Semianalytical model for the derivation of ocean color inherent optical properties: description, implementation, and performance assessment

Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, a d (λ) and a g (λ), respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, a d g (λ), in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions a d g (λ) into a d (λ) and a g (λ). This model improves upon a recently published model [Appl. Opt.58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of a d (λ) and a g (λ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of a d (λ) and a g (λ), and a spectral extension of model output to include the near-UV (350-400 nm) in addition to the VIS (400-700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as A D G _ V I S-U V E x t . Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and A D G _ V I S-U V E x t . Comparative analysis of model-derived and measured values of a d (λ) and a g (λ) indicates negligible or small median bias, generally within ±5% over the majority of the 350-700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.

Estimation of chromophoric dissolved organic matter and non-algal particulate absorption coefficients of seawater in the ultraviolet by extrapolation from the visible spectral region

Extending the capabilities of optical remote sensing and inverse optical algorithms, which have been commonly focused on the visible (VIS) range of the electromagnetic spectrum, to derive the optical properties of seawater in the ultraviolet (UV) range is important to advancing the understanding of various optical, biological, and photochemical processes in the ocean. In particular, existing remote-sensing reflectance models that derive the total spectral absorption coefficient of seawater, a(λ), and absorption partitioning models that partition a(λ) into the component absorption coefficients of phytoplankton, a_ph(λ), non-algal (depigmented) particles, a_d(λ), and chromophoric dissolved organic matter (CDOM), a_g(λ), are restricted to the VIS range. We assembled a quality-controlled development dataset of hyperspectral measurements of a_g(λ) (N = 1294) and a_d(λ) (N = 409) spanning a wide range of values across various ocean basins, and evaluated several extrapolation methods to extend a_g(λ), a_d(λ), and a_dg(λ) ≡ a_g(λ) + a_d(λ) into the near-UV spectral region by examining different sections of the VIS as a basis for extrapolation, different extrapolation functions, and different spectral sampling intervals of input data in the VIS. Our analysis determined the optimal method to estimate a_g(λ) and a_dg(λ) at near-UV wavelengths (350 to 400 nm) which relies on an exponential extrapolation of data from the 400-450 nm range. The initial a_d(λ) is obtained as a difference between the extrapolated estimates of a_dg(λ) and a_g(λ). Additional correction functions based on the analysis of differences between the extrapolated and measured values in the near-UV were defined to obtain improved final estimates of a_g(λ) and a_d(λ) and then the final estimates of a_dg(λ) as a sum of final a_g(λ) and a_d(λ). The extrapolation model provides very good agreement between the extrapolated and measured data in the near-UV when the input data in the blue spectral region are available at 1 or 5 nm spectral sampling intervals. There is negligible bias between the modeled and measured values of all three absorption coefficients and the median absolute percent difference (MdAPD) is small, e.g., < 5.2% for a_g(λ) and < 10.5% for a_d(λ) at all near-UV wavelengths when evaluated with the development dataset. Assessment of the model on an independent dataset of concurrent a_g(λ) and a_d(λ) measurements (N = 149) yielded similar findings with only slight reduction of performance and MdAPD remaining below 6.7% for a_g(λ) and 11% for a_d(λ). These results are promising for integration of the extrapolation method with absorption partitioning models operating in the VIS.

Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances (R _rs) by performing principal component analysis on quality controlled in situ and synthetic R _rs data. We found that retrieval algorithms should be able to retrieve no more than four free parameters from R _rs spectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) from in situ and synthetic R _rs data. The multi-parameter models showed similar performances regardless of the number of parameters. Considering the computational cost associated with larger parameter spaces, we recommend bio-optical models with three free parameters for the use of IOP or joint retrieval algorithms.

