AbstractThe ocean is a sink for ~25% of the atmospheric CO2 emitted by human activities, an amount in excess of 2 petagrams of carbon per year (PgC yr−1). Time-resolved estimates of global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget. However, previous estimates of this flux, derived from surface ocean CO2 concentrations, have not corrected the data for temperature gradients between the surface and sampling at a few meters depth, or for the effect of the cool ocean surface skin. Here we calculate a time history of ocean-atmosphere CO2 fluxes from 1992 to 2018, corrected for these effects. These increase the calculated net flux into the oceans by 0.8–0.9  PgC yr−1, at times doubling uncorrected values. We estimate uncertainties using multiple interpolation methods, finding convergent results for fluxes globally after 2000, or over the Northern Hemisphere throughout the period. Our corrections reconcile surface uptake with independent estimates of the increase in ocean CO2 inventory, and suggest most ocean models underestimate uptake.

The air-sea flux of greenhouse gases [e.g. carbon dioxide (CO2)] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air-sea CO2 flux processing toolbox called the "FluxEngine," designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ, and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae Cloud allows users to easily exploit more than 8 TB of climate-quality Earth observation data for the derivation of gas fluxes. The resultant netCDF data output files contain > 20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, which verifies the air-sea CO2 flux calculations; demonstrates the use of the tools for studying global and shelf sea air-sea fluxes; and describes future developments.

Abstract. The flow (flux) of climate critical gases, such as carbon dioxide (CO2), between the ocean and the atmosphere is a fundamental component of our climate and the biogeochemical development of the oceans. Therefore, the accurate calculation of these air-sea gas fluxes is critical if we are to monitor the health of our oceans and changes to our climate. FluxEngine is an open source software toolbox that allows users to easily perform calculations of air-sea gas fluxes from model, in-situ and Earth observation data. The original development and verification of the toolbox was described in a previous publication and the toolbox is already being used by scientists across multiple disciplines. The toolbox has now been considerably updated to allow its use as a Python library, to enable simplified installation, verification of its installation, to enable the handling of multiple sparingly soluble gases and greatly expanded functionality for supporting in situ dataset analyses. This new functionality for supporting in situ analyses includes user defined grids, time periods and projections, the ability to re-analyse in situ CO2 data to a common temperature dataset and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings and research cruises. Here we describe these new capabilities and then demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO2 fluxes using in situ data from four research cruises from the Surface Ocean CO2 Atlas (SOCAT) database. The second case study shows that reanalysing an eight month time series of pCO2 data collected from a fixed station in the Baltic Sea can remove errors equal to 35 % of the net air-sea gas flux. The third case study demonstrates that biological surfactants could supress individual nitrous oxide sea-air gas fluxes by up to 13 %. The final case study illustrates how a dissipation-based gas transfer parameterisation can be implemented and used. The updated version of the toolbox (version 3) and all documentation is now freely available.
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Increasing anthropogenic carbon dioxide (CO2) emissions to the atmosphere have partially been absorbed by the global oceans. The role which the plankton community contributes to this net CO2 sink, and how it may change under climate change has been identified as a key issue to address within the United Nations decade of ocean science (2021-2030) Integrated Ocean Carbon Research (IOC-R) programme. This thesis sets out to explore how the net community production (NCP; the balance between photosynthesis and respiration) of the plankton community contributes to the variability in air-sea CO2 flux in the South Atlantic Ocean.
In Chapter 2, NCP is shown to be accurately and precisely estimated from satellite measurements with respect to in situ observations. For this, weighted statistics are used to account for satellite, in situ and model uncertainties. The accuracy of satellite NCP could be improved by up to 40% by reducing uncertainties in net primary production (NPP). In Chapter 3, these satellite NCP observations were then used within a feed forward neural network scheme (SA-FNN) to extrapolate partial pressure of CO2 in seawater (pCO2 (sw)) over space and time, which is a key component to estimating the CO2 flux. NCP improved the accuracy and precision of pCO2 (sw) fields compared to using chlorophyll a (Chl a); the primary pigment in phytoplankton which is often used as a proxy for the biological CO2 drawdown. Compared to in situ observations, the seasonal variability in pCO2 (sw) was improved using the SA-FNN in key areas such as the Amazon River plume and Benguela upwelling, which make large regional contributions to the air-sea CO2 flux in the South Atlantic Ocean. In Chapter 4, these complete pCO2 (sw) fields were used with a timeseries decomposition method to determine the drivers of air-sea CO2 flux over seasonal, interannual and multi-year timescales. NCP was shown to correlate with the variability in CO2 flux on a seasonal basis. At interannual and mutli-year timescales, NCP became a more important contributor to variability in CO2 flux. This has not been previously analysed for this region.
Mesoscale eddies in the global ocean can modify the biological, physical, and chemical properties and therefore may modify the CO2 flux. In Chapter 5, the cumulative CO2 flux of 67 long lived eddies (lifetimes > 1 year) was estimated using Lagrangian tracking with satellite observations. The eddies could enhance the CO2 flux into the South Atlantic Ocean by up to 0.08 %, through eddy modification of biological and physical properties. Collectively this research has shown that the plankton community plays a more significant role in modulating the air-sea CO2 flux in the South Atlantic Ocean, which has significant
implications for the global ocean.

