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Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

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Measuring the underwater light field is a key mission of the international Biogeochemical-Argo program. Since 2012, 0-250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm besides photosynthetically available radiation (PAR) have been acquired across the globe every 1 to 10 days. The resulting unprecedented amount of radiometric data has been previously quality-controlled for real-time distribution and ocean optics applications, yet some issues affecting the accuracy of measurements at depth have been identified such as changes in sensor dark responsiveness to ambient temperature, with time and according to the material used to build the instrument components. Here, we propose a quality-control procedure to solve these sensor issues to make Argo radiometry data available for delayed-mode distribution, with associated error estimation. The presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar parking depth and night-time profiles. A test on >10000 profiles from across the world revealed a quality-control success rate >90% for each band. The procedure shows similar performance in re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably during moonless nights and when the temperature range between the surface and 1000 dbar is the largest.
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sensors
Article
Correction of Biogeochemical-Argo Radiometry for Sensor
Temperature-Dependence and Drift: Protocols for a
Delayed-Mode Quality Control
Quentin Jutard 1, Emanuele Organelli 2, * , Nathan Briggs 3, Xiaogang Xing 4, Catherine Schmechtig 1,
Emmanuel Boss 5, Antoine Poteau 6, Edouard Leymarie 6, Marin Cornec 6, Fabrizio D’Ortenzio 6
and HervéClaustre 6


Citation: Jutard, Q.; Organelli, E.;
Briggs, N.; Xing, X.; Schmechtig, C.;
Boss, E.; Poteau, A.; Leymarie, E.;
Cornec, M.; D’Ortenzio, F.; et al.
Correction of Biogeochemical-Argo
Radiometry for Sensor
Temperature-Dependence and Drift:
Protocols for a Delayed-Mode Quality
Control. Sensors 2021,21, 6217.
https://doi.org/10.3390/s21186217
Academic Editor: Federico Angelini
Received: 30 June 2021
Accepted: 9 September 2021
Published: 16 September 2021
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Attribution (CC BY) license (https://
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4.0/).
1CNRS & Sorbonne Université, OSU Ecce Terra, 4 Place Jussieu, CEDEX 05, 75252 Paris, France;
quentin.jutard@sorbonne-universite.fr (Q.J.); catherine.schmechtig@imev-mer.fr (C.S.)
2National Research Council (CNR), Institute of Marine Sciences (ISMAR), Via del Fosso del Cavaliere 100,
00133 Rome, Italy
3National Oceanography Centre, Southampton SO14 3ZH, UK; nathan.briggs@noc.ac.uk
4State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography,
Ministry of Natural Resources, 36 Baochubei Road, Hangzhou 310012, China; xing@sio.org.cn
5School of Marine Sciences, University of Maine, Orono, ME 04469, USA; emmanuel.boss@maine.edu
6CNRS & Sorbonne Université, Laboratoire d’Océanographie de Villefranche, 06230 Villefranche sur mer,
France; antoine.poteau@imev-mer.fr (A.P.); edouard.leymarie@imev-mer.fr (E.L.);
marin.cornec@imev-mer.fr (M.C.); fabrizio.dortenzio@imev-mer.fr (F.D.); herve.claustre@imev-mer.fr (H.C.)
*Correspondence: emanuele.organelli@cnr.it; Tel.: +39-06-45488289
Abstract:
Measuring the underwater light field is a key mission of the international Biogeochemical-
Argo program. Since 2012, 0–250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm
besides photosynthetically available radiation (PAR) have been acquired across the globe every
1 to 10 days
. The resulting unprecedented amount of radiometric data has been previously quality-
controlled for real-time distribution and ocean optics applications, yet some issues affecting the
accuracy of measurements at depth have been identified such as changes in sensor dark responsive-
ness to ambient temperature, with time and according to the material used to build the instrument
components. Here, we propose a quality-control procedure to solve these sensor issues to make
Argo radiometry data available for delayed-mode distribution, with associated error estimation. The
presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar
parking depth and night-time profiles. A test on >10,000 profiles from across the world revealed
a quality-control success rate >90% for each band. The procedure shows similar performance in
re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future
deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably
during moonless nights and when the temperature range between the surface and 1000 dbar is
the largest.
Keywords: BGC-Argo; radiometry; quality control
1. Introduction
The international Biogeochemical-Argo (i.e., BGC-Argo) program has revolutionized
the way we acquire measurements of biogeochemically relevant variables in the open
ocean [
1
,
2
]. In 2016, the Biogeochemical-Argo planning group has defined six core variables
to accomplish the scientific and observational objectives of the program that include the
study of the ocean carbon uptake and acidification, oxygen minimum zones and nitrate
cycling, biological carbon pump, phytoplankton communities, and joint use with ocean
color satellite observations [
3
]. In particular, to study phytoplankton dynamics and combine
in-situ with remote sensing observations, radiometry, i.e., measurements of downwelling
Sensors 2021,21, 6217. https://doi.org/10.3390/s21186217 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 6217 2 of 19
irradiance (E
d
) and photosynthetically available radiation (PAR), has been selected as a
core variable.
Currently, the BGC-Argo program has accumulated more than 40,000 profiles of
downwelling irradiance (between 0 and 250 dbar), acquired by more than 100 floats in
the global ocean, across a variety of trophic and environmental conditions, and in remote
regions (https://biogeochemical-argo.org/, accessed on 13 September 2021). These profiles
have proved to be fruitful measurements for diverse applications. Downwelling irradiances
at various wavelengths have been implied in the analysis of the bio-optical behavior of the
global ocean [
4
] and the dynamics of dissolved organic matter [
5
,
6
], and for the validation
of space-based ocean color measurements and products [
7
13
]. Besides, E
d
and PAR have
been widely used to understand particulate organic carbon fluxes and export [
14
16
], to
study phytoplankton dynamics [
17
25
], and to improve numerical and radiative-transfer
models [26,27].
Despite the relevant scientific results, some inconsistencies in deep radiometric mea-
surements, where the lowest irradiances are expected, have been observed [
8
,
9
,
28
]. With
time and through the analysis of acquired data, our knowledge on the sensor behavior
has progressively improved and identified two main radiometer characteristics which
are independent one from the other, neglected since the launch of the fleet in 2012. First,
the dark measurements of the sensors are sensitive to the ambient temperature which
ultimately reduces measurement accuracy, especially in the deep part of the profile where
the remaining light is very low [
8
,
9
]. Such variance in the sensor responsivity with environ-
mental temperature is radiometer component- and wavelength-dependent [
29
]. Indeed,
we have observed that the sensor dark dependence on temperature is conditioned by
the material used to build the sensor container, i.e., aluminum or polyether-ether-ketone
(hereafter PEEK). Laboratory experiments have confirmed this temperature dependence
for radiometers to be deployed in Arctic waters [
30
] and showed differences between
those made in aluminum and PEEK across a wide range of ambient temperatures (see
Supplementary Materials Section S1). Second, the sensors’ dark measurements may drift
after several years of float operation. Radiometers mounted on Argo floats have not been
equipped with mechanical shutters that acquire along cast dark measurements during
daylight profiles, mainly due to relevant power consumption. We thus evolved the initially
established sampling protocol towards the acquisition of reference night profiles and dark
measurements at the 1000 dbar parking depth over the whole float lifetime in order to
characterize, quality-control and solve these sources of variability in the sensor response.
