Papers by Shubha Sathyendranath
Progress in Physical Geography: Earth and Environment, 2016
Physical oceanography is the study of physical conditions, processes and variables within the oce... more Physical oceanography is the study of physical conditions, processes and variables within the ocean, including temperature–salinity distributions, mixing of the water column, waves, tides, currents and air–sea interaction processes. Here we provide a critical review of how satellite sensors are being used to study physical oceanography processes at the ocean surface and its borders with the atmosphere and sea ice. The paper begins by describing the main sensor types that are used to observe the oceans (visible, thermal infrared and microwave) and the specific observations that each of these sensor types can provide. We then present a critical review of how these sensors and observations are being used to study: (i) ocean surface currents, (ii) storm surges, (iii) sea ice, (iv) atmosphere–ocean gas exchange and (v) surface heat fluxes via phytoplankton. Exciting advances include the use of multiple sensors in synergy to observe temporally varying Arctic sea ice volume, atmosphere–oce...
Remote Sensing
Since the article “Primary Production, an Index of Climate Change in the Ocean: Satellite-Based E... more Since the article “Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades” by Kulk et al [...]
Remote Sensing
Computing the vertical structure of primary production in ocean ecosystem models requires informa... more Computing the vertical structure of primary production in ocean ecosystem models requires information about the vertical distribution of available light, chlorophyll concentration and photosynthesis response parameters. Conversely, given information on vertical structure of chlorophyll and light, we can extract photosynthesis parameters from vertical profiles of primary production measured at sea, as we illustrate here for the Bermuda Atlantic Time-Series Study. The procedure is based on a model of the production profile, which itself depends on the underwater light field. To model the light field, attenuation coefficients were estimated from measured optical profiles using a simple model of exponential decay of photosynthetically-available irradiance with depth, which accounted for 97% of the variance in the measured optical data. With the underwater light climate known, an analytical solution for the production profile was employed to recover photosynthesis parameters by minimizing the residual model error. The recovered parameters were used to model normalized production profiles and normalized watercolumn production. The model explained 95% of the variance in the measured normalized production at depth and 97% of the variance in measured normalized watercolumn production. A shifted Gaussian function was used to model biomass profiles and accounted for 93% of the variance in measured biomass at depth. An analytical solution for watercolumn production with the shifted Gaussian biomass was also tested. With the recovered photosynthesis parameters, maximum instantaneous growth rates were estimated by using a literature value for the carbon-to-chlorophyll ratio in this region of the Atlantic. An exact relationship between the maximum instantaneous growth rate and the daily growth rate in the ocean was derived. It was shown that calculating the growth rate by dividing the production by the carbon-to-chlorophyll ratio is equivalent to calculating it from the ratio of the final to the initial biomass, even when production is time dependent. Finally, the seasonal cycle of the recovered assimilation number at the Bermuda Station was constructed and analysed. The presented approach enables the estimation of photosynthesis parameters and growth rates from measured production profiles with only a few model assumptions, and increases the utility of in situ primary production measurements. The retrieved parameters have direct applications in satellite-based estimates of primary production from ocean-colour data, of which we give an example.
Remote Sensing
In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a stat... more In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth...
Journal of Operational Oceanography
Disclaimer Informa UK Limited, trading as Taylor & Francis Group, make every effort to ensure the... more Disclaimer Informa UK Limited, trading as Taylor & Francis Group, make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Informa UK Limited, trading as Taylor & Francis Group, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Informa UK Limited, trading as Taylor & Francis Group. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Informa UK Limited, trading as Taylor & Francis Group, shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Remote Sensing
Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is es... more Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.
Remote Sensing
The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical g... more The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean using an empirical model based on data from 13 Atlantic Meridional Transect cruises. Our model uses satellite-derived sea surface temperature (SST), remote-sensing reflectance at 443 and 488 nm, and the water temperature at a depth of 200 m from Argo data. The model divides the population of Prochlorococcus into two groups: ProI, which dominates under highlight conditions associated with the surface, and ProII, which favors low light found near the deep chlorophyll maximum. ProI and ProII are then summed to provide vertical profiles of the concentration of Prochlorococcus cells. This model predicts that Prochlorococcus cells contribute 32 Mt of carbon biomass (7.4 × 10 26 cells) to the Atlantic Ocean, concentrated mainly within the subtropical gyres (35%) and areas near the Equatorial Convergence Zone (30%). When projected globally, 3.4 × 10 27 Prochlorococcus cells represent 171 Mt of carbon biomass, with 43% of this global biomass allocated to the upper ocean (0-45 m depth). Annual cell standing stocks were relatively stable between the years 2003 and 2014, and the contribution of the gyres varies seasonally as gyres expand and contract, tracking changes in light and temperature, with lowest cell abundances during the boreal and austral winter (1.4 × 10 13 cells m −2), when surface cell concentrations were highest (9.8 × 10 4 cells mL −1), whereas the opposite scenario was observed in spring-summer (2 × 10 13 cells m −2). This model provides a three-dimensional view of the abundance of Prochlorococcus cells, revealing that Prochlorococcus contributes significantly to total phytoplankton biomass in the Atlantic Ocean, and can be applied using either in situ measurements at the sea surface (r 2 = 0.83) or remote-sensing observables (r 2 = 0.58).
