Inherent Optical Properties of Natural Seawater

The inherent optical properties of pure seawater and of optically active substances generally present in seawater are briefly reviewed. Interrelationship between inherent properties and some of the important apparent properties are also presented, so that apparent properties can be calculated quite simply, if the inherent properties are known, or vice versa

An Exact Solution For Modeling Photoacclimation of the Carbon-to-Chlorophyll Ratio in Phytoplankton

A widely-used theory of the photoacclimatory response in phytoplankton has, until now, been solved using a mathematical approximation that puts strong limitations on its applicability in natural conditions. We report an exact, analytic solution for the chlorophyll-to-carbon ratio as a function of the dimensionless irradiance (mixed layer irradiance normalized to the photoadaptation parameter for phytoplankton) that is applicable over the full range of irradiance occurring in natural conditions. Application of the exact solution for remote-sensing of phytoplankton carbon at large scales is illustrated using satellite-derived chlorophyll, surface irradiance data and mean photosynthesis-irradiance parameters for the season assigned to every pixel on the basis of ecological provinces. When the exact solution was compared with the approximate one at the global scale, for a particular month (May 2010), the results differed by at least 15% for about 70% of Northern Hemisphere pixels (analysis was performed during the northern hemisphere Spring bloom period) and by more than 50% for 24% of Northern Hemisphere pixels (approximate solution overestimates the carbon-to-chlorophyll ratio compared with the exact solution). Generally, the divergence between the two solutions increases with increasing available light, raising the question of the appropriate timescale for specifying the forcing irradiance in ecosystem models

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±0.25 and 1.75±0.25µm; the smallest peak diameters were ≤0.65µm in the equatorial upwelling with larger cells (∼0.95µm) in the surface layers of the tropical gyres. Synechococcus was strongly monodispersed: the smallest (∼1.5µm) and largest cells (∼2.25−2.50µm) were encountered in the lowest and highest abundance regions, respectively. Typical RI for Prochlorococcus was found to be ∼1.06, whereas for Synechococcus surface RI varied between 1.04–1.08 at high and low abundances, respectively

Size-partitioned phytoplankton carbon and carbon-to-chlorophyll ratio from ocean colour by an absorption-based bio-optical algorithm

The standing stock of phytoplankton carbon is a fundamental property of oceanic ecosystems, and of critical importance to the development of Earth System models for assessing global carbon pools and cycles. Some methods to estimate phytoplankton carbon at large scales from ocean-colour data rely on the parameterization of carbon-to-chlorophyll ratio, which is known to depend on factors such as the phytoplankton community structure, whereas other methods are based on the estimation of total particulate organic carbon (POC), and rely on the assumption that a known fraction of POC is made up of phytoplankton carbon. The carbon-to-chlorophyll ratio is also used in marine ecosystem models to convert between carbon and chlorophyll, a common requirement. In this paper we present a novel bio-optical algorithm to estimate the carbon-to-chlorophyll ratio, and the standing stocks of phytoplankton carbon partitioned into various size classes, from ocean colour. The approach combines empirical allometric relationships of phytoplankton size structure with an absorption-based algorithm for estimating phytoplankton size spectra developed earlier. Applying the new algorithm to satellite ocean-colour data from September 1997 to December 2013, the spatio-temporal variations of carbon-to-chlorophyll ratio and phytoplankton carbon across various size classes are computed on a global scale. The average annual stock of phytoplankton carbon, integrated over the oceanic mixed-layer depth, is estimated to be ~0.26 gigatonnes, with the size-partitioned stocks of 0.14 gigatonnes for picoplankton, 0.08 gigatonnes for nanoplankton and 0.04 gigatonnes for microplankton. The root-mean-square error and the bias in the satellite-derived estimates of picoplankton carbon, when compared with corresponding in situ data, are found to be 36.23 mgC m-3 and -13.53 mgC m-3, respectively, on individual pixels. The regional uncertainties in the estimates of phytoplankton carbon are calculated to be less than the relative uncertainties in other satellite-derived products, for most parts of the global ocean, and can amplify only for certain oceanographic regions. Although the new estimates of phytoplankton are of the same order of magnitude as those based on existing models, our study suggests that a consensus is yet to be built on the accurate sizes of the phytoplankton carbon pools; improved satellite chlorophyll products, and better estimates of inherent optical properties would be essential pre-requisites to minimising the uncertainties

Average seasonal changes in chlorophyll a in Icelandic waters

The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset

Optical classification

Optical oceanography or Marine optics is the study of light propagation in the ocean surface through absorption or scattering processes. Marine bio-optics is the term used when the absorption and scattering by particles and dissolved substances are of biological origin. Ocean color is defined as the spectral variation of the water leaving radiance that can be related to the optical constituents present in the medium (Jerlov, 1976; Morel, 1974). Visible Spectral radiometry or Ocean colour remote sensing is the study on spectral signals of optically active materials using satellite observations. When sunlight reaches the upper water column or the photic zone of the ocean surface, the light propagation is determined by the optical properties of seawater

Size Class Dependent Relationships between Temperature and Phytoplankton Photosynthesis-Irradiance Parameters in the Atlantic Ocean