As for all Argo physical and biogeochemical variables, radiometry quality-control
(QC) must be provided in real-time (RT) and delayed-mode (DM). The RT-QC is mainly
devoted to operational oceanography (e.g., assimilation in forecast models of ocean state)
and consists in a number of automatic procedures that target the evaluation of a single
profile at a time and QC data distribution within 12 h from sampling. The DM-QC aims
to make data available within 12 months from the acquisition, after human control and
exploiting all measured profiles together [
31
]. The resulting DM-QC dataset is expected
to have the highest quality requested for scientific analysis and, ultimately, for climate
studies. The RT-QC procedure for radiometry, accepted by the Argo Data Management
Team, aims to check and flag measurements outside the range of expected values [
32
].
Alternatively, Organelli et al. [
28
] have proposed a near-real-time methodology detecting
environmental signals in radiometric profiles due to clouds and wave focusing near surface,
that is dedicated to bio-optical and remote-sensing applications (i.e., calm sea and uniform
sky conditions during the measurement [
33
]). No DM-QC for radiometric data, as well as
methods to characterize and solve sensor dark dependency on temperature and drift have
been implemented yet.
Here, we will exploit the global array of floats equipped with radiometers to develop
and assess a DM-QC procedure that aims to correct the effect of changes in environmental
temperature on BGC-Argo radiometric dark signals according to the material used to
Sensors 2021,21, 6217 3 of 19
build the instrument, and account for sensor dark drift with time (hereinafter referred to
as aging).
Following Equation (1) we convert digital counts (DC) to irradiance (units of
W m2nm1) and PAR (units of µmol photons m2s1) values:
Ed(λ)=Im(λ)a1(λ,Ts,t)(DC(λ)a0(λ,Ts,t)) (1)
this study will focus on the correction of the effects of the time, t, and of the sensor internal
temperature,
Ts
, on the
a0
calibration coefficient for each band of each sensor (i.e., the dark
signal), as the temperature dependency of the calibration coefficient
a1
has been found to
be negligible [
34
]. Im is the immersion coefficient, fixed for each band. We will discuss
procedure performance and show examples for a variety of trophic and illumination condi-
tions encountered across the global ocean. Finally, we will present advantages, limitations
and recommendations for the method. We anticipate the proposed methodology and the
recommended sampling protocol will open the door to the operational distribution of the
highest quality Argo radiometric profiles to the international oceanographic community.
All symbols and abbreviations used here are listed in the nomenclature list given below.
2. Materials and Methods
2.1. The Biogeochemical-Argo Database
Biogeochemical-Argo data used to develop and assess the DM-QC procedure for
radiometric profiles were acquired by 55 no longer profiling PROVOR-CTS4 floats, for a
total of 12,867 measured radiometry profiles. This fleet has operated since 2012 across a
variety of trophic environments and regional seas (Figure 1). All floats were configured
and deployed according to standard procedures [
35
]. The data were downloaded from the
Coriolis Global Data Assembly Center (GDAC) and stored in the Argo B and trajectory
files (ftp://ftp.ifremer.fr/ifremer/argo (accessed on 1 November 2020)).
Sensors 2021, 21, x FOR PEER REVIEW 3 of 21
temperature on BGC-Argo radiometric dark signals according to the material used to
build the instrument, and account for sensor dark drift with time (hereinafter referred to
as aging).
Following Equation (1) we convert digital counts (DC) to irradiance (units of W m−2
nm1) and PAR (units of µmol photons m2 s1) values:
𝐸𝑑(𝜆)= 𝐼𝑚(𝜆)∗ 𝑎1(𝜆, 𝑇𝑠, 𝑡)∗ (𝐷𝐶(𝜆)− 𝑎0(𝜆, 𝑇𝑠, 𝑡))
(1)
this study will focus on the correction of the effects of the time, t, and of the sensor internal
temperature, 𝑇𝑠, on the 𝑎0 calibration coefficient for each band of each sensor (i.e., the
dark signal), as the temperature dependency of the calibration coefficient 𝑎1 has been
found to be negligible [34]. Im is the immersion coefficient, fixed for each band. We will
discuss procedure performance and show examples for a variety of trophic and illumina-
tion conditions encountered across the global ocean. Finally, we will present advantages,
limitations and recommendations for the method. We anticipate the proposed methodol-
ogy and the recommended sampling protocol will open the door to the operational distri-
bution of the highest quality Argo radiometric profiles to the international oceanographic
community. All symbols and abbreviations used here are listed in the nomenclature list
given below.
2. Materials and Methods
2.1. The Biogeochemical-Argo Database
Biogeochemical-Argo data used to develop and assess the DM-QC procedure for ra-
diometric profiles were acquired by 55 no longer profiling PROVOR-CTS4 floats, for a
total of 12867 measured radiometry profiles. This fleet has operated since 2012 across a
variety of trophic environments and regional seas (Figure 1). All floats were configured
and deployed according to standard procedures [35]. The data were downloaded from
the Coriolis Global Data Assembly Center (GDAC) and stored in the Argo B and trajectory
files (ftp://ftp.ifremer.fr/ifremer/argo (accessed on 1 November 2020)).
Figure 1. Sampled stations by the 55 profiling BGC-Argo floats considered in this study.
Floats were programmed to drift at a parking depth of 1000 dbar and acquire vertical
profiles up to the sea surface every 1 to 10 days. Pressure and water temperature data
were collected every 2 s by a SBE-41 CP conductivity-temperature-depth sensor (Sea-Bird
Scientific, Bellevue, WA, USA), and quality-controlled according to standard, internation-
ally-accepted protocols [36]. Ed at three wavelengths (380, 412 and 490 nm) and PAR
Figure 1. Sampled stations by the 55 profiling BGC-Argo floats considered in this study.
Floats were programmed to drift at a parking depth of 1000 dbar and acquire ver-
tical profiles up to the sea surface every 1 to 10 days. Pressure and water temperature
data were collected every 2 s by a SBE-41 CP conductivity-temperature-depth sensor
Sensors 2021,21, 6217 4 of 19
(Sea-Bird Scientific, Bellevue, WA, USA), and quality-controlled according to standard,
internationally-accepted protocols [
36
].
Ed
at three wavelengths (380, 412 and 490 nm) and
PAR measurements were acquired by an OCR-504 radiometer (Sea-Bird Scientific), without
an internal temperature probe and configured with a different sensor for each channel [
37
].
Though all the floats were equipped with the same radiometer model, the thermodynamic
properties of five instruments made with aluminum (i.e., 693 profiles), deployed between
2014 and 2018, were different from those made with PEEK (see Supplementary Materials
Section S1).