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

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

An Operational Model for Remote Estimating Absorption of Optical Activity Constituents

The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [aph(λ) and adg(λ), respectively]. This algorithm included two portions: (1) the total absorption coefficients at the blue and green bands were computed using a neural network technology-based, quasi-analytical algorithm; and (2) the relationship between the adg(λ) coefficient and the coefficient of total absorption was analyzed. This algorithm was evaluated with both in-situ observations and remote-sensed satellite data. The results showed that the algorithm could produce acceptable results in the retrievals of adg(λ) and aph(λ) in both turbid and clear waters. The results also indicated that the proposed algorithm was effective for distinguishing between adg(λ) and aph(λ) from the total coefficients of absorption, even though more independent assessments using in-situ and remote-sensed data are required to further improve the approach.

An evaluation of remote sensing algorithms for the estimation of diffuse attenuation coefficients in the ultraviolet bands

In this study, six algorithms (both empirical and semi-analytical) developed for the estimation of Kd in the ultraviolet (UV) domain (specifically 360, 380, and 400 nm) were evaluated from a dataset of 316 stations covering oligotrophic ocean and coastal waters. In particular, the semi-analytical algorithm (Lee et al. 2013) used remote sensing reflectance in these near-blue UV bands estimated from a recently developed deep learning system as the input. For Kd(380) in a range of 0.018 - 2.34 m^-1, it is found that the semi-analytical algorithm has the best performance, where the mean absolute relative difference (MARD) is 0.19, and the coefficient of determination (R²) is 0.94. For the empirical algorithms, the MARD values are 0.23-0.90, with R² as 0.70-0.92, for this evaluation dataset. For a VIIRS and in situ matchup dataset (N = 62), the MARD of Kd(380) is 0.21 (R² as 0.94) by the semi-analytical algorithm. These results indicate that a combination of deep learning system and semi-analytical algorithms can provide reliable Kd(UV) for past and present satellite ocean color missions that have no spectral bands in the UV, where global Kd(UV) products are required for comprehensive studies of UV radiation on marine primary productivity and biogeochemical processes in the ocean.

Marine picoplankton size distribution and optical property contrasts throughout the Atlantic Ocean revealed using flow cytometry

Depth-resolved flow cytometric observations have been used to determine the size distribution and refractive index (RI) of picoplankton throughout the Atlantic Ocean. Prochlorococcus frequently showed double size distribution peaks centered on ${0.75 \pm 0.25}$0.75±0.25 and ${1.75 \pm 0.25}\,\,{\rm \unicode{x00B5}{\rm m}}$1.75±0.25µm; the smallest peak diameters were ${\le}{0.65}\,\,{\rm \unicode{x00B5}{\rm m}}$≤0.65µm in the equatorial upwelling with larger cells (${\sim}{0.95}\,\,{\rm \unicode{x00B5}{\rm m}}$∼0.95µm) in the surface layers of the tropical gyres. Synechococcus was strongly monodispersed: the smallest (${\sim}{1.5}\,\,{\rm \unicode{x00B5}{\rm m}}$∼1.5µm) and largest cells (${\sim}{2.25{-}2.50}\,\,{\rm \unicode{x00B5}{\rm m}}$∼2.25-2.50µm) were encountered in the lowest and highest abundance regions, respectively. Typical RI for Prochlorococcus was found to be ${\sim}{1.06}$∼1.06, whereas for Synechococcus surface RI varied between 1.04-1.08 at high and low abundances, respectively.

A Secchi Depth Algorithm Considering the Residual Error in Satellite Remote Sensing Reflectance Data

