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Improving Satellite Global Chlorophyll aData Products
Through Algorithm Refinement and Data Recovery
Chuanmin Hu
1
, Lian Feng
1
, Zhongping Lee
2
, Bryan A. Franz
3
, Sean W. Bailey
3
,
P. Jeremy Werdell
3
, and Christopher W. Proctor
3,4
1
College of Marine Science, University of South Florida, St. Petersburg, FL, USA,
2
School for the Environment, University
of Massachusetts Boston, Boston, MA, USA,
3
NASA GSFC, Greenbelt, MD, USA,
4
Science Systems and Applications, Inc.,
Lanham, MD, USA
Abstract A recently developed algorithm to estimate surface ocean chlorophyll aconcentrations
(Chl in mg m
−3
), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National
Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl
maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters
and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is
revisited for its algorithm coefficients and for its algorithm transition between color index and OCx
using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest
that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for
Chl < 0.05 mg m
−3
, but smoother algorithm transition for Chl between 0.25 and 0.40 mg m
−3
. Evaluation
using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and
cross‐sensor consistency between SeaWiFS, MODISA, and VIIRS over the OCx algorithms for oligotrophic
oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans
reduced from ~10% for OCx to 1–2% for OCI2. More importantly, data statistics suggest that the current
straylight masking scheme used to generate global Chl maps can be relaxed from 7 × 5 to 3 × 3 pixels without
losing data quality in either Chl or spectral remote sensing reflectance (R
rs
(λ), sr
−1
) for not just
oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average
relative increase of 39% in data quantity for global oceans, thus making it possible to reduce data product
uncertainties and fill data gaps.
Plain Language Summary There are generally two issues with any remote sensing data product:
data quality (accuracy) and data quantity (coverage). In this work, these two issues for global ocean data
products of chlorophyll aconcentrations (Chl in mg m
−3
) are investigated through revisiting a recently
developed algorithm concept and statistical analyses of cloud‐adjacent data. The use of more data in
algorithm development leads to slightly different algorithm coefficients and smoother transition between
clear and turbid waters, and a new straylight masking scheme is proposed to “recover”some of the
previously masked data in the global data products. The new algorithm leads to significantly improved
cross‐sensor consistency (SeaWiFS, MODIS, VIIRS) as compared to the traditional band‐ratio algorithms,
with mean monthly difference reduced from 10% to 1–2%. The new straylight masking scheme leads to
relative increase of 39% in data quantity in the global ocean without losing data quality. These improvements
are expected to reduce uncertainties and fill gaps in the global data products.
1. Introduction
One of the most historically meaningful and utilized data products from ocean color satellites is surface con-
centration of chlorophyll a(Chl in mg m
−3
) because this photosynthetic pigment plays a fundamental role in
affecting ocean biology and ecology. To many researchers, Chl and “ocean color”have become synonymous,
as Chl (plus accessary phaeopigment) has been the main data product since the proof‐of‐concept Coastal
Zone Color Scanner (1978–1986) era. Indeed, all satellite ocean color missions have Chl as one of their main
data products, for example, the SeaWiFS mission (Sea‐viewing Wide Field‐of‐view Sensor; 1997–2010),
MODIS (Moderate Resolution Imaging Spectroradiometer; 1999 to present on Terra and 2002 to present
on Aqua), MERIS (Medium Resolution Spectroradiometer; 2002–2012), and the most recent VIIRS
(Visible Infrared Imaging Radiometer Suite; 2012 to present).
©2019. American Geophysical Union.
All Rights Reserved.
RESEARCH ARTICLE
10.1029/2019JC014941
Key Points:
•OCI chlorophyll aalgorithm
revisited for its algorithm
coefficients and transition
•New quality‐control scheme
proposed to mask straylight
•Both data quality and data quantity
improved for global oceans
Correspondence to:
C. Hu,
huc@usf.edu
Citation:
Hu, C., Feng, L., Lee, Z., Franz, B. A.,
Bailey, S. W., Werdell, P. J., & Proctor,
C. W. (2019). Improving satellite global
chlorophyll adata products through
algorithm refinement and data
recovery. Journal of Geophysical
Research: Oceans,124, 1524–1543.