Radiometric profiles were acquired in the upper 250 dbar, around local noon to reduce
the impact of low solar zenith angles [
33
]. To develop specific correction procedures for
the dark correction, which is known to be temperature-dependent [
38
,
39
], night profiles
(i.e., solar elevation < 5
) were acquired across a similar temperature range as day profiles
since 2014, but neither systematically nor homogeneously among all floats. Moreover,
radiometric measurements were also acquired daily during the float drift at the 1000 dbar
parking depth to evaluate any change in the instrument’s response with time. This was
implemented mid-2014 for all floats but those deployed in the Baffin Bay (Arctic Sea).
Hereafter, we will refer to radiometric data used to develop and assess the DM-QC control
such as: (i) day profiles (high light and high temperature variability); (ii) night profiles (no
or very dim light with high temperature variability); and (iii) drift measurements (no light
and low temperature variability).
In the following sections, we will show that both the acquisition of night profiles
and daily radiometric measurement at 1000 dbar represent key ancillary measurements
to correct the sensor’s dark signal and develop the most accurate DM-QC procedure.
However, since in the Coriolis GDAC there are additional 11350 profiles acquired by
76 no
longer profiling floats without sufficient ancillary night profiles or drift measurements
acquired for longer than 80% of the float lifetime (Table 1), we have developed specific
DM-QC procedures for those floats that are presented in the Supplementary Material
Section S2. Hence, the following sections will only focus on the best possible DM-QC
method that we recommend for future BGC-Argo radiometry deployments.
Table 1.
Availability of night profiles and daily drift measurements for the 55 and 76 BGC-Argo
floats.
OCR 504
Model
Drift Acquired for > 80%
of the Float Lifetime
Drift Acquired for 80%
of the Float Lifetime Total
Night
Profiles No Night Night
Profiles No Night
PEEK 50 10 32 17 109
Aluminum 5 1 9 7 22
All 55 11 41 24 131
2.2. Reconstruction of the Sensor Internal Temperature
The thermodynamics response of the sensor is not instantaneous (see Supplementary
Materials Section S1), thus the radiometer internal temperature must be reconstructed to
develop the DM-QC procedure. Following laboratory experiments (see Supplementary
Materials Section S1), the internal temperature
Ts
at which the sensor operates was modeled
using a delay first-order differential equation:
1
k
dTs
dt (t)=Tw(tt)Ts(t)(2)
where
Tw
is the temperature of the surrounding water; kand
tare empirically estimated
coefficients which represent the physical characteristics of the radiometer (Table 2).
Sensors 2021,21, 6217 5 of 19
Table 2.
Parameters used to reconstruct the sensor internal temperature T
s
according to the material
of the radiometer components.
OCR 504 Model k t
PEEK 0.2 min11 min
Aluminum 0.44 min10.25 min
To integrate Equation (2) along the entire profile, the following assumptions were made:
1) Ts
=
Tw
at the bottom of the profile. All floats spend at least one day at 1000 dbar
before profiling. Thus, when the float starts acquiring measurements, the sensor
temperature is at the equilibrium with the environment (1⁄k+t<< 1 day);
2)
The ascending speed of the float, c, is assumed to be constant, thus c= 0.1 dbar s
1
.
We analyzed 27,000 profiles from 165 PROVOR CTS-4 Argo floats, and found that 91%
of the profiles showed an average ascending speed ranging between 0.08 dbar s
1
and 0.12 dbar s
1
(Figure 2). A sensitivity test on correction of E
d
(490) for the float
WMO 6901654 revealed that, when using 0.08 and 0.12 dbar s
1
instead of
0.1 dbar
s
1
, the corrected E
d
(490) values change by at most 1.7
×
10
5
W m
2
nm
1
, with
95% of the measurement points vary by less than 5.3
×
10
6
W m
2
nm
1
. This
observed variability is consistent with the manufacturer-established sensor noise of
2.5 ×105W m2nm1[37].
Sensors 2021, 21, x FOR PEER REVIEW 5 of 21
PEEK
0.2 min1
Aluminum
0.44 min1
To integrate Equation (2) along the entire profile, the following assumptions were
made:
1) = at the bottom of the profile. All floats spend at least one day at 1000 dbar
before profiling. Thus, when the float starts acquiring measurements, the sensor tem-
perature is at the equilibrium with the environment (1⁄k + Δt << 1 day);
2) The ascending speed of the float, c, is assumed to be constant, thus c = 0.1 dbar s−1.
We analyzed 27000 profiles from 165 PROVOR CTS-4 Argo floats, and found that
91% of the profiles showed an average ascending speed ranging between 0.08 dbar
s−1 and 0.12 dbar s−1 (Figure 2). A sensitivity test on correction of Ed(490) for the float
WMO 6901654 revealed that, when using 0.08 and 0.12 dbar s−1 instead of 0.1 dbar s−1,
the corrected Ed(490) values change by at most 1.7 × 105 W m2 nm−1, with 95% of the
measurement points vary by less than 5.3 × 10−6 W m−2 nm1. This observed variability
is consistent with the manufacturer-established sensor noise of 2.5 × 10−5 W m−2 nm−1
[37].
Figure 2. Histogram of the average float ascent speed for 27000 BGC-Argo radiometry profiles, re-
constructed from the available time stamps in the trajectory profile. Vertical dashed lines indicate
the two values used for the sensitivity test which interval includes 91% of tested profiles.
We then introduce
which is delayed by Δt. This allows Equation (2) to be re-
written as an ordinary differential equation:

   
(3)
with:
   
(4)
Temperature is measured along a discrete axis of corresponding pressure measure-
ments. We numerically integrate Equation (3) along this discrete axis with index 0 corre-
sponding to the deepest (and first) measurement. We also introduce, i.e., the time at
which each measurement is taken, with = 0, and
which is the pressure measure-
ment associated to .
From Assumption 1 described above:
  
(5)
Equation (3) can be discretized as:
Figure 2.
Histogram of the average float ascent speed for 27000 BGC-Argo radiometry profiles,
reconstructed from the available time stamps in the trajectory profile. Vertical dashed lines indicate
the two values used for the sensitivity test which interval includes 91% of tested profiles.
We then introduce
T
s
which is
Ts
delayed by
t. This allows Equation (2) to be
rewritten as an ordinary differential equation:
1
k
dT
s
dt =TwT
s(3)
with:
T
s=Ts(t+t)(4)
Temperature is measured along a discrete axis of corresponding pressure measure-
ments. We numerically integrate Equation (3) along this discrete axis with index 0 corre-
sponding to the deepest (and first) measurement. We also introduce
tn
, i.e., the time at
which each measurement is taken, with
t0
= 0, and
Pwn
which is the pressure measurement
associated to Twn.
Sensors 2021,21, 6217 6 of 19
From Assumption 1 described above:
T
s0=Ts0=Tw0(5)
Equation (3) can be discretized as:
T
sn=T
sn1+k(tntn1)Twn1T
sn1(6)
Using Assumption 2, we can express:
tn=c1(Pw0Pwn)(7)
so that Equation (6) becomes:
T
sn=T
sn1+k
cPwn1PwnTwn1T
sn1(8)
Equation (8) can be computed to obtain
T
sn
for each
Pwn
value. The pressure axis
Psn
is then defined as:
Psn=Pwn+ct(9)
so that for each n,
Tsn
is equal to
T
sn
when
Tsn
values are associated to the pressure axis
Psn
.
