changes in the near-shore suspended particulate matter. This method not only established the relationship between remote sensing monitoring and the historical sediment record, it also extends the power law model to the application of sediment source and dynamic processes in coastal waters.

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.

Variability of relationship between the volume scattering function at 180° and the backscattering coefficient for aquatic particles

Properly interpreting lidar (light detection and ranging) signal for characterizing particle distribution relies on a key parameter, χ_p(π), which relates the particulate volume scattering function (VSF) at 180° (β_p(π)) that a lidar measures to the particulate backscattering coefficient (b_bp). However, χ_p(π) has been seldom studied due to challenges in accurately measuring β_p(π) and b_bp concurrently in the field. In this study, χ_p(π), as well as its spectral dependence, was re-examined using the VSFs measured in situ at high angular resolution in a wide range of waters. β_p(π), while not measured directly, was inferred using a physically sound, well-validated VSF-inversion method. The effects of particle shape and internal structure on the inversion were tested using three inversion kernels consisting of phase functions computed for particles that are assumed as homogenous sphere, homogenous asymmetric hexahedra, or coated sphere. The reconstructed VSFs using any of the three kernels agreed well with the measured VSFs with a mean percentage difference <5% at scattering angles <170^∘. At angles immediately near or equal to 180°, the reconstructed β_p(π) depends strongly on the inversion kernel. χ_p(π) derived with the sphere kernels was smaller than those derived with the hexahedra kernel but consistent with χ_p(π) estimated directly from high-spectral-resolution lidar and in situ backscattering sensor. The possible explanation was that the sphere kernels are able to capture the backscattering enhancement feature near 180° that has been observed for marine particles. χ_p(π) derived using the coated sphere kernel was generally lower than those derived with the homogenous sphere kernel. Our result suggests that χ_p(π) is sensitive to the shape and internal structure of particles and significant error could be induced if a fixed value of χ_p(π) is to be used to interpret lidar signal collected in different waters. On the other hand, χ_p(π) showed little spectral dependence.

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).

Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean

An extensive data set of measurements within the Chukchi and Beaufort Seas is used to characterize the optical properties of seawater associated with different phytoplankton communities. Hierarchical cluster analysis of diagnostic pigment concentrations partitioned stations into four distinct surface phytoplankton communities based on taxonomic composition and average cell size. Concurrent optical measurements of spectral absorption and backscattering coefficients and remote-sensing reflectance were used to characterize the magnitudes and spectral shapes of seawater optical properties associated with each phytoplankton assemblage. The results demonstrate measurable differences among communities in the average spectral shapes of the phytoplankton absorption coefficient. Similar or smaller differences were also observed in the spectral shapes of nonphytoplankton absorption coefficients and the particulate backscattering coefficient. Phytoplankton on average, however, contributed only 25% or less to the total absorption coefficient of seawater. Our analyses indicate that the interplay between the magnitudes and relative contributions of all optically significant constituents generally dampens any influence of varying phytoplankton absorption spectral shapes on the total absorption coefficient, yet there is still a marked discrimination observed in the spectral shape of the ratio of the total backscattering to total absorption coefficient and remote-sensing reflectance among the phytoplankton assemblages. These spectral variations arise mainly from differences in the bio-optical environment in which specific communities were found, as opposed to differences in the spectral shapes of phytoplankton optical properties per se. These results suggest potential approaches for the development of algorithms to assess phytoplankton community composition from measurements of seawater optical properties in western Arctic waters.

Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data

The Secchi disk depth (ZSD, m) has been used globally for many decades to represent water clarity and an index of water quality and eutrophication. In recent studies, a new theory and model were developed for ZSD, which enabled its semi-analytical remote sensing from the measurement of water color. Although excellent performance was reported for measurements in both oceanic and coastal waters, its reliability for highly turbid inland waters is still unknown. In this study, we extend this model and its evaluation to such environments. In particular, because the accuracy of the inherent optical properties (IOPs) derived from remote sensing reflectance (Rrs, sr−1) plays a key role in determining the reliability of estimated ZSD, we first evaluated a few quasi-analytical algorithms (QAA) specifically tuned for turbid inland waters and determined the one (QAATI) that performed the best in such environments. For the absorption coefficient at 443 nm (a(443), m−1) ranging from ~0.2 to 12.5 m−1, it is found that the QAATI-derived absorption coefficients agree well with field measurements (r2 > 0.85, and mean absolute percentage difference (MAPD) smaller than ~39%). Furthermore, with QAATI-derived IOPs, the MAPD was less than 25% between the estimated and field-measured ZSD (r2 > 0.67, ZSD in a range of 0.1–1.7 m). Furthermore, using matchup data between Rrs from the Medium Resolution Imaging Spectrometer (MERIS) and in-situ ZSD, a similar performance in the estimation of ZSD from remote sensing was obtained (r2 = 0.73, MAPD = 37%, ZSD in a range of 0.1–0.9 m). Based on such performances, we are confident to apply the ZSD remote sensing scheme to MERIS measurements to characterize the spatial and temporal variations of ZSD in Lake Taihu during the period of 2003–2011.

Assessing the Impact of a Two-Layered Spherical Geometry of Phytoplankton Cells on the Bulk Backscattering Ratio of Marine Particulate Matter

The bulk backscattering ratio ( b b p ˜ ) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of modeled structural heterogeneity of phytoplankton cells on b b p ˜ . b b p ˜ is modeled considering viruses, heterotrophic bacteria, phytoplankton, organic detritus, and minerals. Three case studies are defined according to the relative abundance of the components. Two case studies represent typical situations in open ocean, oligotrophic waters, and phytoplankton bloom. The third case study is typical of coastal waters with the presence of minerals. Phytoplankton cells are modeled by a two-layered spherical geometry representing a chloroplast surrounding the cytoplasm. The b b p ˜ values are higher when structural heterogeneity is considered because the contribution of coated spheres to light backscattering is higher than homogeneous spheres. The impact of heterogeneity is; however, strongly conditioned by the hyperbolic slope ξ of the particle size distribution. Even if the relative abundance of phytoplankton is small (<1%), b b p ˜ increases by about 58% (for ξ = 4 and for oligotrophic waters), when the heterogeneity is taken into account, in comparison with a particulate population composed only of homogeneous spheres. As expected, heterogeneity has a much smaller impact (about 12% for ξ = 4 ) on b b p ˜ in the presence of suspended minerals, whose increased light scattering overwhelms that of phytoplankton.

The HydroColor App: Above Water Measurements of Remote Sensing Reflectance and Turbidity Using a Smartphone Camera

HydroColor is a mobile application that utilizes a smartphone’s camera and auxiliary sensors to measure the remote sensing reflectance of natural water bodies. HydroColor uses the smartphone’s digital camera as a three-band radiometer. Users are directed by the application to collect a series of three images. These images are used to calculate the remote sensing reflectance in the red, green, and blue broad wavelength bands. As with satellite measurements, the reflectance can be inverted to estimate the concentration of absorbing and scattering substances in the water, which are predominately composed of suspended sediment, chlorophyll, and dissolved organic matter. This publication describes the measurement method and investigates the precision of HydroColor’s reflectance and turbidity estimates compared to commercial instruments. It is shown that HydroColor can measure the remote sensing reflectance to within 26% of a precision radiometer and turbidity within 24% of a portable turbidimeter. HydroColor distinguishes itself from other water quality camera methods in that its operation is based on radiometric measurements instead of image color. HydroColor is one of the few mobile applications to use a smartphone as a completely objective sensor, as opposed to subjective user observations or color matching using the human eye. This makes HydroColor a powerful tool for crowdsourcing of aquatic optical data.

