ESSD - A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two

ESSD - A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two
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the Creative Commons Attribution 4.0 License.
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Data description paper
15 Jul 2019
Data description paper |
15 Jul 2019
A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two
A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two
A compilation of global bio-optical in situ data for ocean-colour satellite applications –...
André Valente et al.
André Valente
Shubha Sathyendranath
Vanda Brotas
Steve Groom
Michael Grant
Malcolm Taberner
David Antoine
Robert Arnone
William M. Balch
Kathryn Barker
Ray Barlow
Simon Bélanger
Jean-François Berthon
Şükrü Beşiktepe
Yngve Borsheim
Astrid Bracher
Vittorio Brando
Elisabetta Canuti
Francisco Chavez
Andrés Cianca
Hervé Claustre
Lesley Clementson
Richard Crout
Robert Frouin
Carlos García-Soto
Stuart W. Gibb
Richard Gould
Stanford B. Hooker
Mati Kahru
Milton Kampel
Holger Klein
Susanne Kratzer
Raphael Kudela
Jesus Ledesma
Hubert Loisel
Patricia Matrai
David McKee
Brian G. Mitchell
Tiffany Moisan
Frank Muller-Karger
Leonie O'Dowd
Michael Ondrusek
Trevor Platt
Alex J. Poulton
Michel Repecaud
Thomas Schroeder
Timothy Smyth
Denise Smythe-Wright
Heidi M. Sosik
Michael Twardowski
Vincenzo Vellucci
Kenneth Voss
Jeremy Werdell
Marcel Wernand
Simon Wright
and
Giuseppe Zibordi
André Valente
CORRESPONDING AUTHOR
adovalente@fc.ul.pt
MARE – Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
Shubha Sathyendranath
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Vanda Brotas
MARE – Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Steve Groom
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Michael Grant
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany
Malcolm Taberner
EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany
David Antoine
Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, 06230 Villefranche-sur-Mer, France
Remote Sensing and Satellite Research Group, School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia
Robert Arnone
University of Southern Mississippi, Stennis Space Center, MS, USA
William M. Balch
Bigelow Laboratory for Ocean Sciences, 60 Bigelow Dr., East Boothbay, ME 04544, USA
Kathryn Barker
ARGANS Ltd, Plymouth, UK
CSIRO Oceans and Atmosphere, Perth, Western Australia, Australia
Australian Research Data Commons, Caulfield East, Australia
Ray Barlow
Bayworld Centre for Research and Education, Cape Town, South Africa
Simon Bélanger
Université du Québec à Rimouski, Rimouski, Quebec, Canada
Jean-François Berthon
European Commission, Joint Research Centre, Ispra, Italy
Şükrü Beşiktepe
Dokuz Eylul University, Institute of Marine Science and Technology, Izmir, Turkey
Yngve Borsheim
Institute of Marine Research, Bergen, Norway
Astrid Bracher
Alfred Wegener Institute Helmholtz Centre for Polar and
Marine Research, Bremerhaven, Germany
Institute of Environmental Physics, University Bremen, Bremen, Germany
Vittorio Brando
CSIRO Oceans and Atmosphere, Perth, Western Australia, Australia
CNR – ISMAR, Rome, Italy
Elisabetta Canuti
European Commission, Joint Research Centre, Ispra, Italy
Francisco Chavez
Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA
Andrés Cianca
PLOCAN – Oceanic Platform of the Canary Islands, Carretera
de Taliarte, 35214 Telde, Gran Canaria, Spain
Hervé Claustre
Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, 06230 Villefranche-sur-Mer, France
Lesley Clementson
CSIRO Oceans and Atmosphere, Perth, Western Australia, Australia
Richard Crout
Naval Research Laboratory, Stennis Space Center, MS, USA
Robert Frouin
Scripps Institution of Oceanography, University of California San Diego, CA, USA
Carlos García-Soto
Spanish Institute of Oceanography (IEO), Corazón de
María 8, 28002 Madrid, Spain
Plentziako Itsas Estazioa/Euskal Herriko Unibetsitatea
(PIE/EHU), Areatza z/g, 48620 Plentzia, Spain
Stuart W. Gibb
Environmental Research Institute, North Highland College, University of the Highlands and Islands, Thurso, Scotland, UK
Richard Gould
Naval Research Laboratory, Stennis Space Center, MS, USA
Stanford B. Hooker
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Mati Kahru
Scripps Institution of Oceanography, University of California San Diego, CA, USA
Milton Kampel
Remote Sensing Division, National Space Research Institute (INPE), Sao Jose dos Campos, Brazil
Holger Klein
Operational Oceanography Group, Federal Maritime and
Hydrographic Agency, Hamburg, Germany
Susanne Kratzer
Department of Ecology, Environment and Plant Sciences,
Stockholm University, 106 91 Stockholm, Sweden
Raphael Kudela
University of California Santa Cruz, Santa Cruz, CA, USA
Jesus Ledesma
Instituto del Mar del Perú, Callao, Peru
Hubert Loisel
Laboratoire d'Océanologie et de Géosciences, Université du Littoral-Côte-d'Opale, Université Lille, CNRS, UMR
8187, LOG, 32 avenue Foch, Wimereux, France
Patricia Matrai
Bigelow Laboratory for Ocean Sciences, 60 Bigelow Dr., East Boothbay, ME 04544, USA
David McKee
Physics Department, University of Strathclyde, Glasgow, G4 0NG, Scotland, UK
Brian G. Mitchell
Scripps Institution of Oceanography, University of California San Diego, CA, USA
Tiffany Moisan
NASA Goddard Space Flight Center, Wallops Flight
Facility, Wallops Island, VA, USA
deceased
Frank Muller-Karger
Institute for Marine Remote Sensing/ImaRS, College of
Marine Science, University of South Florida, St Petersburg, FL, USA
Leonie O'Dowd
Fisheries and Ecosystem Advisory Services, Marine
Institute, Rinville – Oranmore, Galway, Ireland
Michael Ondrusek
NOAA/NESDIS/STAR/SOCD, College Park, MD, USA
Trevor Platt
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Alex J. Poulton
Lyell Centre for Earth and Marine Science and Technology, Heriot-Watt University, Edinburgh, UK
Michel Repecaud
IFREMER Centre de Brest, Plouzane, France
Thomas Schroeder
CSIRO Oceans and Atmosphere, Perth, Western Australia, Australia
Timothy Smyth
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Denise Smythe-Wright
Ocean Biogeochemistry and Ecosystems, National
Oceanography Centre, Waterfront Campus, Southampton, UK
Heidi M. Sosik
Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
Michael Twardowski
Harbor Branch Oceanographic Institute, Fort Pierce, FL, USA
Vincenzo Vellucci
Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, 06230 Villefranche-sur-Mer, France
Kenneth Voss
University of Miami, Coral Gables, FL, USA
Jeremy Werdell
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Marcel Wernand
Royal Netherlands Institute for Sea Research, Texel, the Netherlands
deceased
Simon Wright
Australian Antarctic Division and the Antarctic Climate
and Ecosystems Cooperative Research Centre, Hobart, Australia
Giuseppe Zibordi
European Commission, Joint Research Centre, Ispra, Italy
Abstract
A global compilation of in situ data is useful to evaluate the
quality of ocean-colour satellite data records. Here we describe the data
compiled for the validation of the ocean-colour products from the ESA Ocean
Colour Climate Change Initiative (OC-CCI). The data were acquired from
several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,
MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.
Observations of the following variables were compiled: spectral
remote-sensing reflectances, concentrations of chlorophyll
, spectral
inherent optical properties, spectral diffuse attenuation coefficients and
total suspended matter. The data were from multi-project archives acquired
via open internet services or from individual projects, acquired directly
from data providers. Methodologies were implemented for homogenization,
quality control and merging of all data. No changes were made to the
original data, other than averaging of observations that were close in time
and space, elimination of some points after quality control and conversion
to a standard format. The final result is a merged table designed for
validation of satellite-derived ocean-colour products and available in text
format. Metadata of each in situ measurement (original source, cruise or
experiment, principal investigator) was propagated throughout the work and
made available in the final table. By making the metadata available,
provenance is better documented, and it is also possible to analyse each set
of data separately. This paper also describes the changes that were made to
the compilation in relation to the previous version (Valente et al., 2016).
The compiled data are available at
(Valente et al., 2019).
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Valente, A., Sathyendranath, S., Brotas, V., Groom, S., Grant, M., Taberner, M., Antoine, D., Arnone, R., Balch, W. M., Barker, K., Barlow, R., Bélanger, S., Berthon, J.-F., Beşiktepe, Ş., Borsheim, Y., Bracher, A., Brando, V., Canuti, E., Chavez, F., Cianca, A., Claustre, H., Clementson, L., Crout, R., Frouin, R., García-Soto, C., Gibb, S. W., Gould, R., Hooker, S. B., Kahru, M., Kampel, M., Klein, H., Kratzer, S., Kudela, R., Ledesma, J., Loisel, H., Matrai, P., McKee, D., Mitchell, B. G., Moisan, T., Muller-Karger, F., O'Dowd, L., Ondrusek, M., Platt, T., Poulton, A. J., Repecaud, M., Schroeder, T., Smyth, T., Smythe-Wright, D., Sosik, H. M., Twardowski, M., Vellucci, V., Voss, K., Werdell, J., Wernand, M., Wright, S., and Zibordi, G.: A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two, Earth Syst. Sci. Data, 11, 1037–1068, https://doi.org/10.5194/essd-11-1037-2019, 2019.
Received: 08 Feb 2019
Discussion started: 18 Feb 2019
Revised: 24 Jun 2019
Accepted: 25 Jun 2019
Published: 15 Jul 2019
Introduction
Currently, there are several sets of in situ bio-optical data, worldwide,
suitable for validation of ocean-colour satellite data. Whereas some are
managed by the data producers, others are in international repositories with
contributions from multiple scientists. Many have rigid quality controls and
are built specifically for ocean-colour validation. The use of only any one
of these datasets would limit the number of data in validation exercises.
It is, therefore, vital to acquire and merge all these datasets into a
single unified dataset to maximize the number of matchups available for
validation, their distribution in time and space, and, consequently, to
reduce uncertainties in the validation exercise. However, merging several
datasets together can be a complicated task. First it is necessary to
acquire and harmonize all datasets into a single standard format. Second,
during the merging, duplicates between datasets have to be identified and
removed. Third, the metadata should be propagated throughout the process and
made available in the final merged product. Ideally, the compiled dataset
would be made available as a simple text table, to facilitate ease of access
and manipulation. In this work such unification of multiple datasets is
presented. This was done for the validation of the ocean-colour products
from the ESA Ocean Colour Climate Change Initiative (OC-CCI), but with the
intent to serve the broader user community as well.
A merged dataset is not without drawbacks: it is likely to be large and so
not always easy to manipulate; because the merging is done on pre-existing,
processed databases, it is not possible to have full control of the whole
processing chain; the dataset would be a compilation of observations
collected by several investigators using different instruments, sampling
methods and protocols, which might eventually have been modified by the
processing routines used by the repositories or archives. To minimize these
potential drawbacks, we have, for the most part, incorporated only datasets
that have emerged from the long-term efforts of the ocean-colour and
biological oceanographical communities to provide scientists with
high-quality in situ data, and we implemented additional quality checks on the data to enhance confidence in the quality of the merged product.
Nevertheless, it is still recognized that different and unpredictable
uncertainties may affect data from the diverse sources as a result of the
application of a variety of field/laboratory instruments, methods and data
reduction schemes.