The final step is to interpolate
Tsn
to retrieve
Ts
values that correspond to the pressure
axis of radiometric measurements.
To reconstruct the sensor internal temperature for radiometric measurements acquired
during the float drift at the 1000 dbar parking depth, the model described by
Equations (2)–(9)
could not be applied because of the low frequency of drift measurements and the inapplicability
of Assumption 2. In this case, because water temperature changes slowly during the drift
of the float, and the float spends at least one day at those given depth and temperature, the
closest (in time) water temperature measurement to the radiometry sampling was selected as the
corresponding Ts.
3. Protocols for the Correction of Aging and Temperature Dependence of the Dark Signal
3.1. Theoretical Framework
The measured irradiance
Edmeas
is described as a function of the real irradiance
Edreal
, the
sensor internal temperature Ts, the time t, and the sensor random normal noise ε:
Edmeas =FEdre al ,Ts,t+ε(10)
We assumed that:
Edmeas =h(Ts,t)Edreal +f(Ts)+g(t)+ε(11)
where his the slope error introduced by the temperature effects and aging, f(
Ts
) and g(t)
are the dark errors introduced by the sensor temperature and aging respectively, which are
assumed to be independent from one another.
For night profiles and drift measurements, the float is in the dark so that
Edreal
is
assumed equal to 0. Equation (11) is thus modified to:
For night profiles, Edmeas =0+f(Ts)+g(t)+ε(12)
For drift measurements, Edmeas =0+f(Tsconstant)+g(t)+ε(13)
In Equation (13), we indicate that the water temperature variations at the 1000 dbar
parking depth are relatively small, which means
Ts
can be considered as near constant. This
also means that drift measurements at 1000 dbar parking depth can be used to estimate
the sensor’s dark aging g(t) almost independently from changes in the environmental
temperature. This estimated g(t) is then needed in Equation (12) to estimate the sensor’s
Sensors 2021,21, 6217 7 of 19
dark temperature dependency f(Ts) using night profiles, which are acquired over a larger
range of temperatures than drift measurements. This is the rationale to estimate g(t) and
perform the correction for sensor dark aging before the estimation of f(
Ts
) and the correction
of the sensor temperature-dependence.
3.2. Overview of the Procedure
The overall quality-control procedure includes five consecutive steps, which will be
described in the following sections, and are the same both for
Ed
(
λ
) and PAR: (i) Visual
quality control; (ii) Correction of the sensor aging; (iii) Correction of the sensor temperature-
dependence; (iv) Error estimation; and (v) Assignment of quality flags.
The overview of the whole procedure to correct for aging and then temperature-
dependence of the dark sensor is shown in Figure 3. After the visual check, the workflow
starts with the computation of a multiple linear or linear-quadratic regression that must be
visually checked by the DM operator before applying the aging correction to all measured
profiles of a given float. We remind that, for BGC-Argo DM-QC, the operator must use
own scientific expertise and provide critical inputs to evaluate the correction results. If the
correction for the aging does not yield satisfactory results, the DM operator may move to
the following step. This is recommended for floats with short lifespan.
Sensors 2021, 21, x FOR PEER REVIEW 7 of 21
3.2. Overview of the Procedure
The overall quality-control procedure includes five consecutive steps, which will be
described in the following sections, and are the same both for Ed(λ) and PAR: (i) Visual
quality control; (ii) Correction of the sensor aging; (iii) Correction of the sensor tempera-
ture-dependence; (iv) Error estimation; and (v) Assignment of quality flags.
The overview of the whole procedure to correct for aging and then temperature-de-
pendence of the dark sensor is shown in Figure 3. After the visual check, the workflow
starts with the computation of a multiple linear or linear-quadratic regression that must
be visually checked by the DM operator before applying the aging correction to all meas-
ured profiles of a given float. We remind that, for BGC-Argo DM-QC, the operator must
use own scientific expertise and provide critical inputs to evaluate the correction results.
If the correction for the aging does not yield satisfactory results, the DM operator may
move to the following step. This is recommended for floats with short lifespan.
Figure 3. Flowchart of the QC procedure to correct radiometry for aging and temperature dependency.
Corrected profiles are then adjusted for the temperature-dependence by computing
linear regressions on night profiles. The linear regression must be visually checked by the
DM operator before applying the correction to all measured profiles of a given float. The
DM operator must thus evaluate that the temperature range covered by night profiles is
representative of the temperature variability encountered by the float over the whole life-
time, as well as the regression fit to the data. If the method does not yield satisfactory
results, the DM operator abandons the quality control of that float. An example of unsat-
isfactory linear regression is shown in Supplementary Material Section S3. If the correction
is successful the error associated to each measurement is estimated and quality flags are
assigned.
3.2.1. Visual Quality Control
According to the standard Argo procedures [36], the DM-QC includes a preliminary
visual check, profile by profile, made by the operator before the application of automatic
Figure 3. Flowchart of the QC procedure to correct radiometry for aging and temperature dependency.
Corrected profiles are then adjusted for the temperature-dependence by computing
linear regressions on night profiles. The linear regression must be visually checked by the
DM operator before applying the correction to all measured profiles of a given float. The
DM operator must thus evaluate that the temperature range covered by night profiles is
representative of the temperature variability encountered by the float over the whole lifetime,
as well as the regression fit to the data. If the method does not yield satisfactory results, the
DM operator abandons the quality control of that float. An example of unsatisfactory linear
regression is shown in Supplementary Material Section S3. If the correction is successful the
error associated to each measurement is estimated and quality flags are assigned.
Sensors 2021,21, 6217 8 of 19
3.2.1. Visual Quality Control
According to the standard Argo procedures [
36
], the DM-QC includes a preliminary
visual check, profile by profile, made by the operator before the application of automatic
correction routines. Thus, each data point within the profile is ultimately assigned one
of the standard Argo QC flags: “1” for good data, “2” for probably good data; “3” for
probably bad data; and “4” for bad data. Both flags 1 and 2 will be used to correct sensor’s
dark aging and temperature dependence as described here below.
Practically, the visual check starts from the evaluation of RT-QC radiometry data [
32
].
The DM operator first evaluates if RT-QC Flag “3” measurements must be confirmed as
bad or upgraded to “1” or “2”. Then, the operator visually detects any obvious outlier
along the profile which is not related to environmental signals due to clouds and wave
focusing/defocusing. The outliers are assigned to Flag “3” and “4” depending on the DM
operator’s confidence. Radiometric measurements flagged as “3” and “4” are not further
evaluated and are excluded from the following QC steps.
3.2.2. Correction of the Sensor Dark’s Aging
In the following section, the protocol to correct the sensor dark’s aging which is based
on the use of drift measurements is presented. Outliers are first removed from drift mea-
surements and are defined as any value falling outside of the range between [1st_quartile–
1.5*(3rd_quartile–1st_quartile)] and [3rd_quartile + 1.5*(3rd_quartile–1st_quartile)].