https://doi.org/10.1029/2019JC014941
Received 3 JAN 2019
Accepted 9 FEB 2019
Accepted article online 14 FEB 2019
Published online 7 MAR 2019
HU ET AL. 1524
Deriving accurate Chl data products from measurements several hundreds of kilometers above the Earth
requires a thorough understanding of the entire system of ocean color measurements from the satellite to
the sea surface. This includes the development and implementation of algorithms for global routine proces-
sing such as on‐orbit temporal stability corrections, vicarious calibration, atmospheric correction (including
whitecap and Sun glint corrections), and bio‐optical algorithm development. All steps except the last one are
designed to obtain accurate spectral remote sensing reflectance in every spectral band (R
rs
(λ), sr
−1
), and the
last one is to estimate Chl from R
rs
(λ). Descriptions of all steps above are beyond the scope of this paper as
they can be found in the literature (e.g., SeaWiFS technical report series; Franz et al., 2007; Frouin et al.,
1996; Gordon, 1997; Gordon & Wang, 1994; McClain, 2009; McClain et al., 2004; Moore et al., 2015;
Stumpf et al., 2003; Wang & Bailey, 2001; Wang & Shi, 2007). Instead, the focus of this paper is on a Chl algo-
rithm optimized for global oceans (i.e., data quality) and also on data quantity through quality control.
A review of Chl algorithms for global oceans has been provided in recent papers including Dierssen (2010) and
Hu and Campbell (2014). Briefly, there are two general approaches to estimate Chl from satellite‐derived
R
rs
(λ). The first is empirical, through regression of R
rs
(λ) band ratios or band differences against Chl (e.g.,
Hu et al., 2012; Kahru & Mitchell, 1999; O'Reilly et al., 2000). The second is semianalytical, through solving
Chl‐R
rs
(λ) equations established from simplifications of radiative transfer theory using certain bio‐optical
assumptions (e.g., Carder et al., 1999; IOCCG, 2006; Lee et al., 2002; Maritorena et al., 2002; Sathyendranath
et al., 1989). While in principle the latter should lead to more accurate results than the former because water
constituents affecting R
rs
(λ) are explicitly separated, in practice both approaches have their own strengths and
weaknesses. For example, the latter relies on tuning of the assignment of assumptions in the bio‐optical rela-
tionships using global or local data sets. The former accounts for R
rs
(λ) input errors through tuning of the
empirical coefficients, although the empirical design makes it impossible to differentiate explicitly the various
in‐water constituents. On the other hand, empirical approaches through either band ratio or band difference
can partially compensate some of the R
rs
(λ) input errors because these errors to a large extent are spectrally
related (Hu et al., 2013). Thus, following the tradition from the CZCS era, even though empirical Chl estimates
are known to contain large uncertainties (Dierssen, 2010) and such uncertainties may vary across different
ocean basins (Szeto et al., 2011), the U.S. National Aeronautics and Space Administration (NASA) still uses
empirical algorithms to produce default (also called “standard”) Chl products from the mainstream sensors.
Recently, NASA adapted a new Chl algorithm proposed by Hu et al. (2012) as the default global algorithm in
their reprocessing 2014.0, with new processing codes updated in the software package SeaDAS (version 7.2).
The new algorithm is a hybrid of a novel concept of three‐band difference color index (CI) and the tradition-
ally band ratio algorithm (OC4 or OC3, termed as OCx in this paper; O'Reilly et al., 2000) to apply to low‐Chl
waters (Chl ≤0.25 mg m
−3
) and higher‐Chl waters (Chl > 0.3 mg m
−3
), respectively. For intermediate‐Chl
waters in the transition zone of 0.25–0.3 mg m
−3
, a weighted mixture of the two algorithms is used.
Collectively, the algorithm is termed as Ocean Color Index (OCI) algorithm. Following this change, after
independent evaluation of the new algorithm, the U.S. National Oceanic and Atmospheric
Administration (NOAA) also made similar changes in their processing code to apply the new algorithm
approach (after further tuning of algorithm coefficients) for VIIRS global processing (Wang & Son, 2016).
This is the first time since the CZCS era that the algorithm concept has been changed in global data proces-
sing from band ratio to band difference for low‐Chl waters. Along with extensive evaluations by the commu-
nity (e.g., Brewin et al., 2013, 2015) and by NASA, the reason behind these changes is mainly because of the
tolerance of the CI design to known and unknown errors from atmospheric correction and other sources;
such a tolerance leads to reductions in many image artifacts. For example, in addition to the documented
error tolerance in Hu et al. (2012), NASA found that the CI algorithm was also tolerant to an unknown error
of digital count round off in one of the atmospheric correction bands due to sensor degradation (Franz,
2014). Indeed, the CI algorithm led to (1) more spatially coherent and temporally consistent Chl patterns,
(2) better Chl retrievals than OCx as gauged by field validations, and (3) more consistent cross‐sensor time
series for low‐Chl waters. Consequently, NASA has applied the OCI algorithm as the default Chl algorithm
for all ocean color sensors currently being used at NASA, including CZCS, SeaWiFS, MODIS, MERIS, VIIRS,
OCTS (Ocean Color and Temperature Sensor; 1996–1997), and GOCI (Geostationary Ocean Color Imager;
2011 to present). This led to a more consistent multisensor time series as shown in the overlapping periods
for more than one sensor (Franz, 2014; Franz et al., 2016). Furthermore, inspired by the algorithm design,
similar band‐difference approaches have been developed to estimate surface concentrations of particulate
10.1029/2019JC014941
Journal of Geophysical Research: Oceans
HU ET AL. 1525
inorganic carbon (Mitchell et al., 2017) and particulate organic carbon (Le et al., 2018), both showing
improved performance over the original algorithms.