In Sect. 2 the methodologies used to harmonize and integrate all data, as
well as a description of individual datasets acquired, are provided. In
Sect. 3 the geographic distribution and other characteristics of the final
merged dataset are shown. Section 4 provides an overview of the data.
Data and methods
2.1
Preprocessing and merging
The compiled global set of bio-optical in situ data described in this work
has an emphasis, though not exclusive, on open-ocean data. It comprises the
following variables: remote-sensing reflectance (rrs), chlorophyll
concentration (chla), algal pigment absorption coefficient (aph),
detrital and coloured dissolved organic matter absorption coefficient
(adg), particle backscattering coefficient (bbp), diffuse attenuation
coefficient for downward irradiance (kd) and total suspended matter
(tsm). The variables rrs, aph, adg, bbp and kd are spectrally
dependent, and this dependence is, hereafter, implied. The data were
compiled from 27 sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,
MERMAID, AMT, ICES, HOT, GeP&CO, AWI, ARCSSPP, BARENTSSEA, BATS, BIOCHEM,
BODC, CALCOFI, CCELTER, CIMT, COASTCOLOUR, ESTOC, IMOS, MAREDAT, PALMER,
SEADATANET, TPSS and TARA): each one described in Sect. 2.2. The data
sources in this work should also be viewed as groups of data that were
acquired from a specific source, standardized with a specific method and
later merged into the compilation. The compiled in situ observations have a
global distribution and cover the period 1997 to 2018. The listed variables,
with the exception of total suspended matter, were chosen as they are the
operational satellite ocean-colour products of the ESA OC-CCI project, which
currently focuses on the merging of four ocean-colour satellite sensors: the
Medium Resolution Imaging Spectrometer (MERIS) of ESA, the Moderate
Resolution Imaging Spectroradiometer (MODIS) of NASA, the Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) of NASA, and the Visible Infrared Imaging
Radiometer Suite (VIIRS) of NASA and the National Oceanic and Atmospheric
Administration (NOAA) to create a time series of satellite data.
This is the second version of the compilation of global bio-optical in situ
data described by Valente et al. (2016). A track-change file of the manuscript of the first version can be found in the Supplement. The new version has more data and
a higher temporal and spatial coverage. The increases in the number of
observations are mainly for chla, rrs and aph. In comparison
with Valente et al. (2016), the observations of chla and aph have
doubled in number and provide a better spatial coverage, especially in the
Southern and Arctic Ocean. The rrs values also increased in number, but
not as much in spatial coverage, because most of the new observations came
from fixed locations.
The present second version is a compilation of data from sources used in the
first version (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT,
ICES, HOT and GeP&CO) plus data from additional sources (AWI, ARCSSPP,
BARENTSSEA, BATS, BIOCHEM, BODC, CALCOFI, CCELTER, CIMT, COASTCOLOUR, ESTOC,
IMOS, MAREDAT, PALMER,
SEADATANET, TPSS and TARA). The main differences from
the first version are (1) some of the data sources used in the first version
were updated (MOBY, AERONET, SeaBASS and HOT), (2) new data sources were
added, (3) a new variable was compiled (total suspended matter), (4) the
format of the database was modified and (5) two new flags were added.
Concerning the change in format, in Valente et al. (2016) the compilation
was provided as one unique two-dimensional table. Now, given its increased
size (136 250 rows and 1286 columns compared with 80 524 rows and 267
columns previously), the table has been broken into three smaller tables
that relate to each other via one unique key identifying each row. One
additional table is also provided to help with data manipulation. Despite
this change, the compilation should still be viewed conceptually as one
unique table, and as such, it is still described in that way. In the present
version, two flags were added: flag_time and
flag_chl_method. The first is because in
the present version three data sources were used (ESTOC, MAREDAT and TPSS)
where information on time (hour of the day) was not available. The time for
these observations was set to 12:00:00 UTC and the observations were
flagged with “1” in the column flag_time. A second flag was
necessary, because in two data sources (ARCSSPP and SEADATANET) there was
uncertainty on whether the compiled chlorophyll concentrations were measured
using fluorometric, spectrophotometric or high-performance liquid
chromatography (HPLC) methods. The compiled
chlorophyll observations from these two data sources were flagged with “1”
in the column flag_chl_method and were marked
as chla_fluor.
Remote-sensing reflectance (rrs) is a primary ocean-colour product defined
as rrs
Lw/Es, where Lw is the upward water-leaving radiance and
Es is the total downward irradiance at sea level. Another quantity that
is often required is the normalized water-leaving radiance (nLw)
(Gordon and Clark, 1981), which is related to remote-sensing reflectance via
rrs
nLw/Fo, where Fo is the top-of-the-atmosphere solar
irradiance. If not directly available, remote-sensing reflectance was
calculated through the equations described above, depending on the format of
the original data. The original data were acquired in an advanced form (e.g.
time-averaged, extrapolated to surface) from nine data sources designed for
ocean-colour validation and applications (MOBY, BOUSSOLE, AERONET-OC,
SeaBASS, NOMAD, MERMAID, COASTCOLOUR, TARA, AWI), therefore only requiring
the conversion to a common format. In the processing made by the space
agencies, the quantity rrs is normalized to a single Sun-viewing
geometry (Sun at zenith and nadir viewing) taking in account the
bidirectional effects as described in Morel and Gentili (1996) and Morel et
al. (2002). Thus, for consistency with satellite rrs product, the latter
normalization was applied to the in situ rrs.
Chlorophyll
concentration is the conventional measure for phytoplankton
biomass and one of the most widely used satellite ocean-colour products
(IOCCG, 2008). To validate satellite-derived chlorophyll
concentration,
two different variables were compiled: one of these represents chlorophyll
measurements made through fluorometric or spectrophotometric methods,
referred to hereafter as chla_fluor and the other is the
chlorophyll concentration derived from HPLC measurements, referred to hereafter as chla_hplc. The chlorophyll data were compiled from the following 25 data
sources: BOUSSOLE, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO, AWI,
ARCSSPP, BARENTSSEA, BATS, BIOCHEM, BODC, CALCOFI, CCELTER, CIMT,
COASTCOLOUR, ESTOC, IMOS, MAREDAT, PALMER, SEADATANET, TPSS and TARA. One
requirement for chla_fluor measurements was that they
were made using in vitro methods (i.e. based on extractions of
chlorophyll
). Although this severely decreased the number of observations,
since in situ fluorometry (e.g. fluorometers mounted on CTDs) is widely
available in oceanographic databases, it was decided to exclude such data
because of potential problems with the calibration of in situ fluorometer
data. The variable chla_hplc was calculated by summing
all reported chlorophyll
derivatives, including divinyl chlorophyll
epimers, allomers and chlorophyllide
. The two chlorophyll variables are
retained separately in the database to facilitate their use. HPLC
measurements could be considered of higher quality, but fluorometric
measurements are more numerous. Thus one option for users is to use
chla_fluor only when there are no chla_hplc measurements available. To be consistent with satellite-derived
chlorophyll values, which are derived from the light emerging from the upper
layer of the ocean, all chlorophyll observations in the top 10 m
(replicates at the same depth, or measurements at multiple depths) were
averaged if the coefficient of variation among observations was less than 50 %, otherwise they were discarded. The averages were then assigned to the
surface. The depth of 10 m was chosen as a compromise between clear
oligotrophic and turbid eutrophic waters. Other methods, such as chlorophyll
depth averages using local attenuation conditions (Morel and Maritorena,
2001), require observations at multiple depths, which, given our decision to
use only in vitro measurements, would have reduced considerably the final
number of observations.
With regard to the inherent optical properties (aph, adg, bbp), if not
already calculated and provided in the contributed datasets, they were
computed from related variables that were available: particle absorption
(ap), detrital absorption (ad), coloured dissolved organic matter (CDOM)
absorption (ag) and total backscattering (bb). The following equations were
used: adg
ad
ag, ap
aph
ad, and bb
bbp
bbw. For the latter equation, the variable bbw was computed using bbw
bw∕2
, where bw is the scattering coefficient of seawater derived
from Zhang et al. (2009). The diffuse attenuation coefficient for downward
irradiance (kd) did not require any conversion and was compiled as
originally acquired. Observations of inherent optical properties (surface
values) and the diffuse attenuation coefficient
for downward irradiance were
acquired in total from six data sources designed for ocean-colour validation
and applications (SeaBASS, NOMAD, MERMAID, AWI, COASTCOLOUR, TPSS), thus
already subject to the processing routines of these datasets. Concerning
total suspended matter, these data were compiled as originally available
from MERMAID and COASTCOLOUR.
The merged dataset was compiled from 27 sets of in situ data, which were
obtained individually either from archives that incorporate data from
multiple contributors (SeaBASS, NOMAD, MERMAID, ICES, ARCSSPP, BIOCHEM,
BODC, COASTCOLOUR, MAREDAT, SEADATANET) or from particular contributors,
measurement programmes or projects (MOBY, BOUSSOLE, AERONET-OC, HOT,
GeP&CO, AMT, AWI, BARENTSSEA, BATS, CALCOFI, CCELTER, CIMT, ESTOC, IMOS,
PALMER, TPSS, TARA) and were subsequently homogenized and merged. Data
contributors are listed in Table 2. There were
methodological differences between datasets. Therefore, after acquisition,
and prior to any merging, each set of data was preprocessed for quality
control and converted to a common format. During this process, data were
discarded if they had (1) unrealistic or missing date and geographic
coordinate fields, (2) poor quality (e.g. original flags) or method of
observation that did not meet the criteria for the dataset (e.g. in situ
fluorescence for chlorophyll concentration), and (3) spuriously high or low
data. For the last item, the following limits were imposed: [0.001–100] mg m
−3
for
chla_fluor and chla_hplc; [0–0.15] sr
−1
for rrs; [0.0001–10] m
−1
for aph, adg and
bbp; [0–1000] g m
−3
for tsm; and
aw(
)−10
] m
−1
for kd, where aw is the pure water absorption
coefficients derived from Pope and Fry (1997). Also during this stage, three
metadata strings were attributed to each observation: dataset,
subdataset and contributor. The dataset contains the name of the
original set of data and can only be one of the following: aoc,
boussole, mermaid, moby, nomad, seabass, hot,
ices, amt, gepco, arcsspp, awi, barentssea,
bats, biochem, bodc, calcofi, cc, ccelter, cimt,
estoc, imos, maredat, palmer, seadatanet, tpss or
tara. The subdataset starts with the dataset identifier and is
followed by additional information about the data, as
dataset
cruise/station/site
(e.g. seabass_car81).
The contributor contains the name of the data contributor. An effort was
made to homogenize the names of data contributors from the different sets of
data. These three metadata are the link to trace each observation to its
origin and were propagated throughout the processing. Finally, this
processing stage ended with each set of data being scanned for replicate
variable data and replicate station data, which when found were averaged if
the coefficient of variation was less than 50 %, otherwise they were
discarded. Replicates were defined as multiple observations of the same
variable, with the same date, time, latitude, longitude and depth. Replicate
station data were defined as multiple measurements of the same variable,
with the same date, time, latitude and longitude. For the latter case, a
search window of 5 min in time and 200 m in distance was given to
account for station drift. A small number of observations that were
identified as replicates had different subdataset identifiers (i.e.
different cruise names). These observations were considered suspicious if
the values were different and discarded. If the values were the same, one
of the observations was retained. This possibly originated from the same
group of data being contributed to an archive by two different data
contributors.
Table 1
The standard variables, nomenclatures and units in the final table.
Download Print Version
Download XLSX
Table 2
Original sets of data and data contributors in the final table.