Following Equation (13), Edmea s is equal to 0 and Tsat 1000 dbar shows relatively low
variance. However, this small variance can still have a visible impact on the drift data
(Figure 4). Apart from deviations due to temperature, the sensor aging most often appears
as a linear function of time. Thus, g(t) is estimated by applying a multiple linear regression
model of Edmea s as a function of tand Ts:
E
dmeas =Ad +Bd Ts+Cd t(14)
Sensors 2021, 21, x FOR PEER REVIEW 8 of 21
correction routines. Thus, each data point within the profile is ultimately assigned one of
the standard Argo QC flags: “1” for good data, “2” for probably good data; “3” for prob-
ably bad data; and “4” for bad data. Both flags 1 and 2 will be used to correct sensor’s dark
aging and temperature dependence as described here below.
Practically, the visual check starts from the evaluation of RT-QC radiometry data [32].
The DM operator first evaluates if RT-QC Flag “3” measurements must be confirmed as
bad or upgraded to “1” or “2”. Then, the operator visually detects any obvious outlier
along the profile which is not related to environmental signals due to clouds and wave
focusing/defocusing. The outliers are assigned to Flag “3” and “4” depending on the DM
operator’s confidence. Radiometric measurements flagged as “3” and “4” are not further
evaluated and are excluded from the following QC steps.
3.2.2. Correction of the Sensor Dark’s Aging
In the following section, the protocol to correct the sensor dark’s aging which is based
on the use of drift measurements is presented. Outliers are first removed from drift meas-
urements and are defined as any value falling outside of the range between [1st_quartile
1.5*(3rd_quartile1st_quartile)] and [3rd_quartile + 1.5*(3rd_quartile1st_quartile)].
Following Equation (13),  is equal to 0 and at 1000 dbar shows relatively
low variance. However, this small variance can still have a visible impact on the drift data
(Figure 4). Apart from deviations due to temperature, the sensor aging most often appears
as a linear function of time. Thus, g(t) is estimated by applying a multiple linear regression
model of  as a function of t and :

     
(14)
Figure 4.
Radiometry drift measurements for E
d
(
λ
) and PAR as a function of time and temperature. Example is shown for
the float WMO6901584.
Sensors 2021,21, 6217 9 of 19
Subsequently, the DM operator must visually check the resulting fit from
Equation (14)
by estimating Edat a reference temperature which has been set to 5 C (Figure 5):
Ed5c=Edmeas Bd (Ts5)(15)
and:
E
d5C=Ad +5Bd +Cd t(16)
Sensors 2021, 21, x FOR PEER REVIEW 9 of 21
Figure 4. Radiometry drift measurements for Ed(λ) and PAR as a function of time and temperature. Example is shown for
the float WMO6901584.
Subsequently, the DM operator must visually check the resulting fit from Equation
(14) by estimating Ed at a reference temperature which has been set to 5 °C (Figure 5):
      
(15)
and:

     
(16)
Figure 5. Radiometry drift measurements for Ed(λ) and PAR as a function of time after estimation at a reference tempera-
ture of 5 °C . Solid line is the fit to all points. For this float, the fit is linear for all channels but Ed(412). Example is shown
for the float WMO6901584.
Because the aging may change sign and/or intensity over time (e.g., Ed(412) in Figure
4), the DM-QC operator may not be satisfied with the results of the linear fit in Equation
(14). In such a case, the operator may decide to fit  by a quadratic function versus t
and linear versus (Ed(412) in Figure 5):

        
(17)
so that:
Figure 5.
Radiometry drift measurements for E
d
(
λ
) and PAR as a function of time after estimation at a reference temperature
of 5
C. Solid line is the fit to all points. For this float, the fit is linear for all channels but E
d
(412). Example is shown for the
float WMO6901584.
Because the aging may change sign and/or intensity over time (e.g.,
Ed
(412) in
Figure 4
), the DM-QC operator may not be satisfied with the results of the linear fit in
Equation (14). In such a case, the operator may decide to fit Edmea s by a quadratic function
versus tand linear versus Ts(Ed(412) in Figure 5):
E
dmeas =Ad +Bd Ts+Cd t+Qd t2(17)
so that:
g(t)=Ad +Cd t+Qd t2(18)
where Qd is 0 when the linear regression in Equation (14) is applied. It should be noted
that Equation (18) includes the constant offset Ad from the bilinear regression. Ad is not
Sensors 2021,21, 6217 10 of 19
mathematically required to compute g(t) because another coefficient will be computed when
temperature correction is performed (see following sections). However, it is here included
in order to allow the DM operator to run the procedure using realistic radiometric values.
The multiple linear model described by Equation (14) is able to correct for the small
temperature variations found at the 1000 dbar parking depth. However, this temperature
correction cannot be applied to the whole profiles because they span a large range of
variability in temperature so that estimated coefficients from Equation (14) are not suitable.
In addition, the estimation at a reference temperature of 5
C allows the DM operator to
visualize and evaluate, float by float, the goodness of the aging’s correction procedure.
However, if the operator is still not satisfied with the proposed correction after visual check,
we suggest to proceed with the temperature-dependence correction anyway and test the
results. This is especially recommended for floats with a short lifespan.
3.2.3. Correction of the Sensor Dark’s Temperature Dependence
In this section, the protocol to correct the sensor dark’s dependence on temperature
which is based on the use of night profiles is presented. We recall that
Edreal
is assumed
equal to 0 along the whole night profile, which covers a large variability in water tempera-
ture. As a first step, all night profiles collected by a single float are corrected for the sensor
aging as described above. Ednig ht is then defined as:
Ednight =Edm eas g(t)=Edmeas Ad Cd tQd t2=f(Ts)+ε(19)
Then,
Ednight
is linearly fitted as a function of the reconstructed sensor internal tem-
perature Ts:
E
dnight =At +Bt Ts(20)
Figure 6shows an example of aging-corrected night profiles and regression analysis.
It is important to note that some night profiles might be influenced by the moon and star
light or acquired close to dawn and dusk. To remove such polluted data, the DM operator
may select a pressure threshold.
Subsequently, the offset to correct for sensor darks’ dependence on temperature is
expressed as:
f(Ts)=At +Bt Ts(21)
The final correction to be applied to all 0–250 dbar profiles is finally expressed as:
Edcorr =Edmeas f(Ts)g(t)(22)
Edcorr =Edmeas At Bt TsAd Cd tQd t2(23)
Edcorr =Edmeas ABTsCtQt2(24)(24)
where A = At + Ad,B = Bt,C = Cd, and Q = Qd. It must be noted that the corrected irradiance
Edcorr
is not equal to
Edreal
(Equation (11)) as only the temperature and aging effects on the
dark signal have been corrected. To equate
Edcorr
and
Edreal
,h(
Ts
,t) in Equation (11) must be
assumed equal to 1.