However, from a practical point of view, several weaknesses still exist in the NASA default global Chl data
products. Some of these originate from the algorithm itself while others are due to data processing quality
control when generating global products. Specifically,
1. The original CI algorithm was tuned using high‐performance liquid chromatography (HPLC) Chl data
only. As a result, there are much fewer data points in the CI algorithm training than in the original
OCx where both HPLC and fluorometric Chl data were used. The difference between CI algorithm tun-
ing using different data sets needs to be evaluated;
2. During evaluation of the OCI algorithm using global data it was found that the threshold of 0.25 mg m
−3
originally proposed by Hu et al. (2012) to transition between CI and OCx could lead to some discontinuity
in global data histogram distributions for intermediate Chl (0.25–0.3 mg m
−3
). Therefore, the OCI imple-
mentation lowered this threshold to 0.175 mg m
−3
. Consequently, the full advantage of the CI design was
compromised because for Chl between 0.175 and 0.25 mg m
−3
the band‐ratio OCx algorithm still played a
role but OCx is less tolerant than CI to input errors. Because 15% of the global oceans have Chl between
0.175 and 0.25 mg m
−3
according to satellite data statistics, it is desirable to extend CI to its original upper
bound of 0.25 mg m
−3
to take its full advantage;
3. A major advantage of remote sensing is large data quantity (i.e., frequent and synoptic measurements).
Current quality control criteria within data processing, however, sometimes make valid retrievals diffi-
cult even under cloud‐free conditions when bright‐target adjacent stray light, Sun glint, large solar or
viewing angle, or other nonoptimal observing conditions exist (Feng & Hu, 2016a). Because the CI algo-
rithm has been shown to be tolerant to many residual correction errors in the algorithm inputs, it might
be possible to relax some of the quality control flags to “recover”some of the previously masked data,
leading to more valid retrievals without diminishing data quality. Indeed, the problem of no valid retrie-
vals under cloud‐free conditions not only reduces coverage, increases uncertainties (due to fewer data
points used in calculating the mean), but also presents an obstacle for real‐time guidance of ship surveys
or monitoring of blooms. Therefore, this problem is briefly reviewed below.
Cloud statistics from MODIS measurements showed that, on average, global ocean cloud fraction is between
70% and 75% (King et al., 2013). MODISA has near‐daily coverage over subtropical and tropical oceans and
daily or more frequent coverage over high‐latitude regions. Assuming daily coverage, on average at a given
location there should be one cloud‐free measurement every three to four days (i.e., about 25–30% chance).
However, after rigorous quality control with the various quality flags applied, global statistics indicated that
valid retrievals of Chl were only 5% (Feng & Hu, 2016a), meaning that for a random 1‐km ocean location
(before spatial or temporal data binning) there was only one valid retrieval every 20 days. In contrast, valid
retrievals for sea surface temperature from the same MODIS measurements nearly doubled to 10%. Clearly,
cloud is not the only major factor affecting the quantity of valid retrievals. Others factors such as Sun glint,
cloud‐adjacent stray light, large solar and/or view angles, and swath width also play important roles. Of
these, the straylight flag appeared to be one of the dominant factors.
Stray light near bright targets, such as clouds, is caused by light scattered by the target entering the field of
view of the satellite instrument through the sensor's point spread function (Meister & McClain, 2010). In
practice, the MODIS straylight flag was defined as a 7 × 5 pixel dilation from any cloud pixel, where 7 is
in the cross‐track direction and 5 is in the along‐track direction (Franz et al., 2005). In other words, a
cloud‐free pixel within 3 pixels in cross‐track direction or 2 pixels in along‐track direction from a nearby
cloud pixel was considered as being contaminated by clouds and therefore discarded in global data compo-
sites. MODIS statistics near clouds indicated that this was perhaps too conservative in masking low‐quality
cloud‐free data, leading to a significant loss of data quantity. Feng and Hu (2016b) showed that although
MODIS total radiance even 10 pixels away from clouds may still have significant stray light effect (i.e.,
>1% in total radiance), some of these effects were mitigated through atmospheric correction because the
atmospheric correction bands also suffered from the same adjacency effects with the same sign, leading to
only 1–2 pixels adjacent to clouds being contaminated by stray light in the retrieved R
rs
(λ) values. Based
on these results, Feng and Hu (2016b) argued that the 7 × 5 straylight flag might be relaxed to increase data
quantity without reducing data quality. However, to what extent the flag can be relaxed is unknown. A thor-
ough evaluation using field data and more statistics is required.