Evidence for ubiquitous preferential particle orientation in representative oceanic shear flows

In situ measurements were undertaken to characterize particle fields in undisturbed oceanic environments. Simultaneous, co-located depth profiles of particle fields and flow characteristics were recorded using a submersible holographic imaging system and an acoustic Doppler velocimeter, under different flow conditions and varying particle concentration loads, typical of those found in coastal oceans and lakes. Nearly one million particles with major axis lengths ranging from ∼14 μm to 11.6 mm, representing diverse shapes, sizes, and aspect ratios were characterized as part of this study. The particle field consisted of marine snow, detrital matter, and phytoplankton, including colonial diatoms, which sometimes formed "thin layers" of high particle abundance. Clear evidence of preferential alignment of particles was seen at all sampling stations, where the orientation probability density function (PDF) peaked at near horizontal angles and coincided with regions of low velocity shear and weak turbulent dissipation rates. Furthermore, PDF values increased with increasing particle aspect ratios, in excellent agreement with models of spheroidal particle motion in simple shear flows. To the best of our knowledge, although preferential particle orientation in the ocean has been reported in two prior cases, our findings represent the first comprehensive field study examining this phenomenon. Evidence of nonrandom particle alignment in aquatic systems has significant consequences to aquatic optics theory and remote sensing, where perfectly random particle orientation and thus isotropic symmetry in optical parameters is assumed. Ecologically, chain-forming phytoplankton may have evolved to form large aspect ratio chains as a strategy to optimize light harvesting.

Simulating PACE Global Ocean Radiances

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P <0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and aCDOC (r = 0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250-800 nm. These unassimilated radiances were within -0.074 mW cm-2 μm1 sr-1 of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of -10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability.

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 (b_bp(λ)), particle biogeochemical variables and remote sensing reflectance (R_rs(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_Q_bbe(640)) and related optical effects. The results show that the P_Q_bbe(640) levels varied by nearly two orders for all of the samples examined. This high degree of P_Q_bbe(640) variability significantly affected b_bp(640) and the mass-specific backscattering coefficient (b_bp*(640)), showing that approximately 63.7% and 20.8% of the variability in the b_bp*(640) and b_bp(640) was attributed to the P_Q_bbe(640), respectively. More importantly, consistent with the observations of Wang et al. [J. Geophys. Res.: Oceans 121, 3955 (2016)], the P_Q_bbe(640) results clearly showed two clusters and this clustering changed the relationships between b_bp*(640), b_bp(640) and R_rs(640) with the biogeochemical variables. However, we confirm that P_Q_bbe(640) clustering generally remained intact across seasons. Therefore, a simple scheme based on a threshold of the P_Q_bbe(640) data is proposed for the classification of particle types. With this classification, impacts of P_Q_bbe(640) on b_bp*(640) and b_bp(640) were clearly reduced, and co-variation trends of b_bp*(640), b_bp(640) and R_rs(640) with biogeochemical variables can be in turn more accurately described. Overall, this study provides general information on P_Q_bbe(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.

Variability of particle size distribution with respect to inherent optical properties in Poyang Lake, China

Suspended particulate matter plays a significant role in the studies of sediment fluxes, phytoplankton dynamics, and water optical properties. This study focuses on the relationships between particle size distribution (PSD), water's inherent optical properties (IOPs), and water constituents. We investigated the complex waters of Poyang Lake, the largest freshwater lake in China, in wet and dry seasons during 2008-2011. Because of the distinct temporal-spatial variation of Poyang Lake, these parameters and relationships also demonstrate seasonal and regional variability. The variation range of the concentration of suspended particulate matter is 0.32-69.08  mg/l, with a mean value of 22.21  mg/l. The median particle size in the dry season is much larger than that of the wet season. The Junge distribution fits the PSD of Poyang Lake very well in the scope of 6.21-331 μm. Furthermore, the slopes of the PSD range from 3.54 to 4.69, with a mean value of 4.11, with the steepest slopes (>4.5) occurring in the waters around Songmen Mountain Island and the northern waterway. A negative correlation was found between median particle size (D_v50) and the mass-specific absorption coefficient at 443 nm [apm(443)] for both wet and dry seasons. Identical to analogous waters, the spectral slopes of the PSD correlate well with the spectral slopes of the attenuation coefficient, but with different fitted formulas. In the dry season, the particle size can better explain the variability of the scattering coefficient, while the mass-specific scattering coefficient is better explained by the apparent density. However, no similar results were found for the wet season. In addition, the spectral slopes of the backscattering coefficient correlated well with the PSD slope, and the bulk refractive index calculated from the backscattering ratio and PSD slope can indicate the particle composition of Poyang Lake. Overall, the knowledge on the PSD and IOPs gained in this study broadens our understanding of water optics in highly turbid water columns.