Abstract. A key step in assessing the global carbon budget is the determination of the partial pressure of CO2 in seawater
(pCO2 (sw)). Spatially complete observational fields of pCO2 (sw) are routinely produced for regional and
global ocean carbon budget assessments by extrapolating sparse in situ measurements of pCO2 (sw) using satellite
observations. As part of this process, satellite chlorophyll a (Chl a) is often used as a proxy for the biological drawdown or release of
CO2. Chl a does not, however, quantify carbon fixed through photosynthesis and then respired, which is determined by net community
production (NCP). In this study, pCO2 (sw) over the South Atlantic Ocean is estimated using a feed forward neural network (FNN) scheme and either
satellite-derived NCP, net primary production (NPP) or Chl a to compare which biological proxy produces the most accurate fields of
pCO2 (sw). Estimates of pCO2 (sw) using NCP, NPP or Chl a were similar, but NCP was more accurate for the
Amazon Plume and upwelling regions, which were not fully reproduced when using Chl a or NPP. A perturbation analysis assessed the potential
maximum reduction in pCO2 (sw) uncertainties that could be achieved by reducing the uncertainties in the satellite biological
parameters. This illustrated further improvement using NCP compared to NPP or Chl a. Using NCP to estimate pCO2 (sw) showed
that the South Atlantic Ocean is a CO2 source, whereas if no biological parameters are used in the FNN (following existing annual carbon
assessments), this region appears to be a sink for CO2. These results highlight that using NCP improved the accuracy of estimating
pCO2 (sw) and changes the South Atlantic Ocean from a CO2 sink to a source. Reducing the uncertainties in NCP derived
from satellite parameters will ultimately improve our understanding and confidence in quantification of the global ocean as a CO2 sink.
.

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and
their redistribution among the atmosphere, ocean, and terrestrial biosphere
in a changing climate is critical to better understand the global carbon
cycle, support the development of climate policies, and project future
climate change. Here we describe and synthesize data sets and methodologies to
quantify the five major components of the global carbon budget and their
uncertainties. Fossil CO2 emissions (EFOS) are based on energy
statistics and cement production data, while emissions from land-use change
(ELUC), mainly deforestation, are based on land use and land-use change
data and bookkeeping models. Atmospheric CO2 concentration is measured
directly, and its growth rate (GATM) is computed from the annual
changes in concentration. The ocean CO2 sink (SOCEAN) is estimated
with global ocean biogeochemistry models and observation-based
data products. The terrestrial CO2 sink (SLAND) is estimated with
dynamic global vegetation models. The resulting carbon budget imbalance
(BIM), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with
fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission
(including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1
(40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9  ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a
BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or
sinks were too high). The global atmospheric CO2 concentration averaged over
2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an
increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %)
globally and atmospheric CO2 concentration reaching 417.2 ppm, more
than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean
and trend in the components of the global carbon budget are consistently
estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal
variability in CO2 fluxes. Comparison of estimates from multiple
approaches and observations shows (1) a persistent large uncertainty in the
estimate of land-use change emissions, (2) a low agreement between the
different methods on the magnitude of the land CO2 flux in the northern
extratropics, and (3) a discrepancy between the different methods on the
strength of the ocean sink over the last decade. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set. The data presented in
this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al. 2022b).
.

Endorheic freshwater lakes can be vital water resources for sustaining large populations. However, their land-locked nature can lead to overexploitation and long-term sediment accumulation, reducing water storage and quality. Lake Naivasha supports a rapidly expanding population and agricultural industry. Therefore, maintaining good water storage and quality within this endorheic lake is crucial for the Kenyan economy and population. The lake has a long history of level fluctuations and the region is considered to be suffering from a chronic imbalance between water supply and demand. This study quantifies the sediment deposition rate and its impact on Lake Naivasha's water levels and volume, using inexpensive remote sensing techniques that could be easily replicated for future monitoring. Evidence of sedimentation in the northern area averaging 23 mm yr−1 was identified, which is likely annually displacing between 40.2 – 576 × 103 m³ of water. The volume displaced each year is equivalent to the water required to sustain between 40 – 1152 people. These results imply that current abstraction management, based purely upon lake level readings that govern a ‘traffic lights’ system, are detrimental to the long-term survival of the lake. The results also imply that lake health is decreasing. We recommend that future monitoring of this water resource and all endorheic lakes consider measurements of available water volume in combination with lake level data using the remote sensing methods we describe.

Abstract. We first validate the performance of the Portable Optical Particle
Spectrometer (POPS), a small light-weight and high sensitivity optical
particle counter, against a reference scanning mobility particle sizer
(SMPS) for a month-long deployment in an environment dominated by biomass
burning aerosols. Subsequently, we examine any biases introduced by
operating the POPS on a quadcopter drone, a DJI Matrice 200 V2. We report
the root mean square difference (RMSD) and mean absolute difference (MAD) in
particle number concentrations (PNCs) when mounted on the UAV and operating
on the ground and when hovering at 10 m. When wind speeds are low (less than 2.6 m s−1), we find only modest differences in the RMSDs and MADs of 5 % and
3 % when operating at 10 m altitude. When wind speeds are between 2.6 and 7.7 m s−1 the RMSDs and MADs increase to 26.2 % and 19.1 %, respectively,
when operating at 10 m altitude. No statistical difference in PNCs was
detected when operating on the UAV in either ascent or descent. We also find
size distributions of aerosols in the accumulation mode (defined by
diameter, d, where 0.1 ≤ d ≤ 1 µm) are relatively consistent
between measurements at the surface and measurements at 10 m altitude, while
differences in the coarse mode (here defined by d >  1 µm)
are universally larger. Our results suggest that the impact of the UAV
rotors on the POPS PNCs are small at low wind speeds, but when operating
under a higher wind speed of up to 7.6 m s−1, larger discrepancies occur. In
addition, it appears that the POPS measures sub-micron aerosol particles
more accurately than super-micron aerosol particles when airborne on the
UAV. These measurements lay the foundations for determining the magnitude of
potential errors that might be introduced into measured aerosol particle
size distributions and concentrations owing to the turbulence created by the
rotors on the UAV.
.