Frontiers in Marine Science
An equation is derived to express the sensitivity of daily, watercolumn production by phytoplankt... more An equation is derived to express the sensitivity of daily, watercolumn production by phytoplankton in the ocean to variations in irradiance at the sea surface. Assuming no spectral effects, and a vertically uniform chlorophyll profile, the sensitivity is a function only of the dimensionless irradiance. Spectral effects can be accounted for as a function of the chlorophyll concentration. At the global scale, the relative reduction in daily production consequent on halving the surface irradiance (representing the expected scope for variation in surface irradiance under natural conditions) is found to be from 30 to 40%. Choice of data source for irradiance may incur a further systematic error of up to 15%. Given that local irradiance (the principal forcing for primary production) may vary from day to day, the issue of how to archive production data for the most generality is discussed and recommendations made in this regard.
Remote Sensing
The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producin... more The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put the regional fish stocks, especially Sardinella aurita, the dominant pelagic species in Ivorian industrial fishery landings, under threat from overfishing. Here, we combine in situ observations of Sardinella aurita catch, temperature, and nutrient profiles, with remote-sensing ocean-color observations, and reanalysis data of wind and sea surface temperature, to investigate relationships between Sardinella aurita catch and oceanic primary producers (including biomass and phenology of phytoplankton), and between Sardinella aurita catch and environmental conditions (including upwelling index, and turbulent mixing). We show that variations in Sardinella aurita catch in the following year may be predicted, with a confidence of 78%, based on a bilinear model using only physical variables, and with a confidence of 40% when using only biological variables. However, the physics-based model alone is not sufficient to explain the mechanism driving the year-to-year variations in Sardinella aurita catch. Based on the analysis of the relationships between biological variables, we demonstrate that in the Ivorian continental shelf, during the study period 1998-2014, population dynamics of Sardinella aurita, and oceanic primary producers, may be controlled, mainly by top-down trophic interactions. Finally, based on the predictive models constructed here, we discuss how they can provide powerful tools to support evaluation and monitoring of fishing activity, which may help towards the development of a Fisheries Information and Management System.
Earth System Science Data Discussions
The photosynthetic performance of marine phytoplankton varies in response to a variety of factors... more The photosynthetic performance of marine phytoplankton varies in response to a variety of factors, environmental and taxonomic. One of the aims of the MArine primary Production: model Parameters from Space (MAPPS) project of the European Space Agency is to assemble a global database of photosynthesis-irradiance (<i>P</i>-<i>E</i>) parameters from a range of oceanographic regimes as an aid to examining the basin-scale variability in the photophysiological response of marine phytoplankton and to use this information to improve the assignment of <i>P</i>-<i>E</i> parameters in the estimation of global marine primary production using satellite data. The MAPPS <i>P</i>-<i>E</i> Database, which consists of over 5000 <i>P</i>-<i>E</i> experiments, provides information on the spatio-temporal variability in the two <i>P</i>-<i>E</i> parameters (the assimilation number,…
Frontiers in Marine Science
2009 Ieee International Geoscience and Remote Sensing Symposium, Jul 12, 2009
This study provides a satellite-based estimate of potential primary production in the Brazilian S... more This study provides a satellite-based estimate of potential primary production in the Brazilian Southeast coast from in situ and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color data. A non-spectral and vertically homogeneous semi-analytical algorithm and a spectral vertically non-homogeneous numerical algorithm were applied to the satellite ocean color data, which incorporate simultaneously measured in situ photosynthetic parameters. A vertically generalized
Satellite-based ocean colour sensors can be used to estimate the global distribution of primary p... more Satellite-based ocean colour sensors can be used to estimate the global distribution of primary production. A general approach is presented, which consists of the development of a local algorithm to compute the photosynthetic rate by phytoplankton, and the application of this local result to estimate the integrated primary production at larger scales. Based on the well known relationship between light and photosynthesis, the local model uses spectral computation of the irradiance field at the ocean surface, and in situ observations of photosynthetic parameters. From satellite data and parametrisation of a generalised profile, the local structure of the vertical pigment profile is retrieved allowing then the computation of the submarine light field as a function of depth, wavelength and zenith angle. Finally, perspectives and applications of the model are discussed in parallel with some recent advances in scientific disciplines related to primary production.
Journal of Geophysical Research, 1992
Limnology and Oceanography, 1989
... For modeling the response of phyto-plankton assemblages to available light, the formalism of ... more ... For modeling the response of phyto-plankton assemblages to available light, the formalism of the light-saturation curve, with its associated parameters, is well established (Platt and Jassby 1976; Platt et al. 1977, 1982; Platt and Gallegos 1980; Gallegos and Platt 1981). ...
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Papers by Shubha Sathyendranath