Spectral attenuation and backscattering as indicators of average particle size

Measurements of the particulate beam attenuation coefficient at multiple wavelengths in the ocean typically exhibit a power law dependence on wavelength, and the slope of that power law has been related to the slope of the particle size distribution (PSD), when assumed to be a power law function of particle size. Recently, spectral backscattering coefficient measurements have been made using sensors deployed at moored observatories, on autonomous underwater vehicles, and even retrieved from space-based measurements of remote sensing reflectance. It has been suggested that these backscattering measurements may also be used to obtain information about the shape of the PSD. In this work, we directly compared field-measured PSD with multispectral beam attenuation and backscattering coefficients in a coastal bottom boundary later. The results of this comparison demonstrated that (1) the beam attenuation spectral slope correlates with the average particle size as suggested by theory for idealized particles and PSD; and (2) measurements of spectral backscattering also contain information reflective of the average particle size in spite of large deviations of the PSD from a spectral power law shape.

Particle size distribution of river-suspended sediments determined by in situ measured remote-sensing reflectance

Suspended sediments in water bodies are classified into organic and inorganic matter and have been investigated by remote-sensing technology for years. Focusing on inorganic matter, however, detailed information such as the grain size of this matter has not been provided yet. In this study, we present a new solution for estimating inorganic suspended sediments' size distribution in highly complex Case 2 waters by using a simple spectrometer sensor rather than a backscattering sensor. An experiment was carried out in the Pearl River Estuary (PRE) in the dry season to collect the remote-sensing reflectance (R_rs) and particle size distribution (PSD) of inorganic suspended sediments. Based on Mie theory, PSDs in the PRE waters were retrieved by R_rs, colored dissolved organic matter, and phytoplankton. The retrieved median diameters in 12 stations show good agreement with those of laboratory analysis at root mean square error of 2.604 μm (27.63%), bias of 1.924 μm (20.42%), and mean absolute error of 2.298 μm (24.37%). The retrieved PSDs and previous PSDs were compared, and the features of PSDs in the PRE waters were concluded.

Spatio-temporal analyses of Symbiodinium physiology of the coral Pocillopora verrucosa along large-scale nutrient and temperature gradients in the Red Sea

Algal symbionts (zooxanthellae, genus Symbiodinium) of scleractinian corals respond strongly to temperature, nutrient and light changes. These factors vary greatly along the north-south gradient in the Red Sea and include conditions, which are outside of those typically considered optimal for coral growth. Nevertheless, coral communities thrive throughout the Red Sea, suggesting that zooxanthellae have successfully acclimatized or adapted to the harsh conditions they experience particularly in the south (high temperatures and high nutrient supply). As such, the Red Sea is a region, which may help to better understand how zooxanthellae and their coral hosts successfully acclimatize or adapt to environmental change (e.g. increased temperatures and localized eutrophication). To gain further insight into the physiology of coral symbionts in the Red Sea, we examined the abundance of dominant Symbiodinium types associated with the coral Pocillopora verrucosa, and measured Symbiodinium physiological characteristics (i.e. photosynthetic processes, cell density, pigmentation, and protein composition) along the latitudinal gradient of the Red Sea in summer and winter. Despite the strong environmental gradients from north to south, our results demonstrate that Symbiodinium microadriaticum (type A1) was the predominant species in P. verrucosa along the latitudinal gradient. Furthermore, measured physiological characteristics were found to vary more with prevailing seasonal environmental conditions than with region-specific differences, although the measured environmental parameters displayed much higher spatial than temporal variability. We conclude that our findings might present the result of long-term acclimatization or adaptation of S. microadriaticum to regionally specific conditions within the Red Sea. Of additional note, high nutrients in the South correlated with high zooxanthellae density indicating a compensation for a temperature-driven loss of photosynthetic performance, which may prove promising for the resilience of these corals under increase of temperature increase and eutrophication.