3.2.4. Error Estimation
Upon implementation of corrections presented above, the error associated with each
measured value (
σEd
) is estimated as the maximum value between the Noise Equivalent
Irradiance (NEI) (as provided by the manufacturer), and the relative error (ER) multiplied
by the corrected radiometry value Edcorr :
σEd=maxNEIEd;EREdEdcorr (25)
NE IEd
is the manufacturer’s NEI value of OCR-504 radiometers equal to 2.5
×
10
5
W
m
2
nm
1
for all
Ed
(
λ
) [
37
]. For PAR,
NEIEd
was estimated by computing the maximum
Sensors 2021,21, 6217 11 of 19
standard deviation observed for the dark values at the 1000 dbar parking depth corrected for
any aging among a total of 34 selected floats. The resulting
NEIEd
for PAR is equal to 0.03
µ
mol
photons m
2
s
1
. ER is 5% for PAR [
40
] and 2% for
Ed
(
λ
) following previous calibration error
estimations [41,42].
Sensors 2021, 21, x FOR PEER REVIEW 11 of 21
Figure 6. Radiometry night profiles of Ed(λ) and PAR as a function of sensor internal temperature . Dots are colored
according to pressure. Solid red line is the fit to all points, and is extrapolated to cover the entire range of temperature
encountered by the float during the whole lifetime. Prior to computing the linear regression, night profiles have been
corrected for any sensor aging. Example is shown for the float WMO6901584.
3.2.4. Error Estimation
Upon implementation of corrections presented above, the error associated with each
measured value () is estimated as the maximum value between the Noise Equivalent
Irradiance (NEI) (as provided by the manufacturer), and the relative error (ER) multiplied
by the corrected radiometry value :
  
(25)
 is the manufacturer’s NEI value of OCR-504 radiometers equal to 2.5 × 10−5 W
m² nm−1 for all Ed(λ) [37]. For PAR,  was estimated by computing the maximum
standard deviation observed for the dark values at the 1000 dbar parking depth corrected
for any aging among a total of 34 selected floats. The resulting  for PAR is equal to
0.03 µ mol photons m2 s1. ER is 5% for PAR [40] and 2% for Ed(λ) following previous
calibration error estimations [41,42].
3.2.5. Assignment of Quality Flags on Temperature Corrected Profiles
The DM-QC flags on sensor aging and temperature corrected profiles are assigned
according to the following procedure:
Recover the QC flags assigned with the visual QC. These profiles contain Flags “1”,
“2”, “3” and “4”;
Detect the dark values within corrected profiles applying successive Lilliefors tests
= 0.01; [28]), and assign Flag “2”;
Change radiometry flags “3” or “4” due to visual QC to “4”;
If pressure QC flag is “3” or “4”, radiometry flag is assigned as “4”;
Figure 6.
Radiometry night profiles of E
d
(
λ
) and PAR as a function of sensor internal temperature
Ts
. Dots are colored
according to pressure. Solid red line is the fit to all points, and is extrapolated to cover the entire range of temperature
encountered by the float during the whole lifetime. Prior to computing the linear regression, night profiles have been
corrected for any sensor aging. Example is shown for the float WMO6901584.
3.2.5. Assignment of Quality Flags on Temperature Corrected Profiles
The DM-QC flags on sensor aging and temperature corrected profiles are assigned
according to the following procedure:
Recover the QC flags assigned with the visual QC. These profiles contain Flags “1”,
“2”, “3” and “4”;
Detect the dark values within corrected profiles applying successive Lilliefors tests
(α= 0.01; ref. [28]), and assign Flag “2”;
Change radiometry flags “3” or “4” due to visual QC to “4”;
If pressure QC flag is “3” or “4”, radiometry flag is assigned as “4”;
If Tscannot be reconstructed, the radiometry flag is assigned as “4”.
Sensors 2021,21, 6217 12 of 19
4. Performance of the DM-QC Procedure
The DM-QC procedure described above to correct for sensor dark changes with time
and varying environmental temperature was tested over a total of 55 BGC-Argo profiling
floats with ancillary night profiles and drift measurements acquired over more than 80% of
the float lifetime. All these floats, operating across the globe, were equipped with OCR-504
radiometers and acquired 0–250 dbar E
d
profiles at 380, 412 and 490 nm in addition to PAR.
In Figure 7, we show examples of vertical profiles before and after correction for
sensor’s dark aging and temperature dependence. The magnitude of the correction applied
as represented by the A, B, and C parameters obtained through Equation (24), and its
variability over the ensemble of floats whose sensor aging was corrected linearly are shown
for each band in Figure S9 (Supplementary Materials Section S4). The distributions of the A,
B, and C parameters were generally normal and, the impact of temperature on the sensor’s
dark signal showed to be larger than the one due to the sensor’s aging.
Examples of corrected profiles (Figure 7) encompass a variety of oceanic environments
with diverse optical, trophic and biogeochemical conditions [
4
,
20
,
43
,
44
], thus showing
applicability of the procedure at the global scale. In particular, the steps we set up for the
DM-QC BGC-Argo radiometry (Figure 3) provide adjustments of specific features that
characterize the profiles (Figure 7). First of all, all non-zero dark measurements at depth are
shifted to zero or re-qualified as very low irradiance measurements that, otherwise, would
have been disregarded. Indeed, the DM-QC procedure makes vertical profiles usable at
greater depths so that biogeochemical, modelling, and optical applications can be enhanced.
This is particularly relevant for permanently oligotrophic clear waters (e.g., mid-ocean
gyres; Figure 7d) where sunlight around local noon can penetrate deeper than 250 dbar [
8
],
or in productive high-latitude seas during wintertime where the underwater light field can
expand down to 150 dbar (Figure 7j) and contribute to phytoplankton blooms [18].
Contrarily, in the upper part of the profile where irradiance values are the highest
and aging and temperature issues are expected to have a negligible impact [
8
,
28
], the
developed correction protocols do not determine significant changes in the measured
values (Figure 7). In addition, the developed QC procedure does not affect the signature
of the environmental signals such as those due to clouds and wave focusing/defocusing
(Figure 7a). Such characteristics reinforce previously published scientific studies restricted
to the first optical depth or the mixed layer [
4
,
6
,
20
], and joint applications with remote
sensing observations [
8
,
11
]. Yet a newly generated radiometric database enhanced with
sensor dark’s aging and temperature-dependence corrections will surely open to the
possibility of re-analysis studies.
However, the applied DM procedure correctly resolves artificial features such as steps
in the profiles due to a significant increase of the dark counts which respond to the sudden
changes in water temperature (Figure 7g–i). The developed protocols remove these features
and shift to zero dark values at depth, so that the resulting radiometric profiles show the
monotonic decrease with depth as expected.
The DM-QC procedure we developed has been implemented over a total of 12,867 measured
profiles each band. The procedure returned profiles that monotonically decreased as expected
from theory and reached greater depths (Figure 7). A total of 11,824 profiles (from 47 floats), i.e.,
about 92% of the tested database for bands at 412 and 490 nm, and PAR was corrected (Figure 8).