AbstractBycatch is one of the key threats to juvenile marine turtles in the Mediterranean Sea. As fishing methods are regional or habitat specific, the susceptibility of marine turtles may differ according to inter- and intra-population variations in foraging ecology. An understanding of these variations is necessary to assess bycatch susceptibility and to implement region-specific management. To determine if foraging ecology differs with region, sex, and size of juvenile loggerhead turtles (Caretta caretta), stable isotope analysis of carbon and nitrogen was performed on 171 juveniles from a range of foraging regions across the central and eastern Mediterranean Sea. Isotope ratios differed with geographical region, likely due to baseline variations in δ13C and δ15N values. The absence of sex-specific differences suggests that within an area, all comparably sized animals likely exploit similar foraging strategies, and therefore, their susceptibility to fisheries threats will likely be similar. The isotope ratios of juveniles occupying the North East Adriatic and North Levantine basin increased with size, potentially due to increased consumption of more prey items at higher trophic levels from a more neritic source. Isotope ratios of juveniles with access to both neritic and oceanic habitats did not differ with size which is consistent with them consuming prey items from both habitats interchangeably. With foraging habitats exploited differently among size classes in a population, the susceptibility to fisheries interactions will likely differ with size; therefore, region-specific management approaches will be needed.

AbstractThe ocean is a sink for ~25% of the atmospheric CO2 emitted by human activities, an amount in excess of 2 petagrams of carbon per year (PgC yr−1). Time-resolved estimates of global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget. However, previous estimates of this flux, derived from surface ocean CO2 concentrations, have not corrected the data for temperature gradients between the surface and sampling at a few meters depth, or for the effect of the cool ocean surface skin. Here we calculate a time history of ocean-atmosphere CO2 fluxes from 1992 to 2018, corrected for these effects. These increase the calculated net flux into the oceans by 0.8–0.9  PgC yr−1, at times doubling uncorrected values. We estimate uncertainties using multiple interpolation methods, finding convergent results for fluxes globally after 2000, or over the Northern Hemisphere throughout the period. Our corrections reconcile surface uptake with independent estimates of the increase in ocean CO2 inventory, and suggest most ocean models underestimate uptake.

This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections.

Understanding the spatial and foraging ecology of marine migrants is challenging, due to the vast distances travelled and the numerous habitats occupied within a dynamic seascape. Mediterranean marine turtles migrate thousands of kilometers and face numerous threats, including bycatch, in their marine realm. To help inform targeted conservation, this complex marine ecology must be better understood. This thesis focuses on Mediterranean loggerhead (Caretta caretta) and green turtles (Chelonia mydas). By complementing stable isotope analysis (SIA), satellite telemetry, and environmental observations, this thesis aims to enhance our understanding of the complexities of marine turtle spatial and foraging ecology, as well as determine how future climate conditions may influence their habitat use.

In Chapter 1, I introduce the importance of conserving marine migrants and discuss the current knowledge of marine turtle spatial and foraging ecology as well as threats faced, with particular emphasis on Mediterranean loggerhead and green turtles. By conducting an extensive review in Chapter 2, I demonstrate how SIA has been used to enhance our understanding of marine turtle ecology, as well as help inform conservation initiatives. I also highlight knowledge gaps (for example, bias in the species studied) and provide recommendations for future SIA studies (for example, following standardised protocols), and use this information to inform latter chapters. In Chapter 3, using SIA I highlight the ecological complexity of juvenile Mediterranean loggerhead turtles, demonstrating there are inter- and intra-population variations in ecology, and that region- and habitat-specific fisheries management is required. In Chapter 4, I identify the foraging grounds for two major Mediterranean loggerhead turtle populations, demonstrate foraging site fidelity over decades, show the proportion of females recruiting from each foraging region does not differ across the multi-decadal study, and suggest site-specific management would be beneficial. Finally, in Chapter 5, I show that migratory dive behaviours of loggerhead and green turtles are influenced by changes in environmental conditions (e.g. wave height and temperature) and that the species-specific migratory corridors used may be due to factors such as feeding preference and physiology, rather than species-specific environmental tolerances, suggesting dynamic and species-specific conservation is required. In Chapter 6, I summarise and discuss the findings from this thesis within the wider context. In conclusion, this thesis emphasises the complexities of marine turtle spatial ecology, shows that habitat use will likely differ under future climate scenarios, and suggests targeted and dynamic conservation is required for effective long term conservation.

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.