10.1029/2019JC014941
Journal of Geophysical Research: Oceans
HU ET AL. 1526
Therefore, given the potential weakness in the OCI algorithm and a desire to increase data quantity, the
objective of this paper is to revisit the OCI algorithm design with its parameterization as well as to improve
both data quality and data quantity for global oceans.
2. Data and Methods
As in Hu et al. (2012), the focus is on a unified algorithm that can be applied to all ocean color sensors rather
than on sensor‐specific tuning of parameterization to account for cross‐sensor difference in sensor band set-
tings. Such a difference can be corrected through converting the sensor‐specificR
rs
(λ) to sensor‐independent
R
rs
(λ) for common, unified wavelengths using hyperspectral R
rs
(λ) data collected from in situ measure-
ments. For this reason, the three wavelengths used in this work are 443, 555, and 670 nm as a heritage
of SeaWiFS.
In situ R
rs
(λ) and Chl data from the NASA bio‐Optical Marine Algorithm Data set (NOMAD) version 2 were
used to refine the Chl algorithm (Werdell & Bailey, 2005). R
rs
(λ) and Chl data were collected by many
research groups around the world, thus forming data pairs for algorithm development. To increase data
volume, Chl data determined using both HPLC and fluorometric methods were used here. If both measure-
ments were conducted at a station, HPLC Chl was used. Several additional quality‐control criteria were
applied to the NOMAD data set: R
rs
(λ) > 0.0 sr
−1
, Chl > 0.0 mg m
−3
, and latitude between 60°N and
60°S. A total of 2,306 data pairs were selected.
CI for each data pair was calculated using R
rs,443
,R
rs,555
, and R
rs,670
as in Hu et al. (2012):
CI ¼Rrs;555−Rrs;443 þ555−443ðÞ=670−443ðÞxR
rs;670−Rrs;443
(1)
In traditional empirical algorithm design, blue/green band ratios have been used to estimate Chl because for
most oceanic waters, R
rs
in blue bands decreases with increasing Chl but R
rs
in green bands tends to be rela-
tively stable. Here the maximum ratio, R, between four wavelengths of SeaWiFS was estimated as (O'Reilly
et al., 2000)
R¼max Rrs;443;Rrs;490;Rrs;510
=Rrs;555 (2)
In the above equation, the maximum of R
rs
is selected from three blue bands (443, 490, and 510 nm) to
improve signal‐to‐noise when the water type changes from clear to more turbid.
Similarly, after the launch of MODIS and VIIRS sensors the maximum ratio Rbetween three wavelengths
was also estimated and used in the same fashion in the algorithm development.
Following the NASA approach to derive the OCx algorithm coefficients, Chl data were first gridded into
logarithmic space, with the corresponding mean CI and mean Rcalculated for each grid as described below.
Then, for consistency with the NASA approach, such gridded data were gridded again according to Rin log
space, with the corresponding mean Chl and mean CI calculated and used in the regression to derive the CI
algorithm coefficients using the following algorithm formulation:
Log10 ChlðÞ¼aCI þb(3)
where a and b are the CI algorithm coefficients. In practice, the incremental steps in the gridded Chl space
were calculated as
L;Chl;i¼0:01x1:01 ;H;Chl;i¼0:01xiþ1ðÞ
1:01;i¼1−N(4)
where L
,Chl,i
and H
,Chl,i
are the lower and upper bounds for the i
th
Chl bin. The form in equation (4) is to
account for lognormal Chl data distributions (Campbell, 1995). For example, the range of the 200th Chl a
bin is 0.0732–0.0739 (i.e., 0.01 × 200
1.01
–0.01 × 201
1.01
). For all data points whose Chl values fall within a
grid, their mean Chl, mean CI, and mean Rvalues were calculated (denoted as Chl′,CI′, and R′, respec-
tively), resulting in 927 valid bins. These bins were then gridded into the Rspace again, where the incremen-
tal steps in the gridded R space were calculated as
10.1029/2019JC014941
Journal of Geophysical Research: Oceans
HU ET AL. 1527
L;R;i¼0:05xi
1:01;H;R;i¼0:05 xiþ1ðÞ
1:01;i¼1−N(5)
where L
,R,i
and H
,R,i
are the lower and upper bounds for the i
th
R bin. The
form in equation (5) is to account for the fact that Ris not used linearly in
the OCx algorithm design, but log
10
(R) is used (equation (6)). For all 927
points whose R′values fall within a grid, the mean Chl and mean CI were
calculated (denoted as Chl″and CI″, respectively), resulting in 245 final
bins.