Download XLSX
Once a set of data was homogenized, its data were integrated into a unique
table. This final merging focused on the removal of duplicates between the
sets of data. Although some duplicates are known (e.g. MOBY, BOUSSOLE,
AERONET-OC and NOMAD data are found in SeaBASS and MERMAID), others are
unknown (e.g. how many data of GeP&CO, ICES, AMT and HOT are within NOMAD,
SeaBASS and MERMAID). Therefore, duplicates were identified using the
metadata (dataset and subdataset) when possible and temporal–spatial
matches, as an additional precaution. For temporal–spatial matches, several
thresholds were used, but typically 5 min and 200 m were taken to
be sufficient to identify most duplicated data, which reflected small
differences in time, latitude and longitude, between the different sets of
data. Larger thresholds were used in some cases as a cautionary procedure.
This was the case when searching for NOMAD data in other datasets, because
NOMAD includes a few cases where merging of radiometric and pigment data was
done with large spatial–temporal thresholds (Werdell and Bailey, 2005). A
large temporal threshold was also used when integrating observations from
the three data sources that did not have time available (ESTOC, MAREDAT and
TPSS). In regard to all data, if duplicates were found, data from the NOMAD
dataset were selected first, followed by data from individual projects or
contributors (MOBY, BOUSSOLE, AERONET-OC, AMT, HOT,GeP&CO, AWI,
BARENTSSEA, BATS, CALCOFI, CCELTER, CIMT, ESTOC, IMOS, PALMER, TPSS and
TARA), and finally for the remaining datasets (SeaBASS, MERMAID,ICES,
ARCSSPP, BIOCHEM, BODC, COASTCOLOUR, MAREDAT and SEADATANET). This procedure
was chosen to preserve the NOMAD dataset as a whole, since it is widely
used in ocean-colour validation. It should be noted that, by this procedure,
data from individual projects or contributors may be listed under NOMAD
(e.g. some PALMER data are found in NOMAD with metadata string
nomad_palmer_lter). After giving priority
to NOMAD, the priority was generally given to data from individual projects
or contributors, but due to an incremental approach, where only new data are
added to previous versions of the compilation, some data from individual
projects or contributors (BATS, CALCOFI, CIMT, PALMER and TPSS) added in
later stages may be found under other data sources. This occurs mainly for
BATS and CALCOFI, which have their earlier chlorophyll data in SeaBASS with
metadata strings seabass_bats* and
seabass_cal*, and also CIMT, which has some of its data
under COASTCOLOUR. After all data from
a given source were free of
duplicates, they were merged consecutively by variable in the final table.
During this process, we also searched for rows (stations) that were
separated from each other by time differences less than 5 min and
horizontal spatial differences of less than 200 m. When such rows were
found, the observations in those rows were merged into a single row. The
compiled merged data were compared with the original sets to certify that no
errors occurred during the merging. As a final step, a water column
(station) depth was recorded for each observation, which was the closest
water column depth from the ETOPO1 global relief model (National Geophysical
Data Center ETOPO1; Amante and Eakins, 2009). For observations where the
closest water depth was above sea level (e.g. data collected very near the
coast), it was given the value of zero.
Data processing thus included two major steps: preprocessing and merging.
The first step was related to each set of contributing datasets in
particular and aimed to identify problems and convert the data of interest
to a standard format. The second step dealt with the integration of data
into one unique file and included the elimination of duplicated data between
the individual sets of data. In the next subsections a brief overview of
each original set of data is provided.
2.2
Preprocessing of each set of data
2.2.1
Marine Optical Buoy (MOBY)
MOBY is a fixed mooring system operated by the National Oceanic and
Atmospheric Administration (NOAA) that provides a continuous time series of
water-leaving radiance and surface irradiance in the visible region of the
spectra since 1997. The site is located a few kilometres west of the
Hawaiian island of Lanai where the water depth is about 1200 m. Since its
deployment, MOBY measurements have been the primary basis for the on-orbit
vicarious calibrations of the SeaWiFS and MODIS ocean-colour sensors. A full
description of the MOBY system and processing is provided in Clark et al. (2003). Data are freely available for scientific use at the MOBY Gold
directory. The products of interest are the Scientific Time Series
files, which refer to MOBY data averaged over sensor-specific wavelengths
and particular hours of the day (around 20:00–23:00 UTC). For this work, the
satellite band-average products for SeaWiFS, MODIS AQUA, MERIS, VIIRS and
the Ocean and Land Colour Instrument (OLCI) were compiled from the R2017
Reprocessing. The in-band-average subproduct was used, and to maintain
the highest quality only data determined from the upper two arms (Lw1)
and flagged as good quality were acquired. Data from the MOBY203 deployment
were discarded due to the absence of surface irradiance data. The compiled
variable was the remote-sensing reflectance, rrs, which was computed
from the original water-leaving radiance (Lw) and surface irradiance
(Es). The water-leaving radiances were corrected for the bidirectional
nature of the light field (Morel and Gentili, 1996; Morel et al., 2002)
using the same lookup table and method as that used in the SeaWiFS Data
Analysis System (SeaDAS) processing code. The MOBY data were reprocessed in
2017 (MOBY R2017 Reprocessing) to include various improvements in the
calibration of the instrument and post-processing, which include (1) a new
method to extrapolate the upwelling radiance attenuation coefficient to the
surface, (2) an increase in arm depth by 0.234 m and (3) a single pixel shift
in the data for the red spectrograph collected at a bin factor of 384. Only
the last two changes were included in present compilation. As mentioned
before, the MOBY data compiled in this work are sensor-specific. Therefore,
attention is necessary to use the correct MOBY data when validating a
particular sensor. The way MOBY data are stored in the final merged table is
consistent with the original wavelengths; however, these wavelengths can
differ from what is sometimes expected to be the central wavelength of a
given band and sensor. Irrespective of the wavelength where MOBY data are
stored in the final table, for validation of bands 1–6 of SeaWiFS, MOBY data
stored in the final merged table at 412, 443, 490, 510, 555 and 670 nm,
respectively, should be used. For validation of bands 1–6 of MODIS AQUA,
MOBY data stored in the final merged table at 416, 442, 489, 530, 547 and
665 nm, respectively, should be used. For validation of bands 1–7 of MERIS,
MOBY data stored in the final merged table at 410.5, 440.4, 487.8, 507.7,
557.6, 617.5 and 662.4 nm, respectively, are the appropriate data. For
validation of bands 2–8 of OLCI, MOBY data stored in the final merged table
at 412.0676, 443.1898, 490.7176, 510.6403, 560.5796, 620.626 nm and
665.3737, respectively, are the appropriate
data. Finally, for validation of
bands 1–5 of VIIRS, MOBY data stored in the final merged table at 412.9,
444.5, 481.2, 556.3 and 674.6 nm, respectively, are the appropriate data.
2.2.2
BOUée pour l'acquiSition de Séries Optiques à Long termE (BOUSSOLE)
The BOUSSOLE project started in 2001 with the objective of establishing a time
series of bio-optical properties in oceanic waters to support the
calibration and validation of ocean-colour satellite sensors (Antoine et
al., 2006). The project consists of a monthly cruise programme and a permanent
optical mooring (Antoine et al., 2008). The mooring collects radiometry and
inherent optical properties (IOPs) in continuous mode every 15 min at two depths (4 and 9 m nominally). The monthly cruises are devoted to the mooring
servicing, to the collection of vertical profiles of radiometry and IOPs,
and to water sampling at 11 depths from the surface down to 200 m, for
subsequent analyses including phytoplankton pigments, particulate
absorption, CDOM absorption and suspended particulate matter load. The
BOUSSOLE mooring is in the western Mediterranean Sea at a water depth of
2400 m. All pigment (2001–2012) and radiometric (2003–2012) data were
provided by the principal investigator. The compiled variables were rrs
and chla_hplc. Observations of the diffuse attenuation
coefficient (kd) were not included in the present compilation, as they
were under internal quality revision at the time of data acquisition.
Remote-sensing reflectance was computed from the original
fully normalized water-leaving radiance (nLw_ex),
which is the normalized water-leaving radiance (nLw, previously
described), with a correction for the bidirectional nature of the light
field (Morel and Gentili, 1996; Morel et al., 2002). The solar irradiance
(Fo) was computed from two available variables in the original set of
data: the normalized water-leaving radiance (nLw) and the remote-sensing
reflectance (rrs), using the equation Fo
nLw/rrs. Only radiometric
observations that meet the following criteria were used: (1) tilt of the buoy
was less than 10
, (2) the buoy was not lowered by more than 2 m
as compared to its nominal water line (to ensure the Es reference sensor is
above water and exempt from sea spray) and (3) the solar irradiance was
within 10 % of its theoretical clear-sky value (determined from Gregg and
Carder, 1990). The latter criterion was used to select clear skies only. An
additional quality control was to remove observations that were 50 %
higher or lower than the daily average. This removed a small number of
spikes in the time series. The final quality control step was to remove
days where the standard deviation was more than half of the daily average.
This was meant to identify days with high variability. Very few days (
2) were removed with this test. These quality control criteria were applied
per wavelength, which resulted in some observations with an incomplete
spectrum.
2.2.3
AErosol RObotic NETwork-Ocean Color (AERONET-OC)
AERONET-OC is a component of AERONET, including sites where sun photometers
operate with a modified measurement protocol leading to the determination of
the fully normalized water-leaving radiance (Zibordi et al., 2006, 2009). As a result of collaboration between the Joint Research
Centre (JRC) and NASA, this component has been specifically developed for
the validation of ocean-colour radiometric products. The strength of
AERONET-OC is “the production of standardized measurements that are
performed at different sites with identical measuring systems and protocols,
calibrated using a single reference source and method, and processed with
the same codes” (Zibordi et al., 2006, 2009). All high-quality data (Level-2) were acquired from the project website for 11
sites: Abu_Al_Bukhoosh (
∼ 25
N,
∼ 53
E), COVE_SEAPRISM (
∼ 36
N,
∼ 75
W),
Gloria (
∼ 44
N,
∼ 29
E),
Gustav_Dalen_Tower (
∼ 58
N,
∼ 17
E), Helsinki Lighthouse
∼ 59
N,
∼ 24
E), LISCO
∼ 40
N,
∼ 73
W), Lucinda
∼ 18
S,
∼ 146
E), MVCO
∼ 41
N,
∼ 70
W),
Palgrunden (
∼ 58
N,
∼ 13
E; Philipson et al., 2016), Venice (
∼ 45
N,
∼ 12
E) and WaveCIS_Site_CSI_6 (
∼ 28
N,
∼ 90
W). The compiled variable was rrs.
Remote-sensing reflectance was computed from the original
fully normalized water-leaving radiance (see Sect. 2.2.2 for
definition). The solar irradiance (Fo), which is not part of the
AERONET-OC data, was computed from the Thuillier et al. (2003) solar spectrum
irradiance, by averaging Fo over a wavelength-centred 10 nm window. Data
were compiled for the exact wavelengths of each record, which can change
over time for a given site depending on the specific instrument deployed.
In comparison with the previous compilation of AERONET-OC data from the Lucinda
site, a calibration correction was applied by NASA affecting instrument
SN-520. All radiometric data from this instrument provided by NASA prior to
October 2018 were underestimated by approximately a factor of 2 due to
incorrect application of instrument gains during the processing.
2.2.4
SeaWiFS Bio-optical Archive and Storage System (SeaBASS)
SeaBASS is one of the largest archives of in situ marine bio-optical data
(Werdell et al., 2003). It is maintained by NASA's Ocean Biology
Processing Group (OBPG) and includes measurements of optical properties,
phytoplankton pigment concentrations, and other related oceanographic and
atmospheric data. The SeaBASS database consists of in situ data from
multiple contributors, collected using a variety of measurement instruments
with consistent, community-vetted protocols from several marine platforms
such as fixed buoys, handheld radiometers and profiling instruments.