Discrimination of phytoplankton functional groups using an ocean reflectance inversion model

Ocean reflectance inversion models (ORMs) provide a mechanism for inverting the color of the water observed by a satellite into marine inherent optical properties (IOPs), which can then be used to study phytoplankton community structure. Most ORMs effectively separate the total signal of the collective phytoplankton community from other water column constituents; however, few have been shown to effectively identify individual contributions by multiple phytoplankton groups over a large range of environmental conditions. We evaluated the ability of an ORM to discriminate between Noctiluca miliaris and diatoms under conditions typical of the northern Arabian Sea. We: (1) synthesized profiles of IOPs that represent bio-optical conditions for the Arabian Sea; (2) generated remote-sensing reflectances from these profiles using Hydrolight; and (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N. miliaris. By comparing the estimates from the inversion model with those from synthesized vertical profiles, we identified those conditions under which the ORM performs both well and poorly. Even under perfectly controlled conditions, the absolute accuracy of ORM retrievals degraded when further deconstructing the derived total phytoplankton signal into subcomponents. Although the absolute magnitudes maintained biases, the ORM successfully detected whether or not Noctiluca miliaris appeared in the simulated water column. This quantitatively calls for caution when interpreting the absolute magnitudes of the retrievals, but qualitatively suggests that the ORM provides a robust mechanism for identifying the presence or absence of species.

Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurements

The ability to estimate mean particle size using simple, low-power optical instruments promises to greatly expand coverage of particle size measurements in the ocean and advance understanding of myriad processes from sediment transport to biological carbon sequestration. Here we present a method for estimating the mean diameter of particles in suspension from high-resolution time series of simple optical measurements, such as beam attenuation or optical backscattering. Validation results from a laboratory clay aggregation experiment show a good fit with independent mean particle diameter estimates in the 10-80 μm diameter range, with relative biases of 17%-38% and relative root mean square errors of 10%-24%. In the 80-200 μm range, quantitative validation data were not available, but our mean diameter estimates correlated strongly with particle settling rates.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Particulate optical scattering coefficients along an Atlantic Meridional Transect

The particulate optical backscattering coefficient (bbp) is a fundamental optical property that allows monitoring of marine suspended particles both in situ and from space. Backscattering measurements in the open ocean are still scarce, however, especially in oligotrophic regions. Consequently, uncertainties remain in bbp parameterizations as well as in satellite estimates of bbp. In an effort to reduce these uncertainties, we present and analyze a dataset collected in surface waters during the 19th Atlantic Meridional Transect. Results show that the relationship between particulate beam-attenuation coefficient (cp) and chlorophyll-a concentration was consistent with published bio-optical models. In contrast, the particulate backscattering per unit of chlorophyll-a and per unit of cp were higher than in previous studies employing the same sampling methodology. These anomalies could be due to a bias smaller than the current uncertainties in bbp. If that was the case, then the AMT19 dataset would confirm that bbp:cp is remarkably constant over the surface open ocean. A second-order decoupling between bbp and cp was, however, evident in the spectral slopes of these coefficients, as well as during diel cycles. Overall, these results emphasize the current difficulties in obtaining accurate bbp measurements in the oligotrophic ocean and suggest that, to first order, bbp and cp are coupled in the surface open ocean, but they are also affected by other geographical and temporal variations.

Particle backscattering as a function of chlorophyll and phytoplankton size structure in the open-ocean

Using an extensive database of in situ observations we present a model that estimates the particle backscattering coefficient as a function of the total chlorophyll concentration in the open-ocean (Case-1 waters). The parameters of the model include a constant background component and the chlorophyll-specific backscattering coefficients associated with small (<20 μm) and large (>20 μm) phytoplankton. The new model performed with similar accuracy when compared with a traditional power-law function, with the additional benefit of providing information on the role of phytoplankton size. The observed spectral-dependency (γ) of model parameters was consistent with past observations, such that γ associated with the small phytoplankton population was higher than that of large phytoplankton. Furthermore, γ associated with the constant background component suggests this component is likely attributed to submicron particles. We envisage that the model would be useful for improving Case-1 ocean-colour models, assimilating light into multi-phytoplankton ecosystem models and improving estimates of phytoplankton size structure from remote sensing.