Of the 245 pairs of Chl″and CI″, only 116 pairs with CI″< 0.0001 (corre-
sponding to Chl″~0.4 mg m
−3
) were selected to determine the algorithm
coefficients using equation (3) (Figure 1).
The CI algorithm is only applicable to low‐Chl waters. For higher‐Chl
waters, the original OCx algorithm is used. For SeaWiFS, the four‐band
algorithm is specified as (O'Reilly et al., 2000)
ChlOC4¼10y
y¼a0þa1χþa2χ2þa3χ3þa4χ4;χ¼log10 RðÞ (6)
where a
0
–a
4
are the algorithm coefficients determined through nonlinear
regression using gridded Chl and Rdata as described above. The current
algorithm coefficients used operationally by NASA (version 6) are 0.3272, −2.9940, 2.7218, −1.2259, and
−0.5683, respectively. The algorithm forms and algorithm coefficients for MODIS and VIIRS are determined
similarly. All algorithm forms and coefficients can be found from the NASA algorithm webpage (https://
oceancolor.gsfc.nasa.gov/atbd/chlor_a/).
Because the CI was designed for low‐Chl waters only, the global OCI algorithm is a hybrid between CI and
OCx, which is formulated as
ChlOCI ¼ChlCI for ChlCI ≤ChlLmg m−3
ChlOCX for ChlCI >ChlHmg m−3
½
αxChlOCX þβx ChlCI for ChlL<ChlCI ≤ChlHmg m−3
½
(7)
where the weighting factors are α= (Chl
CI
−Chl
L
)/(Chl
H
−Chl
L
), β= (Chl
H
−Chl
CI
)/(Chl
H
−Chl
L
). Chl
L
and Chl
H
define the lower and upper bounds of the algorithm transition zone. In Hu et al. (2012), they were
0.25 and 0.30 mg m
−3
, respectively. In the NASA operational processing the lower bound was adjusted to
0.175 mg m
−3
. In this study they are determined to be 0.25 and 0.40 mg m
−3
to make the CI design applicable
for a higher range while maintaining a smooth transition between the CI and OCx algorithms (see below).
On average, SeaWiFS monthly data between 1998 and 2010 showed that 77.8 ± 1.0% of the global ocean had
Chl ≤0.25 mg m
−3
and 5.1 ± 0.4% of the global ocean had Chl between 0.25 and 0.3 mg m
−3
.
Because there are several algorithms used in this paper, for clarity the terminology is defined in Table 1.
Algorithm evaluation was performed using several ways. First, in situ data archived in the NASA SeaWiFS
Bio‐optical Archive and Storage System (SeaBASS; Werdell et al., 2003) were used to evaluate the Chl data
products after applications of the Chl algorithms. The following search criteria, most of which recommended
Figure 1. Scatterplot to show the CI2 algorithm. For comparison, the origi-
nal CI algorithm (CI1) is also presented. The red circle highlights the dif-
ference between CI2 and CI1 for low‐Chl waters.
Table 1
Algorithm Terminology Used in This Paper
Algorithm Name Meaning
CI1 Original CI algorithm with transition zone of 0.25–0.30 mg m
−3
(Hu et al., 2012)
CI1′Same as CI1, but the transition zone is adjusted to 0.25–0.40 mg
−3
CI2 Adjusted algorithm with new parameterization, with transition zone of 0.25–0.40 mg m
−3
OCI1 Original OCI algorithm as a hybrid between CI1 and OCx
OCI1′Adjusted algorithm as a hybrid between CI1′and OCx
OCI2 Adjusted algorithm as a hybrid between CI2 and OCx
10.1029/2019JC014941
Journal of Geophysical Research: Oceans
HU ET AL. 1528
by NASA, were used to find the satellite‐in situ matching pairs: bathymetry >30 m, sensor zenith angle <56°,
solar zenith angle <70°, <3‐hr time difference between in situ and satellite measurements, median value of
coefficients of variation (calculated as standard deviation divided by mean) of several products (R
rs
between
412 and 555 nm, aerosol optical thickness at 865 nm) <15% for the 5 × 5‐pixel window centered at the in situ
station, and difference between simulated and measured surface irradiance <100%; >50% pixels in the 5 × 5
box must be valid (i.e., not associated with any of the standard quality‐control flags such as straylight and
high glint; Bailey & Werdell, 2006). A total of 1,424 matching pairs were selected for SeaWiFS between
1998 and 2010, and 330 were obtained for MODIS Aqua between 2002 and 2010. These are basically the same
data sets as used in Hu et al. (2012).