Cyanobacteria are increasingly appearing as nuisance blooms in freshwater bodies worldwide, causing problems for drinking water treatment, recreation and ecology. These blooms are often the result of accelerating eutrophication caused by anthropogenic influences, such as nutrient inputs from agriculture. This study focussed on Argal Reservoir, a eutrophic lake and source of drinking water that suffers from blooms of cyanobacteria, complicating the water treatment process. Through a combination of spatially distributed data collected through fieldwork (from moorings and sampling) and long-term monitoring data from third parties, the spatial distribution and environmental drivers of cyanobacteria were investigated. Results identified vertical variations of chlorophyll and temperature within the reservoir, despite the presence of a de-stratification mixing system. Aphanizomenon Sp. and Microcystis Sp. were identified as the most dominant species of cyanobacteria in the reservoir, driven predominantly by nutrients, and demonstrated seasonal succession. Both species showed variation to their assumed preferences and therefore exhibited evidence of adapting to different environments. Further, Microcystis was identified as the species producing extremely high concentrations of cyanotoxins, including Microcystin-LR. Catchment management to reduce the sources of nutrients entering the reservoir should continue to be implemented to reduce further eutrophication. Additionally, the impact of the de-stratification system should be investigated further as it may have encouraged the transition towards Microcystis blooms due to the shallow depth of the reservoir. The simple and low-cost methods employed in this study have allowed considerable insight into the conditions within the reservoir. The same approach could be applied to other freshwater reservoirs enabling inexpensive bespoke reservoir management.

Abstract. The flow (flux) of climate-critical gases, such as carbon dioxide
(CO2), between the ocean and the atmosphere is a fundamental component
of our climate and an important driver of the biogeochemical systems within
the oceans. Therefore, the accurate calculation of these air–sea gas fluxes
is critical if we are to monitor the oceans and assess the impact that these
gases are having on Earth's climate and ecosystems. FluxEngine is an open-source software toolbox that allows users to easily perform calculations of
air–sea gas fluxes from model, in situ, and Earth observation data. The original
development and verification of the toolbox was described in a previous
publication. The toolbox has now been considerably updated to allow for its use
as a Python library, to enable simplified installation, to ensure verification of its
installation, to enable the handling of multiple sparingly soluble gases, and to enable the
greatly expanded functionality for supporting in situ dataset analyses. This new
functionality for supporting in situ analyses includes user-defined grids, time
periods and projections, the ability to reanalyse in situ CO2 data to a
common temperature dataset, and the ability to easily calculate gas fluxes
using in situ data from drifting buoys, fixed moorings, and research cruises. Here
we describe these new capabilities and demonstrate their application
through illustrative case studies. The first case study demonstrates the
workflow for accurately calculating CO2 fluxes using in situ data from four
research cruises from the Surface Ocean CO2 ATlas (SOCAT) database. The
second case study calculates air–sea CO2 fluxes using in situ data from a
fixed monitoring station in the Baltic Sea. The third case study focuses on
nitrous oxide (N2O) and, through a user-defined gas transfer
parameterisation, identifies that biological surfactants in the North
Atlantic could suppress individual N2O sea–air gas fluxes by up to
13 %. The fourth and final case study illustrates how a dissipation-based
gas transfer parameterisation can be implemented and used. The updated
version of the toolbox (version 3) and all documentation is now freely
available.
.

This submission includes the reference data required to perform a complete verification of the FluxEngine v3.0 install. All data are in netCDF-3 format. Note that this dataset is greater than 100 MB in size.
FluxEngine is an open source software toolkit for calculating in situ, regional or global gas fluxes between the atmosphere and ocean. It can be used with model, in situ or satellite Earth observation data. A full description of the toolkit is provided in Shutler et al. (2016) and the FluxEngine software can be freely downloaded from GitHub: https://github.com/oceanflux-ghg/FluxEngine
Input data are for the year 2010 with the exception of the Takahashi climatology, which is for the year 2000. The following input data are included:
. input_data/air_pressure - atmospheric pressure data from the European Centre for Medium-Range Weather Forecasts (ECMWF, www.ecmwf.int)
. input_data/ice - fraction ice coverage from the Ocean and Sea Ice Satellite Application Facility (OSI SAF, www.osi-saf.org)
. input_data/rain_gpcp - total precipitation data from the National Oceanic and Atmospheric Administration (NOAA) Global Precipitation Climatology Project (v2.2)
. input_data/sig_wv_ht - significant wave height data from the GlobWave project (www.globwave.org)
. input_data/sigma0 - radar backscatter from the GlobWave project (www.globwave.org)
. input_data/windu10 - wind speed at 10 m above sea level from the GlobWave project (www.globwave.org)
. input_data/SMOS - ocean salinity data from Centre Abal de Traitement des Données Soil Moisture and Ocean Salinity (CATDS SMOS v01, www.catds.fr)
. input_data/SOCATv4 - the Surface Ocean CO₂ Atlas (SOCAT) (Bakker et al. 2016) version 4 reanalysed to a CO₂ climatology using (Goddijn-Murphy et al. 2015)
. input_data/SST - skin sea surface temperature (SST) data from the ATSR Reprocessing for Climate (ARC) project.
. input_data/sstfnd_Reynolds - sub skin sea surface temperature (Optimally Interpolated SST, OISST project: Reynolds et al. 2007, Banzon et al. 2016)
. input_data/takahashi09 - Takahashi CO₂ climatology dataset (Takahashi et al. 2009)
Where appropriate input data have been re-gridded from their original resolution into a monthly 1 × 1 degree resolution.
The verification process involves comparing a newly generated output (e.g. generated by a local install of FluxEngine) to a reference dataset for each verification scenario. These reference datasets are included in the 'reference_data' directory, and use the following naming convention:
. socatv4, takahashi09_pco2 - uses partial pressure of CO₂ (pCO₂) data from SOCATv4 or Takahashi2009 climatologies, respectively
. sst, no_gradients - does/does not use sea surface temperature gradients, respectively
. salinity - applies a correction for skin layer salinity
. N00 - uses (Nightingale et al. 2000) parameterisation for calculating gas transfer velocity
. K0, K1, K2, K3 - uses the generic formulation for calculating gas transfer velocity (using only the 0th, 1st, 2nd and 3rd order components, respectively)
. takahashi09_all_inputs - the Takahashi et al. (2009) dataset is used for all inputs in order to reproduce the results in Shutler et al. (2016).
. FEv1, FEv2, FEv3 - Reference data was generated using FluxEngine version 1, 2 or 3, respectively
Configuration files for each validation set are included in the 'configs' directory. These files are well commented and fully specify the input data to be used, data pre-processing, gas transfer velocity parameterisation and the structure of the gas flux calculation to be performed.
Acknowledgements:
The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.

Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (∼4 mm pixel−1) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object-based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9–9.5%) compared to a more densely vegetated meadow (RMSD 16–22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel−1, whereas smaller features such as cockle shells within seagrass require finer grained data (

Coastal dunes are globally-distributed dynamic ecosystems that occur at the land-sea interface. They are sensitive to disturbance both from natural forces and anthropogenic stressors, and therefore require regular monitoring to track changes in their form and function ultimately informing management decisions. Existing techniques employing satellite or airborne data lack the temporal or spatial resolution to resolve fine-scale changes in these environments, both temporally and spatially whilst fine-scale in-situ monitoring (e.g. terrestrial laser scanning) can be costly and is therefore confined to relatively small areas. The rise of proximal sensing-based Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetric techniques for land surface surveying offers an alternative, scale-appropriate method for spatially distributed surveying of dune systems. Here we present the results of an inter- and intra-annual experiment which utilised a low-cost and highly portable kite aerial photography (KAP) and SfM-MVS workflow to track sub-decimeter spatial scale changes in dune morphology over timescales of between 3 and 12 months. We also compare KAP and drone surveys undertaken at near-coincident times of the same dune system to test the KAP reproducibility. Using a Monte Carlo based change detection approach (Multiscale Model to Model Cloud Comparison (M3C2)) which quantifies and accounts for survey uncertainty, we show that the KAP-based survey technique, whilst exhibiting higher x,y,z uncertainties than the equivalent drone methodology, is capable of delivering data describing dune system topographical change. Significant change (according to M3C2); both positive (accretion) and negative (erosion) was detected across 3, 6 and 12 month timescales with the majority of change detected below 500 mm. Significant topographic changes as small as ~20 mm were detected between surveys. We demonstrate that portable, low-cost consumer-grade KAP survey techniques, which have been employed for decades for hobbyist aerial photography can now deliver science-grade data, and we argue that kites are well-suited to coastal survey where winds and sediment might otherwise impede surveys by other proximal sensing platforms, such as drones.

This dataset consists of sea surface temperature (SST) data collected by recreational surfers around the southern UK and Western Ireland coastline over the period from 5th January 2014 to 8th February 2017. These data were collected as part of a research project supported by Plymouth Marine Laboratory. Over the study period, the recreational surfers collected 297 independent samples of SST. The surfers were equipped with a UTBI-001 Tidbit V2 Temperature Data Logger and a Garmin etrex 10 GPS. The Garmin etrex 10 device was used to extract GPS information (latitude and longitude) for each surf. The Tidbit V2 temperature logger was attached, using cable-ties, at mid-point to the leash of the surfboards to ensure continuous contact with seawater when surfing, measuring temperature in the top metre of the water column. Roughly every 6 months over the study period, the Tidbit V2 temperature loggers were rigorously compared with a VWR1620-200 traceable digital thermometer (with an accuracy of 0.05 degrees C at the range of 0 to 100 degrees C) at 1 degree C intervals from 6 to 25 degrees C using a PolyScience temperature bath. Over the study period, all sensors performed within the manufacturers technical specifications. A piecewise regression to model was used to correct any Tidbit V2 temperature data collected to remove systematic biases between sensors, such that the errors in each sensor were within the accuracy of VWR1620-200 traceable digital thermometer. Temperature data were collected at 10 second intervals during each surfing session. The data were processed to remove any data collected before and after entering the water and SST were extracted by computing the median of the remaining data. Standard deviations on the remaining data are also provided to give an index of SST variability during each surf session.

AbstractThis review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea ice properties, sea surface temperature, sea surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea surface salinity, and a discussion of coincident andin situdata collection. Recommendations include commitment to data continuity, increases in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.

The air-sea flux of greenhouse gases [e.g. carbon dioxide (CO2)] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air-sea CO2 flux processing toolbox called the "FluxEngine," designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ, and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae Cloud allows users to easily exploit more than 8 TB of climate-quality Earth observation data for the derivation of gas fluxes. The resultant netCDF data output files contain > 20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, which verifies the air-sea CO2 flux calculations; demonstrates the use of the tools for studying global and shelf sea air-sea fluxes; and describes future developments.

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-ocean gas fluxes, and the potential for 4 dimensional water circulation observations. For each of these applications we explain their relevance to society, review recent advances and capability, and provide a forward look at future prospects and opportunities. We then more generally discuss future opportunities for oceanography-focussed remote-sensing, which includes the unique European Union Copernicus programme, the potential of the International Space Station and commercial miniature satellites. The increasing availability of global satellite remote-sensing observations means that we are now entering an exciting period for oceanography. The easy access to these high quality data and the continued development of novel platforms is likely to drive further advances in remote sensing of the ocean and atmospheric systems.