Over the past decade, a number of methods have been developed to estimate size-class primary production from either in situ phytoplankton pigment data or remotely-sensed data. In this context, the first objective of this study was to compare two methods of estimating size class specific (micro-, nano-, and pico-phytoplankton) photosynthesis-irradiance (PE) parameters from pigment data. The second objective was to analyse the relationship between environmental variables (temperature, nitrate and PAR) and PE parameters in the different size-classes. A large dataset was used of simultaneous measurements of the PE parameters (n = 1,260) and phytoplankton pigment markers (n = 2,326), from 3 different institutes. There were no significant differences in mean PE parameters of the different size classes between the chemotaxonomic method of Uitz et al. (2008) and the pigment markers and carbon-to-Chl a ratios method of Sathyendranath et al. (2009). For both methods, mean maximum photosynthetic rates (PBm ) for micro-phytoplankton were significantly lower than those for pico-phytoplankton and nano-phytoplankton. The mean light limited slope (�B) for nano-phytoplankton were significantly higher than for the other size taxa. For micro-phytoplankton dominated samples identified using the Sathyendranath et al. (2009) method, both PBm and �B exhibited a significant, positive linear relationship with temperature, whereas for pico-phytoplankton the correlation with temperature was negative. Nano-phytoplankton dominated samples showed a positive correlation between PBm and temperature, whereas for �B and the light saturation parameter (Ek) the correlations were not significant. For the Uitz et al. (2008) method, only micro-phytoplankton PBm , pico-phytoplankton �B, nano- and pico-phytoplankton Ek exhibited significant relationships with temperature. The temperature ranges occupied by the size classes derived using these methods differed. The Uitz et al. (2008) method exhibited a wider temperature range compared to those derived from the Sathyendranath et al. (2009) method. The differences arise from the classification of mixed populations. Based on these patterns, we therefore recommend using the Sathyendranath et al. (2009) method to derive micro-phytoplankton PE parameters at sea water temperatures up to 8◦C during monospecific blooms and the Uitz et al. (2008) method to derive PE parameters of mixed populations over the temperature range from 8 to 18◦C. Both methods exhibited similar relationships between pico-phytoplankton PE parameters and temperatures >18◦C

Classification and Segmentation of Blooms and Plumes from High Resolution Satellite Imagery Using Deep Learning

Recent launches of high-resolution satellite sensors mean Earth Observation data are available at an unprecedented spatial and temporal scale. As data collection intensifies, our ability to inspect and investigate individual scenes for harmful algal or cyanobacterial blooms becomes limited, particularly for global monitoring. Algal Blooms and River Plumes are visible to trained experts in high resolution satellite imagery from Red-Green-Blue composites. Therefore, computer-assisted detection and classification of these events would provide invaluable information to experts and the general public on everyday water use. Advances in image recognition through Deep Learning techniques offer solutions that can accurately detect, classify and segment objects across thousands of images in a fraction of the time a human operator would require, while inspecting these images with much greater detail. Deep Learning techniques that jointly leverage spectral-spatial data are well characterised as a solution to land classification problems and have been shown to be accurate when using multi- or hyper-spectral data such as collected by the Sentinel-2 MultiSpectral Instrument. This work develops on state-of-the-art natural image segmentation algorithms to evaluate the capability of Deep Learning to detect and outline the presence of Algal Blooms or River Plumes in Sentinel 2 MSI data. The challenges in the application of these approaches are highlighted in the availability of suitable training and benchmark data, the use of atmospheric correction and neural network architecture design for utilisation of spectral data. Current Deep Learning network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines. Finally a training dataset and associated Deep Learning method is presented for use in future work relating to water contents classification

Teaching computers to see from space: deep learning and Sentinel 2

An outline of progress in the first year of research activities under my PhD. This is an outline of how and why Deep Learning can be used with remote sensing data for water contents analysis and classification, results from proof of concept experiments are described and future research activities are explained. A recording of the presentation and associated questions is available at https://1drv.ms/v/s!AsHRpsQE0ig4jPcrly23In5Tbqd10

An Exact Solution For Modeling Photoacclimation of the Carbon-to-Chlorophyll Ratio in Phytoplankton

A widely-used theory of the photoacclimatory response in phytoplankton has, until now, been solved using a mathematical approximation that puts strong limitations on its applicability in natural conditions. We report an exact, analytic solution for the chlorophyll-to-carbon ratio as a function of the dimensionless irradiance (mixed layer irradiance normalized to the photoadaptation parameter for phytoplankton) that is applicable over the full range of irradiance occurring in natural conditions. Application of the exact solution for remote-sensing of phytoplankton carbon at large scales is illustrated using satellite-derived chlorophyll, surface irradiance data and mean photosynthesis-irradiance parameters for the season assigned to every pixel on the basis of ecological provinces. When the exact solution was compared with the approximate one at the global scale, for a particular month (May 2010), the results differed by at least 15% for about 70% of Northern Hemisphere pixels (analysis was performed during the northern hemisphere Spring bloom period) and by more than 50% for 24% of Northern Hemisphere pixels (approximate solution overestimates the carbon-to-chlorophyll ratio compared with the exact solution). Generally, the divergence between the two solutions increases with increasing available light, raising the question of the appropriate timescale for specifying the forcing irradiance in ecosystem models