Second, in addition to the approach above for product evaluation, additional effort was used to find in situ
data collected under MODIS straylight mask. To achieve this, all concurrent and collocated MODIS and in
situ matching pairs were downloaded from NASA without applying any criteria above except that the time
window was relaxed from ±3 to ±4 hr. The same was performed to find the corresponding SeaWiFS files. A
total of 3,728 MODIS level‐2files and 5,763 SeaWiFS MLAC level‐2files were downloaded for this purpose.
Then, local processing (instead of using the SeaBASS search engine) was used to apply all but the straylight
flag to determine which in situ data points were associated MODIS straylight flag. Unfortunately, there were
only <5 points qualified for such criteria, making it impossible to evaluate MODIS retrievals under the 7 × 5
straylight flag using in situ measurements. Thus, statistical analysis using satellite data alone is the only
approach to determine whether these 7 × 5 flagged pixels are statistically different from the adjacent,
non‐flagged pixels.
3. Results
3.1. Refined OCI Algorithm (OCI2) and Its Field Validation
Figure 1 shows the new CI algorithm (CI2) based on all qualified NOMAD Chl data and R
rs
(λ) data after
gridding in log space. For comparison, the original CI algorithm (hereafter CI1; Hu et al., 2012) is also
shown. Although they are similar between Chl of 0.1–0.2 mg m
−3
, there are noticeable differences for
Chl < 0.05 mg m
−3
(red circle) and for Chl > 0.3 mg m
−3
. The differences are due to their different inputs
of Chl and R
rs
(λ) data because CI1 used the limited HPLC data only while CI2 used all data including
HPLC and fluorometric Chl. From a pure statistical point of view, with the limited data, however, it is diffi-
cult to conclude which one is superior. This is especially true for Chl < 0.05 mg m
−3
where data spread
around the fitting line is significantly higher than for other Chl values. Also note that for the same CI values
(i.e., same input R
rs
(λ) data), Chl
CI2
is lower than Chl
CI1
for Chl < 0.05 mg m
−3
. This will have significant
impact on studies of ocean gyres where Chl is extremely low.
The OCI2 algorithm was applied to both SeaWiFS and MODISA data where concurrent field‐measured Chl
data were available through NASA's SeaBASS archive. Such estimated Chl
OCI2
were compared with field‐
measured Chl in Figure 2 using the search criteria described above. For comparison, Chl data derived from
SeaWiFS and MODISA using the OCx algorithm are also presented in Figure 2, while statistical measures of
the algorithm performances for low‐Chl waters are all listed in Table 2.
The evaluation results show that compared with the OCx algorithm, OCI2 performance is significantly bet-
ter in nearly all statistical measures for low‐Chl waters. Hu et al. (2012) showed that even if the OCx algo-
rithm coefficients were retuned using low Chl data (<0.4 mg m
−3
) only, the CI algorithm design still led
to improved algorithm performance than OCx because of the former's tolerance to input R
rs
(λ) errors (admit-
tedly that all in situ R
rs
(λ) data still contain a few percent errors or uncertainties even after rigorous quality
control). Compared to OCI1, OCI2 performance measures are mixed when these discrete in situ measure-
ments were used to evaluate product uncertainties, but they are generally comparable to those of OCI1.
One obvious and also previously known finding is the significant data spread for almost the entire data
range, highlighting the need for improved data quality in field measurements. Most importantly, data are
extremely scarce from ocean gyres where Chl is low. For MODISA, there is not a single point with in situ
Chl < 0.03 mg m
−3
. For SeaWiFS, there is not a single point with in situ Chl < 0.02 mg m
−3
, and data for
Chl < 0.1 mg m
−3
appear to be more spread than in other ranges. Clearly, the puzzle of which one is closer
10.1029/2019JC014941
Journal of Geophysical Research: Oceans
HU ET AL. 1529
to the “truth”for low‐Chl waters, Chl
CI1
or Chl
CI2
in Figure 1, cannot be resolved from evaluations using the
current in situ data sets. Other measures, as shown below, may be used to determine which one is better.
3.2. OCI2 Performance by Other Measures
Although scatterplots have been widely used for algorithm evaluation, they only provide one side of the story
because (1) field data are always limited, and they may not cover the full range under all possible measure-
ment scenarios, and (2) more importantly, potential artifacts in image quality and cross‐sensor inconsistency
cannot be revealed by scatterplots. Therefore, data statistics, image quality, and cross‐sensor consistency are
examined in order to further evaluate the OCI2 performance.