This dataset consists of an annual time series of sea surface temperature (SST) data collected by a recreational surfer at Wembury beach, Plymouth, UK, between 5th January 2014 and 4th January 2015. This data were collected as part of a research project funded by Plymouth Marine Laboratory. Over the study period, the recreational surfer collected 63 independent samples on SST at a near weekly temporal sampling rate at Wembury beach. The surfer was equipped with a UTBI-001 Tidbit V2 Temperature Data Logger and a Garmin etrex 10 GPS. The Garmin etrex 10 device was used to extract GPS information (latitude and longitude) for each surf. The Tidbit V2 temperature logger was attached, using cable-ties, at mid-point to the leash of the surfboard to ensure continuous contact with seawater when surfing, measuring temperature in the top metre of the water column. Three times during the period of study (May and August 2014 and January 2015), the Tidbit V2 temperature logger was rigorously compared with a VWR1620-200 traceable digital thermometer (with an accuracy of 0.05°C at the range of 0 to 100°C) at 1°C intervals from 6 to 25°C using a PolyScience temperature bath, and found to perform within the accuracy of the VWR1620-200 traceable thermometer on all three occasions. Temperature data were collected at 10 second intervals during each surfing session. The data were processed to remove any data collected before and after entering the water and SST were extracted by computing the median of the remaining data. Standard deviations on the remaining data are also provided to give an index of SST variability during each surf session

Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea-air CO2 fluxes are investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the eastern equatorial Pacific. Despite considerable spread in the detailed variations, mapping methods that fit the data more closely also tend to agree more closely with each other in regional averages. Encouragingly, this includes mapping methods belonging to complementary types - taking variability either directly from the pCO2 data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea-air CO2 flux of 0.31 PgC yr1 (standard deviation over 1992-2009), which is larger than simulated by biogeochemical process models. From a decadal perspective, the global ocean CO2 uptake is estimated to have gradually increased since about 2000, with little decadal change prior to that. The weighted mean net global ocean CO2 sink estimated by the SOCOM ensemble is -1.75 PgC yr1 (1992-2009), consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trends.

The social and economic benefits of the coastal zone make it one of the most treasured environments on our planet. Yet it is vulnerable to increasing anthropogenic pressure and climate change. Coastal management aims to mitigate these pressures while augmenting the socio-economic benefits the coastal region has to offer. However, coastal management is challenged by inadequate sampling of key environmental indicators, partly due to issues relating to cost of data collection. Here, we investigate the use of recreational surfers as platforms to improve sampling coverage of environmental indicators in the coastal zone. We equipped a recreational surfer, based in the south west United Kingdom (UK), with a temperature sensor and Global Positioning System (GPS) device that they used when surfing for a period of one year (85 surfing sessions). The temperature sensor was used to derive estimates of sea-surface temperature (SST), an important environmental indicator, and the GPS device used to provide sample location and to extract information on surfer performance. SST data acquired by the surfer were compared with data from an oceanographic station in the south west UK and with satellite observations. Our results demonstrate: (i) high-quality SST data can be acquired by surfers using low cost sensors; and (ii) GPS data can provide information on surfing performance that may help motivate data collection by surfers. Using recent estimates of the UK surfing population, and frequency of surfer participation, we speculate around 40 million measurements on environmental indicators per year could be acquired at the UK coastline by surfers. This quantity of data is likely to enhance coastal monitoring and aid UK coastal management. Considering surfing is a world-wide sport, our results have global implications and the approach could be expanded to other popular marine recreational activities for coastal monitoring of environmental indicators.

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008-2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

Phytoplankton phenology is increasingly recognised as a key ecological indicator to characterise marine ecosystems. Existing methods to quantify phenology are often limited by gaps in the data record or by differences between the assumed and actual shapes of the seasonal cycle. A novel method to estimate phytoplankton phenology from satellite chlorophyll-a data is presented here, allowing us to determine the shape of the annual cycle from the data themselves, and to fill data gaps using data from the vicinity at a larger spatial scale. Up to two chlorophyll-a peaks (blooms) per annual cycle can be identified, and their timings and magnitudes estimated. The outputs are a set of time series with no data gaps at a succession of spatial scales, together with information at each scale about the climatological shape of the annual cycle, and the timing and magnitude of the principal and secondary blooms in each year. To illustrate the application of the algorithm we present the results from a 12 year time series of SeaWiFS data from 1998 to 2009 in the North Atlantic; the timings and magnitudes of blooms show strong spatial patterns, and hence are suitable for incorporation into the definitions of ecological provinces. Due to its generic nature, the handling of data gaps and the lack of reliance on a pre-defined seasonal cycle, the method has a wide range of other potential applications including land-based phenology and the study of the timing of seasonal sea ice cover. © 2013 Elsevier Ltd. All rights reserved.

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points. © 2013 Elsevier Ltd.

We used coincident Envisat RA2 and AATSR temperature and wind speed data from 2008/2009 to calculate the global net sea-air flux of dimethyl sulfide (DMS), which we estimate to be 19.6 Tg S a-1. Our monthly flux calculations are compared to open ocean eddy correlation measurements of DMS flux from 10 recent cruises, with a root mean square difference of 3.1 μmol m-2 day-1. In a sensitivity analysis, we varied temperature, salinity, surface wind speed, and aqueous DMS concentration, using fixed global changes as well as CMIP5 model output. The range of DMS flux in future climate scenarios is discussed. The CMIP5 model predicts a reduction in surface wind speed and we estimate that this will decrease the global annual sea-air flux of DMS by 22% over 25 years. Concurrent changes in temperature, salinity, and DMS concentration increase the global flux by much smaller amounts. The net effect of all CMIP5 modelled 25 year predictions was a 19% reduction in global DMS flux. 25 year DMS concentration changes had significant regional effects, some positive (Southern Ocean, North Atlantic, Northwest Pacific) and some negative (isolated regions along the Equator and in the Indian Ocean). Using satellite-detected coverage of coccolithophore blooms, our estimate of their contribution to North Atlantic DMS emissions suggests that the coccolithophores contribute only a small percentage of the North Atlantic annual flux estimate, but may be more important in the summertime and in the northeast Atlantic.