Quality control of the received data includes a rigorous series of
protocols
that range from file format verification to inspection of the geophysical
data values (Werdell et al., 2003). Radiometric data were acquired
through the Validation search tool, which provided in situ data with
matchups for particular ocean-colour sensors (Bailey and Werdell, 2006). The
criterion in the search query was defined to have the minimal flag
conditions in the satellite data, to retrieve a greater number of matchups
and, therefore, in situ data. Regarding phytoplankton pigment data, the
majority were acquired through the Pigment search tool, which provided
pigment data directly from the archives. As was stated in the SeaBASS
website, the Pigment search tool was originally designed to return only
in vitro fluorometric measurements, which is consistent with our approach,
but over time chlorophyll
measurements made using other methods (e.g. in
situ fluorometry) were included in the retrieved pigment data. In the
pigment data used in this work, a large number of in situ fluorometric
measurements from continuous underway instruments were identified and
discarded. These data were initially identified from cruises with more than
50 observations per day and then rechecked in the SeaBASS website to
confirm whether indeed they were continuous underway measurements. A total
of 120 412 such measurements were identified and discarded. Given the large
volume of this group of data, it is possible that some chlorophyll
observations from in situ methods may have escaped the scrutiny and
persisted into the final merged dataset. The Pigment search tool was
recently discontinued, and, instead, the File search tool can be used,
which was also used here to acquire chlorophyll observations for more recent
years. The compiled variables from SeaBASS data were rrs,
chla_hplc, chla_fluor, aph,
adg, bbp and kd. No conversion was necessary since all variables
were acquired in the desired format.
2.2.5
NASA bio-Optical Marine Algorithm Dataset (NOMAD)
NOMAD is a publicly available dataset compiled by the NASA OBPG at the
Goddard Space Flight Center. It is a high-quality global dataset of
coincident radiometric and phytoplankton pigment observations for use in
ocean-colour algorithm development and satellite-data product-validation
activities (Werdell and Bailey, 2005). The source of the bio-optical data is the
SeaBASS archive; therefore, many dependencies exist between these two datasets, which were addressed during the merging. The current version (version
2.0 ALPHA, 2008) includes data from 1991 to 2007 and an additional set of
observations of inherent optical properties. The current version was used in
this work, but with an additional set of columns of remote-sensing
reflectance corrected for the bidirectional effects (Morel and Gentili,
1996; Morel et al., 2002). This additional set of columns was provided
directly by the NOMAD creators. The compiled variables were rrs,
chla_hplc, chla_fluor, aph,
adg, bbp and kd. Conversion was necessary only for aph, adg
and bbp and followed the procedures described in Sect. 2.1. For the
calculation of bbp the variable bb was used with a smooth fitting to
remove noise. A portion of the NOMAD data were optically weighted (for methods
see Werdell and Bailey, 2005). These data are not consistent with the
protocols chosen in this work, but these observations were retained since
NOMAD is a widely used dataset in ocean-colour validation.
2.2.6
MERIS Match-up In situ Database (MERMAID)
MERMAID provides in situ bio-optical data matched with concurrent and
comparable MERIS Level 2 satellite ocean-colour products (Barker, 2013a, b). The MERMAID in situ database consists of data from multiple
contributors, measured using a variety of instruments and protocols from
several marine platforms such as fixed buoys, handheld radiometers and
profiling instruments. Comprehensive quality control and protocols are used
by MERMAID to integrate all the data into a common and comparable format
(Barker, 2013a, b). Access to MERMAID data is limited to the
MERIS Validation Team, the MERIS Quality Working Group and to the in situ
data contributors. For this work, access has been granted to the MERMAID
database through a signed service level agreement. The MERMAID data
includes subsets of several datasets used in this compilation (MOBY,
AERONET-OC, BOUSSOLE, NOMAD). These observations were removed from the
MERMAID dataset to avoid duplication (as discussed in Sect. 2.1). The
compiled variables were rrs, chla_hplc,
chla_fluor, aph, adg, bbp, kd and
tsm. Remote-sensing reflectance was calculated by dividing the
original fully normalized water-leaving reflectance (Rw_ex), which is the water-leaving reflectance (
Rw
Lw
Es
), with a
correction for the bidirectional nature of the light field (Morel and
Gentili, 1996; Morel et al., 2002), by
. Conversion was also necessary for
aph, adg and bbp and followed the procedures described in Sect. 2.1.
2.2.7
Hawaii Ocean Time-series (HOT)
HOT programme provides repeated comprehensive observations of the
hydrography, chemistry and biology of the water column at a station located
100 km north of Oahu, Hawaii, since October 1988 (Karl and Michaels, 1996).
This site is representative of the North Pacific subtropical gyre. Cruises
are made approximately once a month to the deep-water station ALOHA (A
Long-Term Oligotrophic Habitat Assessment; 22
45
N,
158
00
W). Pigment data (chla_hplc and
chla_fluor) were extracted directly from the project
website. Radiometric measurements from the HOT project are also available,
but observations of rrs and kd from the HOT project were acquired in
this work as part of the SeaBASS dataset.
2.2.8
Geochemistry, Phytoplankton, and Color of the Ocean (GeP&CO)
GeP&CO is part of the French PROOF programme and aims to describe and
understand the variability of phytoplankton populations, as well as to assess its
consequences on the geochemistry of the oceans (Dandonneau and Niang, 2007).
It is based on the quarterly travels of the merchant ship Contship London
from France to New Caledonia in the Pacific. A scientific observer sailed on
each trip and operated the sampling for surface water, filtration, various
measurements and checking at several times of each day. The experiment
started in October 1999 and finished in July 2002. Pigment data were
extracted from the project website. Additional pigment data obtained during
the OISO-4 cruise in the southern Indian Ocean on board R/V
Marion Dufresne
(January–February 2000) were added. The samples were measured by Yves Dandonneau
following the method used in the GeP&CO project. The compiled variable
was chla_hplc and chla_fluor.
2.2.9
Atlantic Meridional Transect (AMT)
AMT is a multidisciplinary programme, which undertakes biological, chemical
and physical oceanographic research during an annual voyage between the UK
and destinations in the South Atlantic (Robinson et al., 2006). The
programme was established in 1995 and since then has completed 28 research
cruises. Pigment data between 1997 (AMT5) and 2005 (AMT17) were provided by
the British Oceanographic Data Centre (BODC) following a specific request
for discrete observations of chlorophyll
concentration since 1997. The AMT
data were isolated by searching for the string AMT in the cruise
columns, and the respective principal investigators were then searched for
individually in a separated metadata file. Data not flagged with highest
quality or without method of measurement were not used. For any interest in
the original data, BODC is the point of contact, which ensures that if there
are any updates, the most recent data are supplied. The compiled variables
are chla_hplc and chla_fluor.
2.2.10
International Council for the Exploration of the Sea (ICES)
ICES is a network of more than 4000 scientists from almost 300 institutes,
with 1600 scientists participating in activities annually. The ICES Data
Centre manages a number of large dataset collections related to the marine
environment covering the northeastern Atlantic, Baltic Sea, Greenland Sea and
Norwegian Sea. The majority of data originate from national institutes that
are part of the ICES network of member countries. Data were provided (on
28 April 2014) from the ICES database on the marine environment (Copenhagen,
Denmark) following a specific request. The ICES data were made available
under the ICES data policy, and if there is any conflict between this and the
policy adopted by the users, then the ICES policy applies. The compiled
variables were chla_hplc and chla_fluor.
2.2.11
Arctic System Science Primary Production (ARCSSPP)
The ARCSSPP database is a synthesis of observations between 1954 and 2006 from the Arctic Ocean and northern seas (Matrai et al., 2013). The observations
were acquired from data repositories, publications or provided by individual
investigators. The database includes quality-controlled observations of
productivity and chlorophyll
, photosynthetically available radiation and
hydrographic parameters. This collection of data was acquired at
(last access: 10 July 2019). For the
present work, only observations of chlorophyll
concentration with known
time zones were used. The compiled chlorophyll observations were from
discrete samples, but the exact method (either chla_fluor
or chla_hplc) was not available for all observations.
Thus, the ARCSSPP chlorophyll observations were marked as
chla_fluor, although some might have been from HPLC
measurements, and were flagged with “1” in the column flag_chla_method. The compiled variable was chla_fluor.
2.2.12
Data provided by Astrid Bracher, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI)
In this work, the AWI data source refers to the group of observations that
were provided to the OC-CCI project by Astrid Bracher. These are bio-optical
observations collected during several cruises in the Atlantic and Pacific
Ocean. All data were available through the PANGAEA repository. Observations
of concentration of chlorophyll
as well as 1 nm spectrally resolved remote-sensing reflectances and the algal pigment absorption coefficient were
considered. The methods for these observations are described by Taylor et
al. (2011a). For chlorophyll, data from the following cruises were used:
ANT-XXIV/1, ANT-XXIV/4, ANT-XXVI/4 and MSM18/3
; Bracher et al., 2015); SO202/2
; Zindler et al., 2013); ANT-XXVII/2
; Bracher, 2015); ANT-XXV/1
; Taylor et al., 2011b); and ANT-XXVIII/3 and SO218
; Soppa et al., 2014). Concerning remote-sensing
reflectances, the observations taken during cruises ANT-XXIV/4 and
ANT-XXVI/4 (
; Bracher et al., 2015) and cruise ANT-XXV/1
; Taylor et al., 2011b) were gathered. The remote-sensing
reflectances
were corrected for the bidirectional nature of the light field
(Morel and Gentili, 1996; Morel et al., 2002). The absorption coefficients
were taken during cruise SO202/2 (
; Zindler et al., 2013),
cruise ANT_XXV/1 (
; Taylor et al., 2011b), and
cruises ANT-XXVI/3 and ANT-XXVIII/3 (
; Soppa et al., 2013).
The compiled variables were chla_hplc, rrs and aph.
2.2.13
Bermuda Atlantic Time-series Study (BATS)
BATS is a long-term study by the Bermuda Institute of Ocean Sciences based
on regular cruises in the western Atlantic Ocean (Sargasso Sea) since 1988.
The cruises at the BATS site (
∼ 31
40
N,
64
10
W) sample ocean temperature and salinity but are
focused on biogeochemical variables such as nutrients, dissolved inorganic
carbon, oxygen, HPLC of pigments, primary production and sediment trap flux.
In this work all the phytoplankton pigment data available from the BATS
website (
, last access: 10 July 2019) were considered, which also
included regional and transect cruises not specific to the nominal BATS
site. The compiled variables were chla_hplc and
chla_fluor.
2.2.14
Data provided by Knut Yngve Børsheim (BARENTSSEA)
The BARENTSSEA data source refers to a group of observations that were
provided to OC-CCI project by Knut Yngve Børsheim. This collection was
developed using data from the archives of the Institute of Marine Research
(Norway). It comprises observations of temperature, salinity and
chlorophyll
routinely collected by cruises, mainly in the North Sea, the
Norwegian Sea and the Barents Sea between 1997 and 2013. The chlorophyll
concentration was measured by filtering and extraction using Turner
fluorometers. The compiled variable was chla_fluor.
2.2.15
The Fisheries and Oceans Canada database for biological and chemical data (BIOCHEM)
BioChem is an archive of marine biological and chemical data maintained by
Fisheries and Oceans Canada (DFO, 2018; Devine et al., 2014). The available
observations are from department research initiatives and collected in areas
of Canadian interest. Available parameters include pH, nutrients,
chlorophyll, dissolved oxygen and other plankton data (species and biomass).