In the case of
Ed
(380), correction was successful for 11,597 profiles (from 46 floats), i.e., 90% of the
tested database. In particular, the DM successfully corrected profiles derived from 45 floats made
with PEEK components (44 floats for
Ed
(380)), and two floats with aluminum components. The
uncorrected 227
Ed
(380) profiles (all from one float) were corrected with alternative procedures
(see Supplementary Materials Section S2). The developed QC procedure demonstrated high and
similar performances for all radiometric channels. This suggests strong potential to implement
these DM-QC protocols to other wavelengths and, ultimately to hyperspectral radiometers.
Sensors 2021,21, 6217 13 of 19
Sensors 2021, 21, x FOR PEER REVIEW 13 of 21
Figure 7. Examples of radiometry profiles before and after DM-QC: Left) profiles are shown in a
semi-log scale; Centre) profiles are shown in a linear scale; Right) the reconstructed sensor internal
temperature 𝑇𝑠 is shown (Equations (2)(9)). Examples derive from four BGC-Argo floats deployed
in oceanic regions characterized by diverse trophic and optical regimes: (ac) Southern Ocean; (d
f) South Pacific subtropical gyre; (gi) Mediterranean Sea; (jl) North Atlantic subpolar gyre
Irminger Sea.
Examples of corrected profiles (Figure 7) encompass a variety of oceanic environ-
ments with diverse optical, trophic and biogeochemical conditions [4,20,43,44], thus
showing applicability of the procedure at the global scale. In particular, the steps we set
up for the DM-QC BGC-Argo radiometry (Figure 3) provide adjustments of specific fea-
tures that characterize the profiles (Figure 7). First of all, all non-zero dark measurements
at depth are shifted to zero or re-qualified as very low irradiance measurements that,
Figure 7.
Examples of radiometry profiles before and after DM-QC: Left) profiles are shown in a semi-log scale; Centre)
profiles are shown in a linear scale; Right) the reconstructed sensor internal temperature
Ts
is shown (Equations (2)–(9)).
Examples derive from four BGC-Argo floats deployed in oceanic regions characterized by diverse trophic and optical
regimes: (
a
c
) Southern Ocean;
(df) South
Pacific subtropical gyre; (
g
i
) Mediterranean Sea; (
j
l
) North Atlantic subpolar
gyre—Irminger Sea.
Sensors 2021,21, 6217 14 of 19
Sensors 2021, 21, x FOR PEER REVIEW 15 of 21
Figure 8. Radiometry profiles acquired by the 55 BGC-Argo floats with ancillary night profiles and drift measurements.
Green dots: successfully corrected profiles with the DM-QC procedure; Orange dots: uncorrected profiles; Yellow dots:
profiles corrected with alternative methods (see Supplementary Materials Section S2).
Overall, the DM-QC procedure to correct the sensor dark signal systematically suc-
ceeded for all tested floats with at least four night profiles collected over the float lifetime
(Figure 9).
Figure 9. Number of floats with dark measurements successfully corrected for the four radiometric
channels as a function of available night profiles.
Nevertheless, the majority of floats had three or fewer associated night profiles over
their lifetime, and the correction we implemented was still successful in most of those
cases. As the average lifespan of a float is expected to be four years [3], our results thus
implies that each float equipped with radiometers must acquire one night profile per year,
Figure 8.
Radiometry profiles acquired by the 55 BGC-Argo floats with ancillary night profiles and drift measurements.
Green dots: successfully corrected profiles with the DM-QC procedure; Orange dots: uncorrected profiles; Yellow dots:
profiles corrected with alternative methods (see Supplementary Materials Section S2).
Regarding the remaining uncorrected 8 floats and 1043 radiometric profiles: 582
profiles from three floats (i.e., about 5% of the tested database) were corrected with al-
ternative procedures specifically developed for the array of 76 floats with an insufficient
number of night profiles or drift measurements (Supplementary Materials Section S2),
while
461 profiles
from five floats (i.e., about 4% of the tested database) were not corrected.
Correction was made with alternative procedures when the ancillary data (most often
night profiles) were not good enough to confidently apply the procedure described here,
correction was abandoned when the alternative methods also failed.
Overall, the DM-QC procedure to correct the sensor dark signal systematically suc-
ceeded for all tested floats with at least four night profiles collected over the float lifetime
(Figure 9).
Sensors 2021, 21, x FOR PEER REVIEW 15 of 21
Figure 8. Radiometry profiles acquired by the 55 BGC-Argo floats with ancillary night profiles and drift measurements.
Green dots: successfully corrected profiles with the DM-QC procedure; Orange dots: uncorrected profiles; Yellow dots:
profiles corrected with alternative methods (see Supplementary Materials Section S2).
Overall, the DM-QC procedure to correct the sensor dark signal systematically suc-
ceeded for all tested floats with at least four night profiles collected over the float lifetime
(Figure 9).
Figure 9. Number of floats with dark measurements successfully corrected for the four radiometric
channels as a function of available night profiles.
Nevertheless, the majority of floats had three or fewer associated night profiles over
their lifetime, and the correction we implemented was still successful in most of those
cases. As the average lifespan of a float is expected to be four years [3], our results thus
implies that each float equipped with radiometers must acquire one night profile per year,
Figure 9.
Number of floats with dark measurements successfully corrected for the four radiometric channels as a function
of available night profiles.
Sensors 2021,21, 6217 15 of 19
Nevertheless, the majority of floats had three or fewer associated night profiles over
their lifetime, and the correction we implemented was still successful in most of those
cases. As the average lifespan of a float is expected to be four years [
3
], our results thus
implies that each float equipped with radiometers must acquire one night profile per year,
preferably during moonless nights and when the temperature range between the surface
and 1000 dbar parking depth is the largest.
5. Discussion and Conclusions
To quality-control the large amount of radiometric profiles acquired by BGC-Argo
floats, real-time [
32
] and near real-time quality-control procedures [
28
] have been proposed.
While the method proposed by Poteau et al. [
32
] was mainly verifying the range of mea-
sured values, Organelli et al. [
28
] proposed protocols for the qualification of radiometric
profiles to specifically use in ocean optics science and remote sensing applications
e.g., for
the derivation of the diffuse attenuation coefficient K
d
which is a key quantity for bio-
optical and biogeochemical studies [
4
]. With this aim, their method was not focusing on the
issues addressed here (i.e., sensor’s dark dependence on temperature and aging) but rather
on how the environment (presence of clouds, wave focusing at the surface) drives depar-
tures of the profile with respect to an expected monotonic decrease of irradiance with depth.
Moreover, the scientific exploitation of the quality controlled radiometric profiles according
to Organelli et al. [
28
] was restricted to the upper layer (i.e., first penetration depth [
45
]),
mainly because some inconsistencies likely due to sensor dark’s temperature-dependence
issues were noticed in the deepest part.
The method proposed here offers a pragmatic way to identify and correct BGC-
Argo radiometric profiles for sensor dark’s aging and temperature-dependence issues, by
acquiring one night profile per year and daily dark measurements at the 1000 dbar parking
depth. These new protocols will allow to extend the range of exploitable measurements
and, ultimately, enhance their use among the international biogeochemical community.