We applied coincident Earth observation data collected during 2008 and 2009 from multiple sensors (RA2, AATSR and MERIS, mounted on the European Space Agency satellite Envisat) to characterise environmental conditions and integrated sea-air fluxes of CO2 in three Arctic seas (Greenland, Barents, Kara). We assessed net CO2 sink sensitivity due to changes in temperature, salinity and sea ice duration arising from future climate scenarios. During the study period the Greenland and Barents seas were net sinks for atmospheric CO2, with integrated sea-air fluxes of -36±14 and -11±5 Tg C yr-1, respectively, and the Kara Sea was a weak net CO2 source with an integrated sea-air flux of +2.2±1.4 Tg C yr-1. The combined integrated CO2 sea-air flux from all three was -45±18 Tg C yr-1. In a sensitivity analysis we varied temperature, salinity and sea ice duration. Variations in temperature and salinity led to modification of the transfer velocity, solubility and partial pressure of CO2 taking into account the resultant variations in alkalinity and dissolved organic carbon (DOC). Our results showed that warming had a strong positive effect on the annual integrated sea-air flux of CO2 (i.e. reducing the sink), freshening had a strong negative effect and reduced sea ice duration had a small but measurable positive effect. In the climate change scenario examined, the effects of warming in just over a decade of climate change up to 2020 outweighed the combined effects of freshening and reduced sea ice duration. Collectively these effects gave an integrated sea-air flux change of +4.0 TgC in the Greenland Sea, +6.0 Tg C in the Barents Sea and +1.7 Tg C in the Kara Sea, reducing the Greenland and Barents sinks by 11% and 53 %, respectively, and increasing the weak Kara Sea source by 81 %. Overall, the regional integrated flux changed by +11.7 Tg C, which is a 26% reduction in the regional sink. In terms of CO 2 sink strength, we conclude that the Barents Sea is the most susceptible of the three regions to the climate changes examined. Our results imply that the region will cease to be a net CO2 sink in the 2050s. © Author(s) 2013.

Coccolithophores are the primary oceanic phytoplankton responsible for the production of calcium carbonate (CaCO3). These climatically important plankton play a key role in the oceanic carbon cycle as a major contributor of carbon to the open ocean carbonate pump (∼50%) and their calcification can affect the atmosphere-to-ocean (air-sea) uptake of carbon dioxide (CO 2) through increasing the seawater partial pressure of CO2 (pCO2). Here we document variations in the areal extent of surface blooms of the globally important coccolithophore, Emiliania huxleyi, in the North Atlantic over a 10-year period (1998-2007), using Earth observation data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). We calculate the annual mean sea surface areal coverage of E. huxleyi in the North Atlantic to be 474 000 ± 104 000 km2, which results in a net CaCO 3 carbon (CaCO3-C) production of 0.14-1.71 TgCaCO 3-C per year. However, this surface coverage (and, thus, net production) can fluctuate inter-annually by -54/+81 % about the mean value and is strongly correlated with the El Nino/Southern Oscillation (ENSO) climate oscillation index (r = 0.75, p

High-biomass harmful algal blooms can kill farmed fish through toxicity, physical effects or de-oxygenation of the water column. These blooms often form over spatially large areas meaning that Earth observation is well placed to monitor and study them. In this letter, we present a statistical-based background subtraction technique that has been modified to detect high-biomass algal blooms. The method builds upon previous work and uses a statistical framework to combine spatial and temporal information to produce maps of bloom extent. Its statistical nature allows the approach to characterize the region of interest meaning that region-specific tuning is not needed. The accuracy of the approach has been evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and an in situ cell concentration dataset, resulting in a correct classification rate of 68.0% with a false alarm rate of 0.24 (n = 25). The method is then used to study the surface coverage of a large high-biomass harmful algal bloom of Karenia mikimotoi. The approach shows promise for the early warning of spatially large high-biomass algal blooms, providing valuable information to support in situ sampling campaigns. © 2012 Crown Copyright.