Chlorophyll measurements from in vitro fluorometric methods were extracted
(from
, last access: 10 July 2019)
with close guidance by the BioChem help desk, confirming quality and methods.
The used data span from 1997 to 2014 and were mainly from the Gulf of St Lawrence (western North Atlantic). The compiled variable was
chla_fluor.
2.2.16
British Oceanographic Data Centre (BODC)
BODC is the designated marine science data centre for the United Kingdom.
The data used in this work derive from a specific request for discrete
observations of chlorophyll
concentration since 1997. Initially, this
request was used to compile AMT data (see Sect. 2.2.9). The remaining data
comprising observations of chlorophyll
concentration from fluorometric and
HPLC methods, mostly sampled in the North Atlantic, were analysed and added
(the dataset string for this data source is bodc). Data not flagged with
highest quality or without method of measurement were discarded. The
compiled variables were chla_hplc and chla_fluor.
2.2.17
California Cooperative Oceanic Fisheries Investigations (CALCOFI)
CalCOFI is a partnership of the California Department of Fish and Wildlife,
National Oceanic and Atmospheric Administration Fisheries Service, and
Scripps Institution of Oceanography. CalCOFI has conducted quarterly cruises
off southern and central California since 1949. Data collected in the upper
500 m include temperature, salinity, oxygen, nutrients, chlorophyll,
primary productivity, plankton biodiversity and biomass. For this work,
only observations of chlorophyll
concentration derived from fluorometric
methods flagged with highest quality were used. Data were acquired from the
file CalCOFI_Database_194903-201701_csv_20Sept2017.zip available
at
(last access: 10 July 2019) The
compiled variable was chla_fluor.
2.2.18
California Current Ecosystem Long-Term Ecological Research (CCELTER)
CCELTER investigates the California Current coastal pelagic ecosystem, with
a focus on long-term forcing. The CCELTER data include primary and derived
measurements from both Process and CalCOFI-augmented cruises, as well as other time series. CCELTER data include variables from the physical environment,
biogeochemistry and biological populations/communities. For this work
chlorophyll observations measured from discrete bottle samples from CCELTER
Process cruises determined by extraction and bench fluorometry
; Goericke, 2017) were
compiled. The compiled variable was chla_fluor.
2.2.19
Center for Integrated Marine Technologies (CIMT)
CIMT was a non-operational programme where marine scientists from different
disciplines and institutions combine their efforts on observations directed
towards understanding the central California upwelling system. The CIMT
archived data include coastal ocean observations from satellites, shipboard
data, moorings and large marine animal movements. For this work, pigment
data from discrete bottle samples taken during CIMT monthly cruises were
used. Data were acquired from the project website
, last access: 10 July 2019). The compiled
variable was chla_fluor.
2.2.20
CoastColour Round Robin (COASTCOLOUR)
COASTCOLOUR datasets were designed to evaluate the performance of ocean-colour satellite algorithms in the retrieval of water quality parameters in
coastal waters (Nechad et al., 2015a). Three types of COASTCOLOUR datasets
are available: (1) a matchup dataset where in situ bio-optical observations
are available simultaneously with a cloud-free MERIS product, (2) an in situ
reflectance dataset where an in situ reflectance is available simultaneously
with an in situ measurement of chlorophyll
concentration and/or total
suspended matter, and (3) a simulated dataset where reflectances were
generated by a radiative transfer model. This work used the matchup
dataset, which includes most of the in situ measurements and is available
at
(Nechad et al., 2015b). The matchup dataset
provides optical, biogeochemical and physical data collections at 17 sites
across the globe. From this dataset, observations of reflectance,
chlorophyll
, total suspended matter and IOPs were compiled. The remote-sensing reflectances were corrected for the bidirectional nature of the
light field (Morel and Gentili, 1996; Morel et al., 2002). The compiled
variables were rrs, chla_hplc, chla_fluor, aph, adg, bbp and tsm.
2.2.21
European Station for Time series in the Ocean, Canary Islands (ESTOC)
ESTOC is an open-ocean monitoring site located in the eastern North Atlantic
subtropical gyre. ESTOC was initiated in 1991 with particle flux
measurements and in 1994 began standard observations of the water column,
in addition to the deployment of a current meter mooring. The core
parameters measured at ESTOC include salinity, temperature, current speed,
nutrients, chlorophyll, inorganic carbon, particulate organic carbon and
nitrogen, and sinking particle flux (Neuer et al., 2007). For this work
measurements of chlorophyll
concentration from monthly cruises from 1994
to 2011 were used. These data were provided to CCI following a specific
request. The time of day was unavailable and was set to 12:00:00 UTC.
These observations were flagged with “1” in the column flag_time. The compiled variable was chla_fluor.
2.2.22
Integrated Marine Observing System (IMOS)
IMOS is a national collaborative research infrastructure supported by
Australian Government. Since 2006, IMOS has operated a wide range of observing
equipment throughout the coastal and open ocean around Australia, making all
data openly available to the scientific community and other stakeholders
and users. In this work, the IMOS dataset refers only to a data collection
entitled IMOS National Reference Station (NRS) – Phytoplankton HPLC Pigment
Composition Analysis, which was acquired from the Australian Ocean Data
Network Portal (
, last access: 10 July 2019). This dataset comprises
phytoplankton pigment composition measured by HPLC collected as part of the
IMOS National Mooring Network – National Reference Station field sampling.
Pigment sampling was conducted on a monthly basis with small vessels at nine
sites. The IMOS also hosts the Satellite Remote Sensing Bio-optical
Database, which comprises phytoplankton pigment composition measured by HPLC
collected as part of a suite of bio-optical parameters from samples
collected from research voyages in Australian waters; however, for this work,
the observations from the IMOS Bio-optical Database were acquired as a
subset of the SeaBASS dataset. The compiled variable was
chla_hplc.
Figure 1
Relative spectral frequency of remote-sensing reflectance in the
final table, using 10 nm wide class intervals, defined as the ratio of the
number of observations at a particular waveband to the total number of
observations at all wavebands, multiplied by 100 to report results in
percentage. Data at a total of 611 unique wavelengths, between 404.7 and
1022.1 nm, were compiled.
Figure 2
The distribution of
(a)
rrs at 44
nm and
(b)
rrs at 55
nm. Data were first searched for at 445 and 555 nm and then with a search
window of up to 8 nm to include data at 547 nm. The black boxes delimit the
percentiles 0.25 and 0.75 of the data and the black horizontal lines show
the extension of up to percentiles 0.05 and 0.95. The red line represents
the median value and the black circles the values below (and above) the
percentile 0.05 (0.95). The number of measurements of each dataset is
reported on the right axis of the graph.
2.2.23
MARine Ecosystem DATa (MAREDAT)
The MAREDAT database is a global assemblage of pigments measured by HPLC
(Peloquin et al., 2013a) from the combination of 136 independent field datasets,
solicited from investigators and databases. The database provides high-quality measurements of taxonomic pigments including chlorophyll
and
19'-butanoyloxyfucoxanthin, 19'-hexanoyloxyfucoxanthin, alloxanthin, divinyl
chlorophyll
, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin
and zeaxanthin. The database is available through PANGAEA
; Peloquin et al., 2013b). For this work only
measurements of total chlorophyll
flagged with high quality were used. The
time of day was unavailable and was set to 12:00:00 UTC. These
observations were flagged with “1” in the column flag_time.
The compiled variable was chla_hplc.
Figure 3
Temporal distribution of chlorophyll
concentration (chl),
remote-sensing reflectance (rrs), algal pigment absorption coefficient
(aph), detrital plus CDOM absorption coefficient (adg), particle
backscattering coefficient (bbp), the diffuse attenuation coefficient for
downward irradiance (kd) and total suspended matter (tsm) in the
final table. All chlorophyll data were considered, but for a given station,
HPLC data were selected if available. Colours indicate the number of
stations available for each variable, as a function of month and hemisphere
of data acquisition (N – Northern Hemisphere; S – Southern
Hemisphere). The empty (white) squares indicate no data for that month.
Figure 4
Ranges of remote-sensing reflectance band ratios (412 : 443 and
490 : 555) for all data. The points from the NOMAD dataset are shown in blue
for reference. To maximize the number of ratios per dataset a search window
up to 12 nm was used, when the four wavelengths (412, 443, 490, 555) were
not simultaneously available. The effect of different search windows was
negligible in the ratio distribution.
Figure 5
Global distribution of remote-sensing reflectance per dataset in
the final table. The data sources are identified with different colours.
Points show locations where at least one observation is available. Crosses
show sites from which time series data of remote-sensing reflectance are
available.
2.2.24
Palmer station Long-Term Ecological Research (PALMER)
PALMER is a monitoring station located in western Antarctic Peninsula. The
Palmer station investigates the marine ecology of the Southern Ocean with
a focus on the pelagic marine ecosystem, including sea ice habitats, regional
oceanography and nesting sites of seabird predators. The PALMER data include
measurements of meteorological, oceanographic, sea ice, predators, nutrients
and biogeochemistry, pigments, primary production, zooplankton and microbe
parameters. This work used the measurements of chlorophyll analysed by HPLC
and fluorometry taken at the Palmer station
; Schofield et al., 2018a; and
; Schofield et al., 2018b) and from
the annual cruises off the coast of the western Antarctic Peninsula
; Schofield et al., 2018c; and
; Schofield et al., 2017). The
compiled variables were chla_hplc and chla_fluor.
Figure 6
Comparison of coincident observations of chlorophyll
concentration derived with different methods (chla_fluor
and chla_hplc). The data were transformed prior to
regression analysis to account for their log-normal distribution.
Figure 7
Number of observations per chlorophyll
concentration acquired
with different methods (chla_fluor and
chla_hplc).
2.2.25
SeaDataNet archive (SEADATANET)
SeaDataNet is a Pan-European infrastructure for ocean and marine data
management. It aims to develop a standardized
system for managing large and
diverse datasets collected by oceanographic cruises and automatic
observation systems. For this work, discrete chlorophyll
concentration
observations with an access restriction set to academic and
unrestricted were acquired from the SeaDataNet platform with guidance from
the help desk. Only data from the Institute of Marine Research – Norwegian
Marine Data Centre (NMD), Norway, which comprised most of the acquired
data, were used. All chlorophyll observations were from discrete samples
measured by fluorometric, spectrophotometric or HPLC methods, but the exact
method was not given. Thus, the observations were marked as
chla_fluor, although some were possibly from HPLC
measurements, and were flagged with “1” in the column flag_chla_method. The compiled variable was
chla_fluor.
Figure 8
Global distribution of chlorophyll
concentration per interval of the observed value. All chlorophyll data were considered, but for a given
station, HPLC data were selected if available.
Figure 9
Global distribution of chlorophyll
concentration per dataset in
the final table. All chlorophyll data were considered, but for a given
station, HPLC data were selected if available. Crosses show sites from where
data of chlorophyll are available in a specific geographic location.
2.2.26
Data provided by Trevor Platt and Shubha Sathyendranath (TPSS)
In this work, the TPSS data source refers to a group of observations that
were provided to this compilation by Trevor Platt and Shubha Sathyendranath.
This is a collection of bio-optical in situ data collected during cruises
predominantly in the northwestern Atlantic but also from the Indian Ocean,
South Pacific and central Atlantic (see Sathyendranath et al., 2009, for
additional details regarding the cruises). It comprises measurements of
phytoplankton pigments and algal pigment absorption coefficients. The time
of day was unavailable and was set to 12:00:00 UTC. These observations
were flagged with “1” in the column flag_time. The compiled
variables were chla_hplc, chla_fluor and
aph.