Determination of the volume scattering function of aqueous particle suspensions with a laboratory multi-angle light scattering instrument

We describe a methodology for determining the volume scattering function β(ψ) of aqueous particle suspensions from measurements with a laboratory multi-angle light scattering instrument called DAWN (Wyatt Technology Corporation). In addition to absolute and angular calibration, the key component of the method is the algorithm correcting for reflection errors that reduce the percent error in β(ψ) from as much as ~300% to <13% at backward scattering angles. The method is optimized and tested with simulations of three-dimensional radiative transfer of exact measurement geometry including the key components of the instrument and also validated experimentally using aqueous suspensions of polystyrene beads. Example applications of the method to samples of oceanic waters and comparisons of these measurements with results obtained with other light scattering instruments are presented.

Optical assessment of particle size and composition in the Santa Barbara Channel, California

The suspended particle assemblage in complex coastal waters is a mixture of living phytoplankton, other autochthonous matter, and materials of terrestrial origin. The characterization of suspended particles is important for understanding regional primary productivity and rates of carbon sequestration, the fate of anthropogenic materials released to the coastal environment, as well as its effects on bulk optical properties, which influence the passive optical remote sensing of the coastal ocean. Here, the extensive bio-optical Plumes and Blooms data set is used to characterize the surface particle assemblage in the Santa Barbara Channel, California, a highly productive, upwelling-dominated, coastal site affected by episodic sediment inputs. Available variables sensitive to characteristics of the particle assemblage include particle beam attenuation and backscattering coefficients, High Performance Liquid Chromatography (HPLC) pigment concentration observations, chlorophyll and particulate organic carbon concentration, particulate and phytoplankton absorption coefficients, and Laser In-situ Scattering and Transmissometry (LISST) 100-X particle sizer observations. Comparisons among these particle assemblage proxy variables indicate good agreement and internal consistency among the data set. Correlations among chlorophyll concentration, particulate organic carbon concentration (POC), HPLC pigments, and proxies sensitive to the entire particle assemblage such as backscattering and LISST data strongly indicate that in spite of its coastal character, variability in the particle assemblage in the Santa Barbara Channel is dominated by its marine biogenic component. Relatively high estimates of the bulk real index of refraction and its positive correlation with chlorophyll and lithogenic silica concentration tentatively indicate that there is minerogenic particle influence in the Santa Barbara Channel that tends to covary with the phytoplankton blooms. Limitations of each particle assemblage proxy and remote-sensing applications are discussed.

Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters

Past years have seen the development of different approaches to detect phytoplankton groups from space. One of these methods, the PHYSAT one, is empirically based on reflectance anomalies. Despite observations in good agreement with in situ measurements, the underlying theoretical explanation of the method is still missing and needed by the ocean color community as it prevents improvements of the methods and characterization of uncertainties on the inversed products. In this study, radiative transfer simulations are used in addition to in situ measurements to understand the organization of the signals used in PHYSAT. Sensitivity analyses are performed to assess the impact of the variability of the following three parameters on the reflectance anomalies: specific phytoplankton absorption, colored dissolved organic matter absorption, and particles backscattering. While the later parameter explains the largest part of the anomalies variability, results show that each group is generally associated with a specific bio-optical environment which should be considered to improve methods of phytoplankton groups detection.

Plankton and particle size and packaging: from determining optical properties to driving the biological pump

Understanding pelagic ecology and quantifying energy fluxes through the trophic web and from the surface to the deep ocean requires the ability to detect and identify all organisms and particles in situ and in a synoptic manner. An idealized sensor should observe both the very small living or dead particles such as picoplankton and detritus, respectively, and the large particles such as aggregates and meso- to macroplankton. Such an instrument would reveal an astonishing amount and diversity of living and nonliving particles present in a parcel of water. Unfortunately such sensors do not exist. However, complex interactions constrain the space, temporal, and size distributions of these objects in such ways that general rules can be inferred from the measurement of their optical properties. Recent technological developments allow for the in situ measurement of the optical properties and size distributions of particles and plankton in a way such that synoptic surveys are possible. This review deals with particle and plankton size distributions (PSDs) as well as how particles' geometry and nature affect their optical properties. Finally, we propose the integration of the PSD into size-structured mathematical models of biogeochemical fluxes.