Yet, we also recommend a technological upgrade of radiometers installed on floats with a
probe to directly monitoring the internal temperature at which the sensor operates, which
has only been modelled so far.
We must notice that the joint use of the DM-QC method here proposed with the one
presented by Organelli et al. [
28
] represents an opportunity to generate a unique high-
quality and interoperable radiometric dataset free of clouds and wave focusing/defocusing.
Given the potential for the BGC-Argo network to expand [
2
,
46
], it can be expected that
the resulting dataset, potentially increasing in near real-time, would allow addressing or
readdressing key topics of applications in ocean optics the investigation of which was up
to now suffering from limited data availability. The quality of the data could be further
enhanced when also the impact of instrument tilt on measured values as well as the effect
of bio-fouling that can occur [8] will be taken into account.
Among these ocean optics science topics, the understanding of regional and seasonal
variability of K
d
with a higher degree of confidence must be refined along the water
column [
4
]. Additionally, comparing such in-situ BGC-Argo float products with their
satellite counterparts would allow the identification of the locations where bio-optical
anomalies or nuances exist. This would represent a preliminary step to understand the
causes of discrepancies and, as a consequence, possibly refine the retrieval algorithms for
satellite products in some areas.
The possible derivation of radiometry with depth over the whole vertical dimension
is expected to provide high resolution K
d
profiles that will be useful to address the link
between surface remotely-sensed properties and their vertical variability according to
region and season. Such data could in turn allow to re-evaluate and possibly improve
methods developed to retrieve the vertical profile of chlorophyll-a from simultaneous
measurement of chlorophyll fluorescence and radiometry from floats, methods that were
initially developed on a very small float dataset [47].
Sensors 2021,21, 6217 16 of 19
The improved accuracy of radiometric measurements with depth will also enhance
their use across the biogeochemical and ecosystem model community. An improved accu-
racy is expected to support studies that assimilate irradiance data to model phytoplankton
photosynthesis [
26
], especially at the most elevated depths where the deep chlorophyll
maxima are observed and supported by small quantities of light.
When considering the DM corrected profiles over the whole tested database, the
method we presented showed high and similar applicability for the three channels of
downwelling irradiance as well as for PAR, thus suggesting potential applicability to
hyperspectral radiometers. With the advent of future hyperspectral satellite missions [
48
],
there is an increasing interest in in-situ hyperspectral optics. Profiling floats equipped with
hyperspectral radiometers represent an especially cost-effective approach to evaluate satel-
lite performances during the post-launching so-called commissioning phases (few months).
Such technology would indeed allow the acquisition of numerous calibration/validation
high-quality matchups in a limited period of time, provided that a significant fleet of
dedicated floats [
49
] would be deployed in diverse environments with specific bio-optical
status and atmospheric specificities. Additionally, hyperspectral measurements could
possibly become a component of the standard BGC-Argo fleet offering the possibility to
refine the detection and quantification of optically significant substances (phytoplankton
communities, detritus, mineral substance, colored dissolved organic matter).
Finally, it should be noticed that with the increasing development of robotic observa-
tion systems, a fleet of sensors can now be deployed and operated globally which definitely
will change our way to look at data and qualify them. Working with a dense dataset ac-
quired from multiple-a priori identical and interoperable-instruments will indeed allow us
to identify sensor issues that would be difficult to discover on a case-by-case analysis [
43
].
In this respect the BGC-Argo network represents a unique platform to help in improving
sensor performances for the benefit of other observation systems.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/10
.3390/s21186217/s1.
Author Contributions:
Conceptualization, Q.J., E.O. and H.C.; methodology, Q.J., E.O., N.B., X.X.,
C.S., E.B., A.P., E.L. and M.C.; software, Q.J.; validation, Q.J., E.O. and H.C.; formal analysis, Q.J.;
investigation, Q.J. and E.O. and H.C.; resources, C.S. and A.P.; data curation, Q.J., C.S. and A.P.;
writing—original draft preparation, Q.J., E.O. and H.C.; writing—review and editing, all; visualiza-
tion, Q.J.; supervision, E.O., C.S. and H.C.; project administration, F.D. and H.C.; funding acquisition,
F.D. and H.C. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by EUROPEAN RESEARCH COUNCIL, grant numbers 246777
(remOcean project) and 834177 (REFINE project); EUROPEAN UNION’S HORIZON 2020 research
and innovation program, grant numbers 2014-633211 (AtlantOS project) and 824131 (EA-RISE project);
AGENCE NATIONALE DE LA RECHERCHE in the framework of the French “Equipement d’avenir”
program, grant number ANR J11R107-F (NAOS project); BNP PARIBAS FOUNDATION (SOCLIM
project); CNES-TOSCA and LEFE-GMMC (BGC-Argo France program); SECOND INSTITUTE
OF OCEANOGRAPHY, MNR, grant numbers QNYC1702 and 14283. The APC was funded by
EUROPEAN RESEARCH COUNCIL through the REFINE project (grant number 834177).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data used in this study are openly available at https://doi.org/10
.17882/42182 (accessed on 13 September 2021).
Acknowledgments:
The technical staff at the French GDAC (Coriolis) is acknowledged for their
invaluable support.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Sensors 2021,21, 6217 17 of 19
Computer Code and Software:
The computed code to implement the protocols presented in this
study is openly available at https://github.com/qjutard/radiometry_QC (version 1.03, accessed on
13 September 2021).
Nomenclature
Symbol Definition
EdDownwelling irradiance
PAR Photosynthetically Available Radiation
Im Immersion coefficient
a0;a1Calibration coefficients
DC Dark counts
tTime
TsSensor internal temperature
kRate of change of the sensor temperature
TwWater temperature
tResponse delay of the sensor temperature to the water temperature
cAscending speed of floats (assumed constant)
T
sSensor temperature delayed by t
TsnDiscretized sensor temperature
TwnWater temperature measurements, sorted from the deepest to the shallowest
T
snDiscretized delayed sensor temperature, follows the water temperature
measurements axis
tnDiscretized time corresponding to water temperature measurements
PwnPressure measurements associated to water temperature measurements
PsnPressure axis associated to Tsn
Edmeas Measured irradiance
Edreal Real irradiance that would be obtained with a perfect sensor
hSlope error introduced by the temperature and aging effects
εSensor noise
f(Ts)Error offset caused by the sensor temperature being different from calibration
g(t)Error offset caused by sensor aging over time
E
dmeas Measured irradiance, fitted to Tsand t
Ad,Bd,Cd,Qd Coefficients in the fit of drift measurements to Tsand t
Ed5CMeasured irradiance in drift, projected on the Ts=5C plane along the E
dmeas fit
E
d5CE
dmeas projected on the Ts=5C plane along the E
dmeas fit
Ednight Irradiance measurements in night profiles, corrected for sensor aging
E
dnight Edni ght , fitted to Ts
At Coefficients in the fit of night measurements to Ts
Bt
Edcorr Irradiance corrected for the effects of temperature and aging on the dark signal
A,B,C,QCoefficients in the full expression of the irradiance correction
σEdError associated to Ed
NE IEdNoise Equivalent Irradiance
EREdRelative Error
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