Figure 10
The chlorophyll
(mg m
−3
) data partitioned into
boxes showing
(a)
number of
observations,
(b)
average value and
(c)
standard deviation in each box. All
chlorophyll data were considered, but for a given station, HPLC data were
selected if available. In the standard deviation plot, grey colour boxes
represent zero standard deviation (i.e. one observation).
2.2.27
Bio-optical data from Tara expeditions (TARA)
The Tara expeditions consist of several cruises around the world, some with
durations of several years, designed to study and understand the
distribution of planktonic organisms in the world ocean. The discrete
observations of remote-sensing reflectance and chlorophyll
concentration
from HPLC measurements taken during the Tara Oceans (2009–2013) and
Mediterranean (2014) expeditions were considered in this work. These
data were provided to the ESA OC-CCI project by Emmanuel Boss and were available
in the SeaBASS archive. The remote-sensing reflectances were corrected for
the bidirectional nature of the light field (Morel and Gentili, 1996; Morel
et al., 2002). The compiled variables were chla_hplc and
rrs.
Results
In this work several sets of bio-optical in situ data were acquired,
homogenized and merged into a single table. The table comprises in situ
observations between 1997 and 2018, with a global distribution, and includes
the following variables: remote-sensing reflectance (rrs), chlorophyll
concentration (chla), algal pigment absorption coefficient (aph),
detrital and coloured dissolved organic matter absorption (adg), particle
backscattering coefficient (bbp), diffuse attenuation coefficient for
downward irradiance (kd) and total suspended matter (tsm). All
observations in the table were processed in such a way that they can be
compared directly with satellite-derived ocean-colour data. The table
consists of 136 250 rows and 1286 columns. Each row represents a unique
station in space and time, separated from the rest by at least 5 min and
200 m. For each observation in a given station, there are three
metadata strings: dataset, subdataset and contributor. The
columns of the table take the form described in Table 1. The data
contributors are indicated in Table 2. Regarding spectral variables, all
original wavelengths were preserved, which requires a large number of unique
wavelengths to be maintained in the database. No band shifting was performed
(though some archived data in some data sources may have been merged with
nearby wavelengths) and no minimum number of wavelengths per observation was
imposed. This allows further manipulation of the table for different
purposes. In the following paragraphs, the table is analysed and the final
group of observations is described for each contributing dataset; however,
the numbers reported here do not reflect the original numbers in each dataset, since duplicates across contributing datasets were removed (e.g. NOMAD
and others were removed from MERMAID).
Observations of remote-sensing reflectance are available at 611 unique
wavelengths (i.e. columns), between 404.7 and 1022.1 nm (Fig. 1). In
total there are 59 781 observations (i.e. rows) with remote-sensing
reflectance in the table. The total number of observations are partitioned
per contributing datasets as follows: AERONET-OC (31 574), BOUSSOLE
(17 364), MOBY (5466), NOMAD (3326), MERMAID (885), SeaBASS (698), AWI
(54), COASTCOLOUR (307) and TARA (107). Data from AERONET-OC, BOUSSOLE and
MOBY correspond to continuous time series, and, hence, the higher number of
observations. Data distribution at 44
and 55
nm is provided in Fig. 2a
and b, respectively. Data were first searched for at 445 and 555 nm and then
with a search window up to 8 nm to include also data at 547 nm. Median
values at 44
nm range from 0.003 m
−1
(AERONET-OC) and 0.009 m
−1
(MOBY), whereas at 55
nm the median values lie between 0.001 m
−1
(AWI)
and 0.007 m
−1
(COASTCOLOUR). The observations are unevenly distributed
between each month of the year in both hemispheres, with a higher coverage
in summer months (Fig. 3). There are fewer data in the Southern Hemisphere
than in the Northern Hemisphere (Fig. 3). For additional analysis, rrs
band ratios were plotted against each other (490 : 555 versus 412 : 443, Fig. 4). Most points are within the boundaries of the NOMAD dataset, but some
scattered points were found. These points were retained in the table to
allow further manipulation with different quality control criteria.
Complementary analysis of remote-sensing reflectance data is made when other
variables are concurrently
available and discussed below (see Figs. 11 and
16). The geographic distribution of remote-sensing reflectance
observations (Fig. 5) shows a higher number of observations in some coastal
regions, such as those of North America and northern Europe. The central
regions of the ocean show a lower number of observations, with the Atlantic
Ocean having the highest density in relation to the other oceans. The best
geographic coverage is provided by the NOMAD database. Data from SeaBASS are
fewer in number but are still important. Data from MERMAID are mainly
located along the coasts of Europe, North America and the central region of
the North Atlantic Ocean. The observations from COASTCOLOUR are concentrated
in 17 coastal sites around the world, while AWI data are available for the
Atlantic, Pacific and Southern Ocean. TARA data are spread across several
regions, with the highest data density in the Mediterranean Sea.
Figure 11
A remote-sensing reflectance maximum band ratio (as defined in
text) ([443,490,510]
555 or [443,490,510]
560 if 555 not available) as a
function of chlorophyll
concentration. All chlorophyll data were
considered, but for a given station, HPLC data were selected if available.
Data within 2 nm of the wavelengths were used. For reference, the solid and
dotted lines show the NASA OC4 and OC4E v6 standard algorithms, respectively
, last access: 10 July 2019). The total
number of points was 3814, of which 79 % were from NOMAD.
Figure 12
The distribution of
(a)
aph at 44
nm,
(b)
aph at 55
nm,
(c)
adg at 44
nm,
(d)
adg at 55
nm,
(e)
bbp at 44
nm,
(f)
bbp at 55
nm,
(g)
kd at 44
nm, and
(h)
kd at 55
nm. Data were first searched for at 445 and 555 nm and then with a search window up to 8 nm to include data at 547 nm. The graphical convention is identical to Fig. 2.
For chlorophyll
concentration, two types of observations were compiled,
one measured by fluorometric or spectrophotometric methods
(chla_fluor) and the other measured by HPLC methods
(chla_hplc). A comparison of both measurements (Fig. 6),
when available at the same station, shows good agreement (Trees et al.,
1985). As stated before, the analysis was done on the final merged table;
thus no data were filtered and the good relation can be explained in part by
the quality control implemented by the data providers and curators of
repositories such as NOMAD and SeaBASS (Werdell and Bailey, 2005). The total
number of rows with concurrent chla_fluor and
chla_hplc is 5344, with contributions from SeaBASS (39 %), TPSS (18 %), NOMAD (13 %), PALMER (9 %), BATS (6 %),
COASTCOLOUR (5 %), MERMAID (4 %), HOT (4 %), and
AMT
GeP&CO
BODC
CCELTER
CALCOFI (2 %). The
chla_fluor observations are available in 61 525 stations
(rows), with values ranging from 0.001 to 100 mg m
−3
(Fig. 7). They are
from NOMAD (2350), SeaBASS (18 122), MERMAID (3711), ICES (5421), HOT
(702),
AMT (164), ARCSSPP (189), BARENTSSEA (7188), BATS (356), BIOCHEM
(4592), BODC (895), CALCOFI (4631), COASTCOLOUR (3322), CCELTER (254),
CIMT (204), ESTOC (100), GEPCO (56), PALMER (2865), SEADATANET (5403) and
TPSS (1000). The total number of chla_hplc observations
is 23 550, ranging from 0.002 to 99.8 mg m
−3
(Fig. 7), with
contributions from NOMAD (1309), SeaBASS (9478), MERMAID (707), ICES
(2994), HOT (193), GeP&CO (1536), BOUSSOLE (397), AMT (902), AWI (750),
BATS (334), BODC (735), COASTCOLOUR (848), IMOS (103), MAREDAT (1024),
PALMER (1077), TPSS (1002) and TARA (161). The combined chlorophyll dataset (all chlorophyll data considered, but for a given station, HPLC data
were selected if available) has a total of 79 731 observations, with 10 %, 49 % and 41 % respectively from oligotrophic (
<0.1
mg m
−3
), mesotrophic (0.1–1 mg m
−3
) and eutrophic (
>1
mg m
−3
) waters. When compared with the proportions of the world ocean
in these trophic classes, 56 % oligotrophic, 42 % mesotrophic and 2 %
eutrophic (Antoine et al., 1996), oligotrophic waters are underrepresented
relative to eutrophic waters in the compilation. The combined chlorophyll
dataset is unevenly distributed between each month of the year in both
the Northern and Southern Hemisphere, with higher coverage in summer months
(Fig. 3). There are fewer data in the Southern Hemisphere than in the
Northern Hemisphere (Fig. 3). The spatial distribution of the chlorophyll
values for the combined dataset (Fig. 8) shows a good agreement with known
biogeographical features, such as lower chlorophyll values in the
subtropical gyres and higher values in temperate, coastal and upwelling
regions. Many regions show a good spatial coverage (e.g. Atlantic and
Pacific Ocean), while others are less well sampled (e.g. Southern and Indian
Ocean). Of the contributing datasets, NOMAD and SeaBASS provide a good
spatial coverage in many regions (Fig. 9). Other datasets also provide
coverage from several locations across the globe (GEPCO, MAREDAT, TARA). The
ICES, MERMAID and BODC data are mainly located along the coastal regions of
Europe. The AMT and many AWI data mainly cover the central part of the
Atlantic Ocean, other AWI data cover the Atlantic sector and the Amundsen to
Bellingshausen Sea of the Southern Ocean and the western subtropical and
tropical Pacific. The SEADATANET, ARCSSPP and BARENTSSEA provide coverage for
the Arctic region and northern seas of the North Atlantic. The observations
from BIOCHEM and TPSS are mostly from the northwestern Atlantic, while
CALCOFI, CCELTER and CIMT provide data for the western coast of North America. The remaining
datasets provide observations for fixed locations: PALMER (western
Antarctic Peninsula), COASTCOLOUR (17 coastal sites across the world), BATS
(Bermuda, North Atlantic), BOUSSOLE (Mediterranean), HOT (Hawaii, North
Pacific), IMOS (coastal sites around Australia) and ESTOC (Canaries, North
Atlantic). Figure 9 shows all data sources that contribute with chlorophyll
observations, but many overlap each other, especially around Europe and
North America. For additional analysis and as an example of the applications
of the compiled dataset, the combined chlorophyll data
(chla_fluor and chla_hplc) were
partitioned into
boxes, and for each
box the number of observations, average value and standard deviation were
computed (Fig. 10a, b and c, respectively). The number of observations can
be very high (
>1000
) in some boxes along the European and North
American coastlines and relatively low (
<20
) in oceanic regions.
Again, there is evidence in the average value map (Fig. 10b) of well-known
biogeographical features, such as the lower chlorophyll in the subtropical
gyres and higher values in coastal and upwelling areas. There is a close
correspondence between the spatial patterns of the average and standard
deviation maps (Fig. 10b and c), which may be an indicator of the data
quality.
Coincident observations of chlorophyll
concentration and remote-sensing
reflectance are available at 3814 stations. These observations are mostly
from NOMAD (79 %), MERMAID (9 %), COASTCOLOUR (6%), and SeaBASS (5 %). The maximum of three selected band ratios of remote-sensing
reflectance is plotted against chlorophyll
concentration (Fig. 11). The
chla values used are the combined HPLC and fluorometric chlorophyll
and for the rrs, the closest spectral observation within 2 nm was used.
The maximum band ratios were calculated as the maximum of
[rrs(443)
rrs(555), rrs(490)
rrs(555), rrs(510)
rrs(555)] or
[rrs(443)
rrs(560), rrs(490)
rrs(560), rrs(510)
rrs(560)] if rrs(555) was
not available. The relationship between maximum band ratio and chlorophyll
is close to the NASA OC4 and OC4E v6 standard algorithm
) similarly
based on maximum band ratios, providing confidence in the quality of the
compiled data.