A scheme to semi-analytically derive waters’ Secchi depth (Zsd) from remote sensing reflectance (Rrs) considering the effects of the residual errors in satellite Rrs was developed for the China Eastern Coastal Zone (CECZ). This approach was evaluated and compared against three existing algorithms using field measurements. As it was challenging to provide the accurately inherent optical properties data for running the three existing algorithms in the extremely turbid waters, the new developed algorithm worked more effective than the latter. Moreover, with both synthetic and match-up data, the results indicated that the proposed algorithm was able to minimize some residual errors in Rrs, and thus could generate inter-mission consistent Zsd results from two ocean color missions. Finally, after application of new model to satellite images, we presented the spatial and temporal variations of Secchi depth and trophic state in the CECZ during 2002–2014. The study led to several findings: Firstly, the Zsd-based trophic state index (TSI) in the East China Sea first increased since 2002, and then gradually dropped during 2008–2014. Secondly, more and more waters within 30–35 m and 20–25 m isobaths were deteriorating from oligotrophic to mesotrophic type and from mesotrophic to eutrophic water, respectively, during 2002–2014. Lastly, the TSI increased on average 0.091 and 0.286 m per year respectively in Bohai Sea and Yellow Sea since 2002, and it might only take 14 and 67 years for Bohai Sea and Yellow Sea to deteriorate from mesotrophic to eutrophic water, following their current yearly deterioration rate and trophic trend. These results highlighted the importance to make some strict regulations for protecting the aquatic environment in the CECZ.

Variability of the Suspended Particle Cross-Sectional Area in the Bohai Sea and Yellow Sea

A cross-sectional area (CSA) is a key element in the optical properties of suspended particles. The seasonal evolution of CSA has great potential for use in mapping total suspended particles, and such mapping plays an important role in water quality monitoring. In this study, the spatiotemporal variations in CSA in the Bohai Sea and the Yellow Sea were studied using temperature, salinity and chlorophyll-a data collected by four cruises. The CSA field data covered a wide range of spatiotemporal variabilities in the Bohai Sea and the Yellow Sea. The results revealed that the largest CSA (>2 m−1) was found in the coastal area, while the CSA (≤1 m−1) on the outer shelf was much smaller. Large values of CSA (>15 m−1) were observed in winter, whereas the smallest values of CSA (0~2 m−1) were observed in summer. These results suggest that vertical mixing and ocean stratification might be important physical mechanisms that influences the CSA seasonal distribution in the surface layer. The results also showed that phytoplankton played an important role in the CSA, with an R2 value of 0.601. The seasonal patterns of CSA documented in this study provide a fundamental theory for research on optical properties, mapping transparency, and photosynthetically active radiation.

Model for separating the contributions of non-algal particles and colored dissolved organic matter to light absorption by seawater

We evaluated the performance of a recently developed absorption partitioning model [J. Geophys. Res. Oceans120, 2601 (2015)JGRCEY0148-022710.1002/2014JC010604] that derives the spectral absorption coefficients of non-algal particles, a _{N A P} (λ), and colored dissolved organic matter, a _g (λ), from the total absorption coefficient of seawater. The model's performance was found unsatisfactory when the model was tested with a large dataset of absorption measurements from diverse open-ocean and coastal aquatic environments. To address these limitations, we developed a new model based on a different approach for estimating a _{N A P} (λ) and a _g (λ) from the sum of these two coefficients, a _{d g} (λ), within the visible spectral region. The very good overall performance of the model is demonstrated, with no tendency for bias and relatively small absolute differences (the median ≤20%) between the model-derived and measured values of a _{N A P} (λ) and a _g (λ) over a wide range of aquatic environments.

Approach for identifying optically shallow pixels when processing ocean-color imagery

Ocean-color remote sensing is routinely used to derive marine geophysical parameters from sensor-observed spectral water-leaving radiances. However, in clear geometrically shallow regions, traditional ocean-color algorithms can be confounded by light reflected from the seafloor. Such regions are typically referred to as "optically shallow". When performing spatiotemporal analyses of ocean color datasets, optically shallow features such as submerged sand banks and coral reefs can lead to unexpected regional biases. Most contemporary approaches mask or flag suspected optically shallow pixels based on ancillary bathymetric data. However, the extent to which seafloor reflectance contaminates the water-leaving radiance is dependent on bathymetry, water clarity and seafloor albedo. In this paper, an approach for flagging optically shallow pixels has been developed that considers all three of these variables. In the method, the optical depth of the water column at 547 nm, ζ(547), is predicted from bathymetric data and estimated water-column optical properties. If ζ(547) is less then the pre-defined threshold, a pixel is flagged as potentially optically shallow. Radiative transfer modeling was used to identify a conservative threshold value of ζ(547) = 20 for a bright sand seafloor. In addition, pixels in waters shallower than 5 m are also flagged. We also examined how varying bathymetric datasets may affect the optically shallow flag using MODIS data. It is anticipated that the optically shallow flag will benefit end-users when quality controlling derived ocean color products. Further, the flag may prove useful as a mechanism for switching between optically deep and shallow algorithms during ocean color processing.