Inherent optical properties and satellite retrieval of chlorophyll concentration in the lagoon and open ocean waters of New Caledonia

The retrieval of chlorophyll-a concentration from remote sensing reflectance (Rrs) data was tested with the NASA OC4v4 algorithm on the inner New Caledonian lagoon (Case 2) and adjacent open ocean (Case 1) waters. The input to OC4v4 was Rrs measured in situ or modeled from water's inherent optical properties (2001-2007). At open ocean stations, backscattering and absorption coefficients were correlated with chlorophyll (R(2)=0.31-0.51, respectively), in agreement with models for Case 1 waters. Taking spectrofluorometric measurement as reference, the OC4v4 model leads to an average underestimation of 33% of the chlorophyll concentration. For the lagoon waters, OC4v4 performed inadequately because the backscattering coefficient, highly correlated with turbidity and suspended matter (R(2)=0.98), was poorly correlated to chlorophyll (R(2)=0.42). The OC4v4 performance was better in deep lagoon waters for stations with a TDT index (Tchla x depth/turbidity) higher than 19 mg m(-2) NTU(-1) (R(2)=0.974, bias=10.2%). Global Imager Rrs provided a good estimate of Tchla (R(2)=0.79, N=28) in the deeper part of the lagoon.

Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments

We present an extensive data set of particle attenuation (c(p)), backscattering (b(bp)), and chlorophyll concentration (Chl) from a diverse set of open ocean environments. A consistent observation in the data set is the strong coherence between c(p) and b(bp) and the resulting constancy of the backscattering ratio (0.010 +/- 0.002). The strong covariability between c(p) and b(bp) must be rooted in one or both of two explanations, 1) the size distribution of particles in the ocean is remarkably conserved and particle types responsible for c(p) and b(bp) covary, 2) the same particle types exert influence on both quantities. Therefore, existing relationships between c(p) or Chl:c(p) and phytoplankton biomass and physiological indices can be conceptually extended to the use of b(bp). This finding lends support to use of satellite-derived Chl and b(bp) for investigation of phytoplankton biomass and physiology and broadens the applications of existing ocean color retrievals.

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Light scattering properties and their relation to the biogeochemical composition of turbid productive waters: a case study of Lake Taihu

Light scattering properties in such a highly turbid productive lake as Lake Taihu in China were examined through 118 samples collected during three cruises in November 2006, March 2007, and November 2007. The particulate scattering and backscattering coefficients were observed using WETLabs AC-S and ECO-BB9. A power model with a spectral exponent of -0.729 was used to simulate the particulate scattering coefficient (b(p)) spectra. It has a better performance than the linear model. Scattering parameters are more closely related to inorganic suspended matter (ISM) concentration than to other water components, such as total suspended matter (TSM), organic suspended matter (OSM), and chlorophyll a (Chla). This indicates that ISM dominates the scattering signal in the lake. Three discrepancies with oceanic/coastal waters are observed: (a) the backscattering ratio (b (bp)) decreases with an increase in the ISM concentration because of a highly strong contribution by ISM to b(p); (b) the mass-specific scattering coefficient (b(p) (m)) exhibits a wider range of variability than that reported in previous studies, which can be attributed to considerable variation in the OSM and ISM distributions; (c) the particle size distribution slope (xi) is mostly larger than 4.0 in Lake Taihu, whereas it is usually within 3.5-4.0 for marine particles. In addition, the bulk refractive index (n (p)) calculated according to the Twardowski et al. model [J. Geophys. Res. 106, 14129 (2001)JGREA20148-0227] indicates that some stations (n (p)<1.07) can be regarded as organic-particle dominant. Other stations with high ISM concentrations have a very small n (p) value mostly within 1.10-1.17. Overall, the knowledge on the scattering properties gained in this study broadens our understanding of water optics in highly turbid productive water columns.