The first check is on Chl continuity in the transition zone, which is 0.25–0.3 mg m
−3
for OCI1 but 0.25–0.4
for OCI2. For an apple‐to‐apple comparison, an adjusted OCI1 (i.e., OCI1′in Table 1) with the same transi-
tion zone of 0.25–0.4 mg m
−3
was tested as well. This is because if a smoother transition zone is achieved it is
easier to verify whether it is due to algorithm change from CI1 to CI2 or due to transition zone change from
0.25–0.3 to 0.25–0.4 mg m
−3
. Here the transition thresholds of 0.25 and 0.4 mg m
−3
correspond to the clear‐
water definition given by Gordon and Clark (1981) and the upper limit of the CI algorithm applicability
(Figure 1), respectively. The comparison between these three schemes is shown in Figure 3. The discontinu-
ity in OCI1 largely disappeared in OCI1′and OCI2, with the latter two showing slightly different histogram
distributions due to their different algorithm coefficients (Figure 1). Overall, OCI2 appears to have signifi-
cantly reduced the discontinuity in Chl distribution across the transition zone for all three sensors examined.
Next, the three OCI algorithms are compared against the default OCx algorithm for the same images as used
in Figure 3, with results shown in Figure 4. Compared to OCI1, both OCI1′(black dots) and OCI2 (red dots)
Figure 2. Evaluation of the OCI2 algorithm with SeaBASS data. For comparison, performances of the OCx algorithms are
also presented. The inset figures show the locations of the satellite‐in situ matching pairs. The statistical measures for low‐
Chl waters can be found in Table 2.
Table 2
Performance of the Updated Chl Algorithm (CI2) for Both MODISA and SeaWiFS for Chl < 0.25 mg m
−3
, as Gauged by
Field‐Measured Chl From NASA's SeaBASS Data Archive
RMSE URMSE Mean Ratio Median Ratio MRE R
2
R
2
(Log) N
MODISA OCx 77.7% 44.2% 1.24 1.05 32.0% 0.42 0.66 63
CI1 43.9% 32.7% 1.15 1.04 25.4% 0.62 0.71 63
CI2 51.2% 37.6% 1.12 0.94 35.2% 0.59 0.71 63
SeaWiFS OCx 535.8% 54.2% 1.79 1.19 41.5% 0.01 0.33 357
CI1 91.8% 47.2% 1.40 1.16 36.8% 0.31 0.39 357
CI2 102.0% 49.6% 1.38 1.14 39.4% 0.28 0.39 357
Note. For comparison, performances of the original CI1 (Hu et al., 2012) and the OCx algorithms are also listed.
Definitions and meanings of the statistical terms can be found in Hu et al. (2012). RMSE: root‐mean‐square error,
URMSE: unbiased RMSE, MRE: mean relative error.
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showed smoother transition (i.e., no apparent “bump”) between low and
higher Chl ranges. However, compared to OCI1′, OCI2 is closer to OCx
around the 1:1 line for Chl > 0.2 mg m
−3
and especially for
Chl > 0.4 mg m
−3
, suggesting that OCI2 is likely better in algorithm con-
sistency when considering that OCx is actually the algorithm used for
Chl > 0.4 mg m
−3
. Subtle but noticeable difference was found for
Chl > 0.2 mg m
−3
, which does not indicate poor OCI algorithm perfor-
mance because by algorithm design CI is different from OCx and this dif-
ference can also show up in the transition zone. Because in the transition
zone the weighted mixing (equation (7)) is based on Chl
CI
, OCI2 and OCx
do not merge exactly on the 1:1 line even after Chl > 0.4 mg m
−3
.
Statistically it is difficult to determine which algorithm is closer to the
truth due to significant data spread in this range and due to the small dif-
ference between CI1 and CI2 (Figure 1). However, an algorithm should
have smooth transition between low‐Chl and higher‐Chl waters. In this
regard, OCI2 appears to have better performance than either OCI1
(0.25–0.3) or OCI1 (0.25–0.4; i.e., OCI1′).
The effect of OCI2 on image quality is significant when the OCx image is
used as a reference to compare with (Figure 5). This is similar to the OCI1
effect shown in Hu et al. (2012). Most of the noise due to cloud‐adjacent
stray light contamination, shown clearly in the OCx image, disappeared
in both OCI1 and OCI2 images. This effect is what the algorithm concept
was designed for (Hu et al., 2012). For this gyre location OCI2 showed
lower Chl than OCI1, which was also closer to OCx (not necessarily more
accurate, however), a result of adjustment of the algorithm
coefficients (Figure 1).