An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral waterleaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

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.

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea systems including the Danube-Black Sea is considerable.

Implementation of an analytical Raman scattering correction for satellite ocean-color processing

Raman scattering of photons by seawater molecules is an inelastic scattering process. This effect can contribute significantly to the water-leaving radiance signal observed by space-borne ocean-color spectroradiometers. If not accounted for during ocean-color processing, Raman scattering can cause biases in derived inherent optical properties (IOPs). Here we describe a Raman scattering correction (RSC) algorithm that has been integrated within NASA's standard ocean-color processing software. We tested the RSC with NASA's Generalized Inherent Optical Properties algorithm (GIOP). A comparison between derived IOPs and in situ data revealed that the magnitude of the derived backscattering coefficient and the phytoplankton absorption coefficient were reduced when the RSC was applied, whilst the absorption coefficient of colored dissolved and detrital matter remained unchanged. Importantly, our results show that the RSC did not degrade the retrieval skill of the GIOP. In addition, a time-series study of oligotrophic waters near Bermuda showed that the RSC did not introduce unwanted temporal trends or artifacts into derived IOPs.

Spectral slopes of the absorption coefficient of colored dissolved and detrital material inverted from UV-visible remote sensing reflectance

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), S_cdm (units: nm^-1), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multiband ocean color in the visible domain. In this study, we explore the potential of semianalytically retrieving S_cdm with added ocean color information in the ultraviolet (UV) range between 360 and 400 nm. Unique features of hyperspectral remote sensing reflectance in the UV-visible wavelengths (360-500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized data set to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of S_cdm from remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors.

Remote estimation of phytoplankton size fractions using the spectral shape of light absorption

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of phytoplankton size structure using a(ph)(λ) derived from satellite observations or rapid field instrument measurements in the future.

Noise tolerance of algorithms for estimating chlorophyll a concentration in turbid waters

The accuracy and noise tolerance of 13 global models and 5 Case II chlorophyll a (chl a) retrieval models were evaluated using three dataset. It was found that if 5% input noise related to atmospheric correction is considered, then the uncertainty associated with noise tolerance varied from 5.5% to 55.6%, and these uncertainties generally accounts for 15.63% to 24.75% of the total uncertainty. This observation suggests that an optimal algorithm not only should have a strong chl a concentration prediction ability but also should possess high insensitivity to the noise of remote-sensing imagery. The accuracy evaluations of chl a models were based on comparisons of chl a predicted models with chl a concentration measured analytically for field measurements. The results indicate that none of the selected chl a estimation algorithms provide accurate retrievals of chl a in turbid waters. This may be attributed to the strong optical influence of organic and inorganic matter at the blue green range, and the non-negligible of non-organic matter absorption at the red and near-infrared ranges. In order to solve this problem, the chl a concentration retrieval models must be further optimized. After being optimized using the empirical optimized method constructed in this paper, a single parameterized NDCI (normalized difference chl a index) model produces accurate retrievals in the Yellow River Estuary, Taihu Lake and Chesapeake Bay. If 5% input noise associated with residual uncertainty 0of atmospheric correction is taken into account, the model produces only 29.96% uncertainty for the remote sensing of chl a concentration in these three turbid waters.

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into "harmful", "non-harmful" and "no bloom" categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as "unknown". This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.