Figure 13
The distribution of absorption coefficients band ratios:
adg(443)
adg(490), adg(412)
adg(443), aph(490)
aph(443) and
aph(412)
aph(443). Data within 2 nm of the wavelengths were used. The
graphical convention is identical to Fig. 2. The vertical dashed lines show
the lower and upper thresholds used for quality control in the IOCCG report
5. The total number of points for adg ratios are divided between NOMAD
(89 %), COASTCOLOUR (7 %), MERMAID (3 %) and SeaBASS (1 %). The total number of points for aph ratios are divided between NOMAD
(36 %), TPSS (29 %), COASTCOLOUR (18 %), AWI (14 %), MERMAID (2 %) and SeaBASS (1 %).
Table 3
Summary of median values for aph, adg and bbp at 44
and 55
nm for each dataset (as shown in Fig. 12a–f). Data were first
searched for at 445 and 555 nm and then with a search window up to 8 nm to
include data at 547 nm.
Download Print Version
Download XLSX
Figure 14
Global distribution of observations of inherent optical
properties (algal pigment absorption coefficient aph, detrital plus CDOM
absorption coefficient adg, and particle backscattering coefficient
bbp) in the final table.
Figure 15
Global distribution of diffuse attenuation coefficient for
downward irradiance (kd) and total suspended matter (tsm) per dataset in the final table. The tsm and kd points from MERMAID overlap
each other in the western Black Sea (
∼ 40
N, 30
E) and the Arctic (
∼ 70
N, 120
W).
Figure 16
Examples of bio-optical relationships in the final merged table:
(a)
aph(443) versus chlorophyll
. The total number of points (2953) is divided between AWI (334), COASTCOLOUR (335), MERMAID (214), NOMAD (991), SeaBASS (124) and TPSS (955). For reference the solid line shows the regression from
Bricaud et al. (2004).
(b)
[aph(443)
adg(443)] versus rrs(443). The total number of points (1112) is divided between MERMAID (33) and NOMAD (1079).
(c)
[rrs(490)
rrs(555)] versus kd(490). The total number of points (2280)
is divided between MERMAID (62), NOMAD (2117) and SeaBASS (101). For
reference the solid line shows the NASA KD2S standard algorithm
, last access: 10 July 2019).
(d)
[rrs(490)
rrs(555)] versus bbp(555). The total number of points (365) is
divided between MERMAID (33), NOMAD (324), and COASTCOLOUR
SeaBASS (4). For
reference the solid line shows the relation proposed by Tiwari and Shanmugam (2013). A search window of 2 nm was used for panels
(a)
and
(b)
, and a search
window of 5 nm was used for panels
(c)
and
(d)
to include data at 560 nm when not available at 555 nm.
The inherent optical properties (aph, adg and bbp) are available
at 550 unique wavelengths between 300 and 850 nm. There is a total of 3293,
1654 and 792 observations, for aph, adg and bbp, respectively.
For aph the total number of observations is distributed among NOMAD
(1190), TPSS (966), COASTCOLOUR (593), AWI (458), SeaBASS (14) and MERMAID
(72). For adg the contributions are as follows: NOMAD (1079),
COASTCOLOUR (531), SeaBASS (11) and MERMAID (33). The bbp observations
come from NOMAD (371), COASTCOLOUR (154), SeaBASS (32) and MERMAID (235).
The data distribution of aph, adg and bbp at 44
nm and 55
nm for
each dataset is provided in Fig. 12a–f. Median values of aph,
adg and bbp at 44
and 55
nm for each dataset are summarized in
Table 3. For additional analysis, the following band ratios for the
absorption coefficients were calculated: aph(490)
aph(443),
aph(412)
aph(443), adg(443)
adg(490) and adg(412)
adg(443). Data within 2 nm
of the wavelengths were used to maximize the number of points. The
distribution of the ratios is shown in Fig. 13. Several observations were
found to be outside the thresholds used in the IOCCG report 5 (2006) for quality
control (see dotted vertical black lines in Fig. 13). These points are
highlighted here for information but retained in the database, as these
were mostly from NOMAD and there was an
interest to preserve this dataset
as a whole. Also, not discarding these data allows further manipulation with
different quality control criteria. On the annual scale, the observations of
the inherent optical properties are strongly underrepresented in the
Southern Hemisphere where there is a complete absence of data in several
months of the year (Fig. 3). Overall, the geographic coverage for
observations of aph, adg and bbp (Fig. 14) is poor, with most
open ocean regions not being sampled, except for the Atlantic Ocean. Small
clusters of data are located in particular coastal regions.
Finally, for the diffuse attenuation coefficient for downward irradiance
(kd) there are 25 unique wavelengths between 405 and 709 nm. There is a
total of 2454 observations from NOMAD (2266), SeaBASS (118) and MERMAID
(70). Data distribution of kd at 44
and 55
nm for each dataset is
shown in Fig. 12g and h. No kd data at these wavelengths were available
for the SeaBASS dataset (only at 490 nm). Median values of kd at 44
nm
span between 0.08 m
−1
(NOMAD) and 0.1 m
−1
(MERMAID), whereas at
55
nm the kd values are approximately 0.1 m
−1
(NOMAD and MERMAID).
NOMAD provides the best geographical coverage (Fig. 15), with a higher
coverage in the Atlantic, compared with other oceans. With the exception of
the coastal regions of North America and the Sea of Japan, most coastal regions
are not sampled. In the Northern Hemisphere, kd is distributed roughly
evenly across all months of the year, but in the Southern Hemisphere there
are few data points during the austral winter and none at all in September
(Fig. 3). For total suspended matter (tsm) there is a total of 1546
observations divided between COASTCOLOUR (1199) and MERMAID (347). The
observations of tsm are available in a greater number in the Northern
Hemisphere (Fig. 3) and are distributed across several coastal regions
around Europe, the Mediterranean Sea, the South China Sea, Indonesia and Australia (Fig. 15).
Although most of the stations with concurrent variables are from the NOMAD
dataset, for completeness, an examination of bio-optical relationships is
provided (Fig. 16). The relation between aph at 443 nm and chlorophyll
(Fig. 16 a) agrees with Bricaud et al. (2004). A total of 2953 points
exist with these two variables available (34 % from NOMAD, 32 % from
TPSS, 11 % from AWI, 11% from COASTCOLOUR, and the remaining 12 % from
MERMAID and SeaBASS). The relation between the sum of aph and adg at
443 nm and rrs at 443 nm (Fig. 16 b) shows a similar dispersion, with
the exception of some scattered points, to an equivalent analysis on the
IOCCG report 5 (see their Fig. 2.3). Again, the scattered data were retained
in the final table to preserve the NOMAD dataset. A total of 1112 points
exist for which these three variables are available (97 % from NOMAD).
The relation between the ratio rrs(490)
rrs(555) and kd(490) (Fig. 16c)
shows a good agreement with the NASA KD2S standard algorithm
). A total of
2280 points exist for which these three variables are available (93 %
from NOMAD). The relation between the ratio rrs(490)
rrs(555) and bbp at
555 nm (Fig. 16 c) shows a good agreement with the relation suggested by
Tiwari and Shanmugam (2013). A total of 365 points exist for which these
three variables are available (89 % from NOMAD).
Data availability
Information about the data availability can be found in Appendix B.
Conclusions
In this work, a compilation of bio-optical in situ data is presented,
resulting from the acquisition, homogenization and integration of several
sets of data obtained from different sources. The compiled data have a
global coverage and span the period from 1997 to 2018. Minimal changes were
made to the original data, other than the ones occurring from conversion to
standard format and quality control. In situ measurements of the following
variables were compiled: remote-sensing reflectance, chlorophyll
concentration, algal pigment absorption coefficient, detrital and coloured
dissolved organic matter absorption coefficient, particle backscattering
coefficient, diffuse attenuation coefficient for downward irradiance and
total suspended matter.
The final set of data consists of a substantial number of in situ
observations, available in a simple text table and processed in a way that
could be used directly for the evaluation of satellite-derived ocean-colour
data. The major advantages of this compilation are that it merges six
commonly used data sources in ocean-colour validation (MOBY, BOUSSOLE,
AERONET-OC, SeaBASS, NOMAD and MERMAID), four data sources developed for
ocean-colour applications (AWI, COASTCOLOUR, TPSS and TARA) and 17
additional sets of chlorophyll
concentration data (AMT, ICES,
HOT,GeP&CO, ARCSSPP, BARENTSSEA, BATS, BIOCHEM, BODC, CALCOFI, CCELTER,
CIMT, ESTOC, IMOS, MAREDAT, PALMER and SEADATANET) into a simple text table
free of duplicated observations. This compilation was initially created with
the intention of evaluating the quality of the satellite ocean-colour
products from the ESA OC-CCI project, but it can also be used for
other
purposes, including the validation of retrievals from recent space-borne
sensors such as Landsat 8 and Sentinel-2 and 3. It may also be useful in the
preparation of future sensors like NASA PACE. The objective of publishing
the compilation is to make it easily accessible to the broader community.
Note on former version
A former version of this article was published on 3 June 2016 and is available at
Appendix A:
Notation
ad
Detrital absorption coefficient (m
−1
adg
Detrital plus CDOM absorption coefficient (m
−1
AERONET-OC
AErosol RObotic NETwork-Ocean Color
ag
CDOM absorption coefficient (m
−1
AMT
Atlantic Meridional Transect
ap
Particle absorption coefficient (m
−1
aph
Algal pigment absorption coefficient (m
−1
ARCSSPP
Arctic System Science Primary Production
AWI
Data collection from Astrid Bracher
aw
Pure water absorption coefficient (m
−1
BARENTSSEA
Data collection from Knut Yngve Børsheim
BATS
Bermuda Atlantic Time-series Study
bb
Total backscattering coefficient (m
−1
bbp
Particle backscattering coefficient (m
−1
bbw
Backscattering coefficient of seawater (m
−1
BIOCHEM
The Fisheries and Oceans Canada database for biological and chemical data
BODC
British Oceanographic Data Centre
BOUSSOLE
Bouée pour l'acquisition d'une Série Optique à Long Terme
CALCOFI
California Cooperative Oceanic Fisheries Investigations
CCELTER
California Current Ecosystem Long Term Ecological Research
CDOM
Coloured Dissolved Organic Matter
chla
Chlorophyll
concentration (mg m
−3
chla_fluor
Chlorophyll
concentration determined from fluorometric or spectrophotometric methods (mg m
−3
chla_hplc
Total chlorophyll
concentration determined from the HPLC method (mg m
−3
CIMT
Center for Integrated Marine Technology
COASTCOLOUR
Compilation of data in several coastal sites
Es
Surface irradiance (or above- water downwelling irradiance) (mW cm
−2
−1
ESA
European Space Agency
ESTOC
Estación Europea de Series Temporales del Oceano
Fo
Top-of-the-atmosphere solar irradiance (mW cm
−2
−1
GeP&CO
Geochemistry, Phytoplankton, and Color of the Ocean
HOT
Hawaii Ocean Time-series
HPLC
High-performance liquid chromatography
ICES
International Council for the Exploration of the Sea
IMOS
Integrated Marine Observing System
kd
Diffuse attenuation coefficient for downward irradiance (m
−1
Lw
Water-leaving radiance (or above-water upwelling radiance) (mW cm
−2
−1
sr
−1
MAREDAT
Compilation of data in several coastal sites
MERIS
Medium Resolution Imaging Spectrometer
MERMAID
MERIS Match-up In situ Database
MOBY
Marine Optical Buoy
MODIS
Moderate Resolution Imaging Spectroradiometer
NASA
National Aeronautics and Space Administration
nLw
Normalized water-leaving radiance (mW cm
−2
−1
sr
−1
nLw_ex
nLw with a correction for bidirectional effects (mW cm
−2
−1
sr
−1
NOMAD
NASA bio-Optical Marine Algorithm Dataset
OC-CCI
Ocean Colour Climate Change Initiative
OLCI
Ocean and Land Colour Instrument
PALMER
Palmer station Long-Term Ecological Research
rrs
Remote-sensing reflectance (sr
−1
Rw
Irradiance reflectance (dimensionless)
SeaBASS
SeaWiFS Bio-optical Archive and Storage System
SEADATANET
Archive of in situ marine data
SeaWiFS
Sea-viewing Wide Field-of-view Sensor
TARA
Data collection from global transects
TPSS
Data collection from Trevor Platt and Shubha Sathyendranath
VIIRS
Visible Infrared Imaging Radiometer Suite
Appendix B:
Data availability
The compiled data are available at
(Valente et al., 2019). The database is composed of
three main tables: table insitudb_chla.csv with the
observations of chla_fluor and chla_hplc,
table insitudb_rrs.csv with observations of rrs and
table insitudb_iopskdtsm.csv with remaining observations
(aph, adg, bbp, kd and tsm). The rows within the three tables
relate to each other via a unique key (column idx). The three tables can
be viewed conceptually as one table with all data. To help with data
manipulation, six auxiliary tables derived from the previous three main
tables are provided. The table insitudb_metadata.csv
contains all available metadata and helps, for example, to find rows (i.e.