A decade of satellite ocean color observations

After the successful Coastal Zone Color Scanner (CZCS, 1978-1986) demonstration that quantitative estimations of geophysical variables such as chlorophyll a and diffuse attenuation coefficient could be derived from top of the atmosphere radiances, a number of international missions with ocean color capabilities were launched beginning in the late 1990s. Most notable were those with global data acquisition capabilities, i.e., the Ocean Color and Temperature Sensor (OCTS,Japan, 1996-1997), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, United States, 1997-present), two Moderate Resolution Imaging Spectroradiometers (MODIS, United States, Terra/2000-present and Aqua/2002-present), the Global Imager (GLI, Japan, 2002-2003), and the Medium Resolution Imaging Spectrometer (MERIS, European Space Agency, 2002-present). These missions have provided data of exceptional quality and continuity, allowing for scientific inquiries into a wide variety of marine research topics not possible with the CZCS. This review focuses on the scientific advances made over the past decade using these data sets.

Investigation of the variations in the water leaving polarized reflectance from the POLDER satellite data over two biogeochemical contrasted oceanic areas

The biogeochemical characterization of marine particles suspended in sea water, is of fundamental importance in many areas of ocean science. Previous studies based on theoretical calculations and field measurements have demonstrated the importance of the use of the polarized light field in the retrieval of the suspended marine particles properties. However, because of the weakness of the water leaving polarized signal and of the limited number of appropriate spatial sensors, such measurements have never been exploited from space. Here we show that the marine polarized remote sensing reflectance, as detected from the POLarization and Directionality of the Earth's Reflectances (POLDER) sensor, can be measured from space over bright waters and in absence of aerosols. This feasibility study is carried out over two oceanic areas characterized by different nature of the bulk particulate assemblage: the Barents sea during an intense coccolithophore bloom, and the Rio de la Plata estuary waters dominated by suspended sediments. The retrieved absolute values of the degree of polarization, P, its angular pattern, and its behavior with the scattering level are consistent with theory and field measurements. Radiative transfer simulations confirm the sensitivity of the POLDER-2 P values to the nature of the particulate assemblage. These preliminary results are very promising for the assessment of the bulk particle composition from remote sensing of the polarized signal, at least over highly scattering waters.

Short-term Influences on Suspended Particulate Matter Distribution in the Northern Gulf of Mexico: Satellite and Model Observations

Energetic meteorological events such as frontal passages and hurricanes often impact coastal regions in the northern Gulf of Mexico that influence geochemical processes in the region. Satellite remote sensing data such as winds from QuikSCAT, suspended particulate matter (SPM) concentrations derived from SeaWiFS and the outputs (sea level and surface ocean currents) of a nested navy coastal ocean model (NCOM) were combined to assess the effects of frontal passages between 23-28 March 2005 on the physical properties and the SPM characteristics in the northern Gulf of Mexico. Typical changes in wind speed and direction associated with frontal passages were observed in the latest 12.5 km wind product from QuikSCAT with easterly winds before the frontal passage undergoing systematic shifts in direction and speed and turning northerly, northwesterly during a weak and a strong front on 23 and 27 March, respectively. A quantitative comparison of model sea level results with tide gauge observations suggest better correlations near the delta than in the western part of the Gulf with elevated sea levels along the coast before the frontal passage and a large drop in sea level following the frontal passage on 27 March. Model results of surface currents suggested strong response to wind forcing with westward and onshore currents before the frontal passage reversing into eastward, southeastward direction over a six day period from 23 to 28 March 2005. Surface SPM distribution derived from SeaWiFS ocean color data for two clear days on 23 and 28 March 2005 indicated SPM plumes to be oriented with the current field with increasing concentrations in nearshore waters due to resuspension and discharge from the rivers and bays and its seaward transport following the frontal passage. The backscattering spectral slope γ, a parameter sensitive to particle size distribution also indicated lower γ values (larger particles) in nearshore waters that decreased offshore (smaller particles). The use of both satellite and model results revealed the strong interactions between physical processes and the surface particulate field in response to the frontal passage in a large river-dominated coastal margin.