Coastal areas of the North Sea are commercially important for fishing and tourism, and are subject to the increasingly adverse effects of harmful algal blooms, eutrophication and climate change. Monitoring phytoplankton in these areas using Ocean Colour Remote Sensing is hampered by the high spatial and temporal variations in absorption and scattering properties. In this paper we demonstrate a clustering method based on specific-absorption properties that gives accurate water quality products from the Medium Resolution Imaging Spectrometer (MERIS). A total of 468 measurements of Chlorophyll a (Chla), Total Suspended Material (TSM), specific- (sIOP) and inherent optical properties (IOP) were measured in the North Sea between April 1999 and September 2004. Chla varied from 0.2 to 35mgm -3, TSM from 0.2 to 75gm -3 and absorption properties of coloured dissolved organic material at 442nm (a CDOM(442)) was 0.02 to 0.26m -1. The variation in absorption properties of phytoplankton (a ph) and non-algal particles (a NAP) were an order of magnitude greater than that for a ph normalized to Chla (a ph*) and a NAP normalized to TSM (a NAP*). Hierarchical cluster analysis on a ph*, a NAP. and a CDOM reduced this large data set to three groups of high a NAP*-a CDOM, low a ph. situated close to the coast, medium values further offshore and low a NAP*-a CDOM, high a ph. in open ocean and Dutch coastal waters. The median sIOP of each cluster were used to parameterize a semi-analytical algorithm to retrieve concentrations of Chla, TSM and a CDOM(442) from MERIS data. A further 60 measurements of normalized water leaving radiance (nL w), Chla, TSM, a CDOM(442) and a NAP(442) collected between 2003 and 2006 were used to assess the accuracy of the satellite products. The regionalized MERIS algorithm showed improved performance in Chla and a CDOM(442) estimates with relative percentage differences of 29 and 8% compared to 34 and 134% for standard MERIS Chla and a dg(442) products, and similar retrieval for TSM at concentrations >1g -3. © 2011.

Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~80 in situ Remote Sensing Reflectance, (Rrs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~20 SeaWiFS Rrs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log10-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla >2mgm-3 and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations >1mgm-3 and under-estimated Chla at values 5500 SeaWiFS Rrs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2mgm-3. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM >25gm-3. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~5mgm-3) in October and November and were 0.08mgm-3 further offshore increasing to 0.2mgm-3 during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2mgm-3 on the east coast of the Bay. © 2011 Elsevier Inc.

Coccolithophores are the largest source of calcium carbonate in the oceans and are considered to play an important role in oceanic carbon cycles. Current methods to detect the presence of coccolithophore blooms from Earth observation data often produce high numbers of false positives in shelf seas and coastal zones due to the spectral similarity between coccolithophores and other suspended particulates. Current methods are therefore unable to characterise the bloom events in shelf seas and coastal zones, despite the importance of these phytoplankton in the global carbon cycle. A novel approach to detect the presence of coccolithophore blooms from Earth observation data is presented. The method builds upon previous optical work and uses a statistical framework to combine spectral, spatial and temporal information to produce maps of coccolithophore bloom extent. Validation and verification results for an area of the north east Atlantic are presented using an in situ database (N = 432) and all available SeaWiFS data for 2003 and 2004. Verification results show that the approach produces a temporal seasonal signal consistent with biological studies of these phytoplankton. Validation using the in situ coccolithophore cell count database shows a high correct recognition rate of 80% and a low false-positive rate of 0.14 (in comparison to 63% and 0.34 respectively for the established, purely spectral approach). To guide its broader use, a full sensitivity analysis for the algorithm parameters is presented.

National and regional obligations to control and maintain water quality have led to an increase in coastal and estuarine monitoring. A potentially valuable tool is high temporal and spatial resolution satellite ocean colour data. NASA's MODIS-Terra and -Aqua can capture data at both 250 m and 500 m spatial resolutions and the existence of two sensors provides the possibility for multiple daily passes over a scene. However, no robust atmospheric correction method currently exists for these data, rendering them unusable for quantitative monitoring applications. Therefore, this paper presents an automatic and dynamic atmospheric correction approach allowing the determination of ocean colour. The algorithm is based around the standard MODIS 1 km atmospheric correction, includes cloud masking and is applicable to all of the visible 500 m bands. Comparison of the 500 m ocean colour data with the standard 1 km data shows good agreement and these results are further supported by in situ data comparisons. In addition, a novel method to produce 500 m chlorophyll-a estimates is presented. Comparisons of the 500 m estimates with the standard MODIS OC3M algorithm and to in situ data from a near-coast validation site are given. Crown Copyright © 2006.

The increasing interest in processing sequences of images motivates development of techniques for sequence-based object analysis and description. Accordingly, new velocity moments have been developed to allow a statistical description of both shape and associated motion through an image sequence. Through a generic framework motion information is determined using the established centralised moments, enabling statistical moments to be applied to motion based time series analysis. The translation invariant Cartesian velocity moments suffer from highly correlated descriptions due to their non-orthogonality. The new Zernike velocity moments overcome this by using orthogonal spatial descriptions through the proven orthogonal Zernike basis. Further, they are translation and scale invariant. To illustrate their benefits and application the Zernike velocity moments have been applied to gait recognition-an emergent biometric. Good recognition results have been achieved on multiple datasets using relatively few spatial and/or motion features and basic feature selection and classification techniques. The prime aim of this new technique is to allow the generation of statistical features which encode shape and motion information, with generic application capability. Applied performance analyses illustrate the properties of the Zernike velocity moments which exploit temporal correlation to improve a shape's description. It is demonstrated how the temporal correlation improves the performance of the descriptor under more generalised application scenarios, including reduced resolution imagery and occlusion. © 2006 Elsevier B.V. All rights reserved.

The Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Aqua and Terra platforms was designed to improve understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. The UK Dundee Satellite Receiving Station has two X-band receiving systems capable of capturing direct broadcast data from these spacecraft with a range covering the European shelf-areas, north-east Atlantic ocean and the western Mediterranean Sea. Raw data are transferred to the Plymouth Marine Laboratory (PML) and processed in near-real time into ocean colour and sea-surface temperature products for the academic community. Data can be used operationally and are made available through the web within 1.5 hours of the satellite overpass time. To our knowledge this is the only such developed system in Europe producing near-real time MODIS ocean colour products. © 2005 Taylor & Francis Ltd.