idx) with multiple variables (e.g. rrs and chla_fluor).
The table auxiliary_table_contributors.csv
contains the number of observations per data contributor, variable and
dataset. The remaining four tables (insitudb_rrs_satbands2.csv, insitudb_rrs_satbands6.csv, insitudb_iopskdtsm_satbands2.csv and insitudb_iopskdtsm_satbands6.csv) contain the spectral data of the
main tables (i.e. insitudb_rrs.csv and
insitudb_iopskdtsm.csv) aggregated within
±2
and
±6
nm, respectively, of SeaWiFS, MODIS AQUA, MERIS, VIIRS and OLCI
sensor bands. The tables are generated by assigning, in each row of the main
tables (i.e. insitudb_rrs.csv and insitudb_iopskdtsm.csv), the closest spectral observation within 2 nm (or 6 nm) of a
sensor band. The centre wavelengths of each band and sensor used in the
generation of the files are the following: SeaWiFS bands 1–8 were centred at
[412, 443, 490, 510, 555, 670, 765, 865] nm, respectively; MODIS-AQUA bands
1–9 were centred at [412, 443, 488, 531, 547, 667, 678, 748, 869] nm,
respectively; MERIS bands 1–13 were centred at [412, 442, 490, 510, 560,
620, 665, 681, 709, 753, 779, 865, 885] nm, respectively; VIIRS bands 1–5
were centred at [410, 443, 486, 551, 671] nm, respectively; OLCI bands 1–7
were centred at [412, 442, 490, 510, 560, 620, 665] nm. An exception to this
procedure was made to confirm that the correct MOBY data are stored in the
files (see Sect. 2.2.1. for discussion on how MOBY wavelengths are stored in
the main file). Finally, a readme file is provided to help the user.
Table B1 shows how the compiled data look. The example of a query for available chlorophyll data from subdataset
seabass_car81 is given.
Table B1
Example of how the compiled data look. The
result if the compilation is queried for the chlorophyll data from
subdataset seabass_car81 is shown.
Download Print Version
Download XLSX
Supplement
The supplement related to this article is available online at:
Author contributions
AV complied the database, carried out the integration and quality checking, and drafted the manuscript. The first six authors are part of the ESA OC-CCI
team and contributed to the design of the compilation and to the quality
checking, as well as contributing data. The remaining authors are listed
alphabetically and are data contributors (see their respective dataset in
Table 2) or individuals responsible for the development of a particular dataset (e.g. JW for NOMAD and KB for MERMAID). All data
contributors (listed in Table 2) were contacted for authorization of data
publishing and offered co-authorship. In the case of the ICES dataset the
permission for publishing was given by the ICES team. All the authors have
critically reviewed the manuscript. MW and TM passed away before submission. We regard their approval of this work as implicit.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This paper is a contribution to the ESA OC-CCI project. This work is also a
contribution to project PEst-OE/MAR/UI0199/2014. We would like to thank the
efforts of the teams responsible for collection of the data in the field and
of the teams responsible for processing and storing the data in archives,
without which this work would not be possible. We thank Tamoghna Acharyya
and Robert Brewin at Plymouth Marine Laboratory for their initial
contribution to this work. We thank the NOAA (US) for making available the
MOBY data and Yong Sung Kim for the help in questions about MOBY data.
BOUSSOLE is supported and funded by the European Space Agency (ESA), the
Centre National d'Etudes Spatiales (CNES), the Centre National de la
Recherche Scientifique (CNRS), the Institut National des Sciences de
l'Univers (INSU), the Sorbonne Université (SU) and the Institut de la
Mer de Villefranche (IMEV). We thank ACRI-ST, ARGANS and ESA for access to
the MERMAID database (
, last access: 10 July 2019). We thank Annelies Hommersom, Pierre Yves Deschamps, Gavin Tilstone and David Siegel for
allowing the use of MERMAID data for which they are principal investigators.
We thank the British Oceanographic Data Centre (BODC) for access to AMT data
and in particular to Polly Hadziabdic and Rob Thomas for their help in
questions about the AMT dataset. We thank Victoria Hill, Patrick Holligan,
Gerald Moore and Emilio Suarez for the use of AMT data for which they are
principal investigators. We thank Sam Ahmed, Hui Feng, Alex Gilerson and
Brent Holben for allowing the use of the AERONET-OC data for which they are
principal investigators. We thank also the AERONET staff and site support
people. The Australian Integrated Marine Observing System (IMOS) and CSIRO
are acknowledged for funding the Lucinda AERONET-OC site. We thank Bob Bidigare, Matthew Church, Ricardo Letelier and Jasmine Nahorniak for making
the HOT data available, as well as the National Science Foundation for support of
the HOT research (grant OCE 09-26766). We thank Yves Dandonneau for allowing
the use of GeP&CO data. We thank the ICES database on the marine environment
(Copenhagen, Denmark, 2014) for allowing the use of their archived data and
Marilynn Sørensen for the help with questions about the ICES dataset. We
thank all ICES contributors for their data. We thank Eric Zettler and Sea
Education Association. The CARIACO Ocean Time-Series Program also provided
significant decade-long bio-optical information used in this study. These
data were obtained from NOMAD and SeaBASS. We thank NASA, SeaBASS and the
Ocean Biology Processing Group (OBPG) for access to SeaBASS and NOMAD data.
We thank NASA for project funding for data collection. We thank Chris Proctor from SeaBASS for his valuable and prompt help in a variety of
questions. We are deeply thankful to the data contributors of NOMAD and
SeaBASS: Kevin Arrigo, Mike Behrenfeld, Emmanuel Boss, Chris Brown, Mary Luz Canon, Douglas Capone, Ken Carder, Alex Chekalyuk, Jay-Chung Chen, Dennis Clark, Jorge Corredor, Glenn Cota, Yves Dandonneau, Heidi Dierssen, David Eslinger, Piotr Flatau, Alex Gilerson, Joaquim Goes, Gwo-Ching Gong, Adriana Gonzalez-Silvera, Larry Harding, Jon Hare, Chuanmin Hu, Sung-Ho Kang, Gary Kirkpatrick, Oleg Kopelevich, Sam Laney, Pierre Larouche, Zhongping Lee,
Ricardo Letelier, Marlon Lewis, Steven Lohrenz, Antonio Mannino, John Marra,
Chuck McClain, Christophe Menkes, Mark Miller, Ru Morrison, James Mueller,
Ruben Negri, James Nelson, Norman Nelson, Mary Jane Perry, David Phinney,
John Porter, Collin Roesler, David Siegel, Mike Sieracki, Jeffrey Smart,
Raymond Smith, James Spinhirne, Dariusz Stramski, Rick Stumpf, Ajit Subramaniam, Chuck Trees, Ronald Zaneveld, Eric Zettler and Richard Zimmerman. For the BIOCHEM data we thank the Fisheries and Oceans Canada and
the following data contributors: Diane Archambault, Hughes Benoit, Esther Bonneau, Eugene Colbourne, Alain Gagne, Yves Gagnon, Tom Hurlbut, Catherine Johnson, Pierre Joly, Maurice Levasseur, Jean-Francois Lussier, Sonia Michaud, Patrick Ouellet, Jacques Plourde, Stephane Plourde, Luc Savoie,
Michael Scarratt, Philippe Schwab, Michel Starr and François Villeneuve.
We also thank Laure Devine for the help in processing the BIOCHEM dataset.
We thank Ralph Goericke for allowing the use of the CalCOFI and CCELTER
data. CalCOFI research is supported by contributions from the participating
agencies: the California State Department of Fish and Wildlife,
NOAA, National Marine Fisheries Service, Southwest Fisheries Science Center,
and the University of California, Integrative Oceanography Division at the
Scripps Institution of Oceanography, UCSD. The authors would like to thank
the Oceanic Platform of the Canary Islands (PLOCAN) and its staff for making
freely available the use of this ESTOC dataset. We thank the following
MAREDAT data providers: Robert Bidigare, Denise Cummings, Giacomo DiTullio,
Chris Gallienne, Ralf Goericke, Patrick Holligan, David Karl, Michael
Landry, Michael Lomas, Michael Lucas, Jean-Claude Marty, Walker Smith, Rick
Stumpf, Emilio Suarez, Koji Suzuki, Maria Vernet and Simon Wright. We thank
Oscar Schofield, Raymond Smith and Maria Vernet for allowing the use of the
PALMER data. Data from the Palmer LTER data repository were supported by
the Office of Polar Programs, NSF grants OPP-9011927, OPP-9632763 and
OPP-0217282. We thank the SeaDataNet Pan-European infrastructure for ocean
and marine data management (
, last access: 10 July 2019). We thank Emmanuel Boss for the TARA data. Funding for the collection and processing of the
TARA dataset was provided by the NASA Ocean Biology and Biogeochemistry
programme
under grants NNX11AQ14G, NNX09AU43G, NNX13AE58G and NNX15AC08G to the
University of Maine. Vanda Brotas received a sabbatical grant from FCT SFRH/BSAB/142981/201.
We would like to honour the memory of Marcel Wernand
and Tiffany Moisan, authors who contributed to the first version.
Financial support
This research has been supported by the ESA Climate Change Initiative – Ocean Colour project (ref: AO-1/6207/09/I-LG).
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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Articles
Abstract
Introduction
Data and methods
Results
Data availability
Conclusions
Note on former version
Appendix A:
Notation
Appendix B:
Data availability
Author contributions
Competing interests
Acknowledgements
Financial support
Review statement
References
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Short summary
A compiled set of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the compilation of global bio-optical in situ data (spanning from 1997 to 2018) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
A compiled set of in situ data is useful to evaluate the quality of ocean-colour satellite data...
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Sections
Abstract
Introduction
Data and methods
Results
Data availability
Conclusions
Note on former version
Appendix A:
Notation
Appendix B:
Data availability
Author contributions
Competing interests
Acknowledgements
Financial support
Review statement
References
Supplement