A semi-analytical total suspended sediment retrieval model in turbid coastal waters: a case study in Changjiang River Estuary

A simple semi-analytical model to estimate total suspended sediment matter (3S) was established for estimating TSM concentrations in Changjiang River Estuary. The results indicate that 3S model with near-infrared wavelengths provide good estimates of TSM concentrations in the study region. Furthermore, the applicability of 3S model was evaluated using an independent data set taken from Oujiang river estuary during September 2012. The results indicate that providing an available atmospheric correction scheme for satellite imagery, the 3S model could be used for quantitative monitoring of TSM concentration in coastal waters, even though local bio-optical information is still needed to reinitialize the model.

Retrieval chlorophyll-a concentration from coastal waters: three-band semi-analytical algorithms comparison and development

The objectives of this study are to validate the applicability of a three-band algorithm in determining chlorophyll-a in eutrophic coastal waters, and to improve the model using improved three-band algorithm. Evaluated using two independent data sets collected from the West Florida Shelf, the variation three-band model was found to have a superior performance to both the three-band and modified three-band model. Using the variation three-band algorithm decreased 18% and 56% uncertainty, respectively, from the three-band and modified three-band algorithms. The significantly reduced uncertainty in chlorophyll-a estimations is attributed to effective removal of absorption of gelbstoff and suspended solids and backscattering of water molecules.

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.

Retrieval of absorption and backscattering coefficients from HJ-1A/CCD imagery in coastal waters

A simple semi-analytical model (SAB) was developed for computing a(560) and b(b)(550) from HJ-1A/CCD images. By comparison with field measurements, the SAB model produces 5.3-23.5% uncertainty for a(560) and b(b)(550) retrievals. The a(560) and b(b)(550) are also retrieved from satellite images. The match-up analysis results indicate that a(560) and b(b)(550) may be derived from the HJ-1A/CCD images with respective uncertainties of 29.84 and 21.35%. These findings imply that, provided that an atmospheric correction scheme for the green bands is available, the extensive database of HJ-1A/CCD imagery may be used for the quantitative monitoring of optical properties in coastal waters.

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.

Model of phytoplankton absorption based on three size classes

Using the phytoplankton size-class model of Brewin et al. [Ecol. Model.221, 1472 (2010)], the two-population absorption model of Sathyendranath et al. [Int. J. Remote. Sens.22, 249 (2001)] and Devred et al. [J. Geophys. Res.111, C03011 (2006)] is extended to three populations of phytoplankton, namely, picophytoplankton, nanophytoplankton, and microphytoplankton. The new model infers total and size-dependent phytoplankton absorption as a function of the total chlorophyll-a concentration. A main characteristic of the model is that all the parameters that describe it have biological or optical interpretation. The three-population model performs better than the two-population model at retrieving total phytoplankton absorption. Accounting for the contributions of picophytoplankton and nanophytoplankton, rather than the combination of both as in the two-population model, improved significantly the retrieval of phytoplankton absorption at low chlorophyll-a concentrations. Class-dependent specific absorption of phytoplankton derived using the model compares well with previously published models. However, the model presented in this paper provides the specific absorption of three size classes and is applicable to a continuum of chlorophyll-a concentrations. Absorption obtained from remotely sensed chlorophyll-a using our model compares well with in situ absorption measurements.

Modelling the global coastal ocean

Shelf and coastal seas are regions of exceptionally high biological productivity, high rates of biogeochemical cycling and immense socio-economic importance. They are, however, poorly represented by the present generation of Earth system models, both in terms of resolution and process representation. Hence, these models cannot be used to elucidate the role of the coastal ocean in global biogeochemical cycles and the effects global change (both direct anthropogenic and climatic) are having on them. Here, we present a system for simulating all the coastal regions around the world (the Global Coastal Ocean Modelling System) in a systematic and practical fashion. It is based on automatically generating multiple nested model domains, using the Proudman Oceanographic Laboratory Coastal Ocean Modelling System coupled to the European Regional Seas Ecosystem Model. Preliminary results from the system are presented. These demonstrate the viability of the concept, and we discuss the prospects for using the system to explore key areas of global change in shelf seas, such as their role in the carbon cycle and climate change effects on fisheries.