The OCI2 and OCx algorithms are further compared in their global
monthly MODIS Chl maps in Figure 6. Visually, they appear similar in
all major spatial patterns as well as Chl magnitudes except that OCI2
Chl maps are more spatially coherent because most of the noise‐induced
errors for low‐Chl waters have been removed in the band difference
design (Hu et al., 2012; such a contrast can only be visualized in the
full‐size versions of the images). However, their relative difference images
clearly show disparity across major ocean basins. For example, during
winter months (January and March), Chl
OCx
is mostly lower than
Chl
OCI2
for North Atlantic and North Pacific, but during the month of
June the opposite is observed. Similar to those from the scatterplot evalua-
tions, however, it is difficult to conclude from these results alone which
algorithm is closer to the truth.
The most significant improvement of the CI design is on cross‐sensor con-
sistency, as shown in Hu et al. (2012). Such an improvement is illustrated
again in Figure 7, where histograms of monthly Chl distributions for oli-
gotrophic oceans (defined by SeaWiFS mission climatology
Chl < 0.1 mg m
−3
), derived from OCx, OCI1, and OCI2 algorithms, are
compared between SeaWiFS and MODISA and between VIIRS and
MODISA. The distributions from OCx showed large cross‐sensor differences that are difficult to observe
from image inspection or scatterplot evaluations. Such differences are largely removed in both OCI1 and
OCI2, where they show nearly identical cross‐sensor distributions.
The improvement in cross‐sensor consistency is further shown in Figure 8, where global mean Chl of oli-
gotrophic oceans (defined by SeaWiFS mission climatology Chl < 0.1 mg m
−3
) are compared between
SeaWiFS and MODISA and between VIIRS and MODISA using their ratios. A perfect cross‐sensor consis-
tency would result in a ratio of 1.0 for all months. The OCx algorithms are associated with relatively large
Figure 3. Improvements of OCI2 over OCI1 in the algorithm transition
zone of 0.25–0.4 mg m
−3
for all three sensors. Global level‐3 daily R
rs
composites in 2010 (365 daily composites) were used to generate the histo-
gram distributions for MODISA and SeaWiFS, and 365 global daily level‐3
R
rs
composites in 2014 were used for VIIRS. The wavelengths of MODIS and
VIIRS green bands (547 and 551 nm, respectively) were adjusted to 555 nm
using empirical relationships between these bands derived from NOMAD
data.
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cross‐sensor discrepancies (about 10%), while both OCI1 and OCI2 reduced such discrepancies
significantly to 1–2%.
In summary, similar to the original OCI1, the OCI2 algorithm showed improvements over the OCx algo-
rithms in production of equivalent or better Chl values for low‐Chl waters, but more importantly, far
improved image quality and cross‐sensor consistency. Furthermore, it reduced the discontinuity in the algo-
rithm transition zone. Therefore, future data processing may adopt OCI2 as an update of OCI1.
3.3. Data Recovery
The MODIS default 7 × 5 straylight mask was changed to 0 × 0 (i.e., no straylight), 3 × 3, 5 × 3, and 9 × 7.
Because in situ data could not be found from the entire SeaBASS archive corresponding to these masked pix-
els, statistics of image data alone was used to evaluate their potential differences.
Figure 9 shows the Chl histogram distributions from pixels with different masking schemes. The evaluation
was performed from pixels of 665 MODISA imagesfor all “rings”near cloud edges. The first ring includes the
cloud‐free pixels with “0”distance to cloud edge, that is, those between the 0 × 0 and 3 × 3 masks. The second
ring includes cloud‐free pixels between the 3 × 3 and 5 × 3 masks, and so on. Figure 9a shows that except for
the first ring (black line), all other rings have similar histogram distributions, especially when considering
that they are comparable to those pixels traditionally considered as being not influenced by stray light (i.e.,
between 7 × 5 and 9 × 7). This is not a surprise, as the tolerance of the OCI design to input errors has already
been demonstrated in Hu et al. (2012). What is surprising is that for the 665 evaluated MODISA images, OCx
also appears to be tolerant to stray light after a 3 × 3 masking (Figure 9b). Indeed, other than the first ring (i.e.,
3 × 3 masked pixels), allother rings show similar histogram distributions. These results indicate that the 7 × 5
straylight mask might be relaxed to 3 × 3 in order to increase data quantity without losing data quality.
The statistics on Chl distributions were from 665 MODISA images over clear waters in the North Atlantic
gyre. To evaluate whether the same observations can also be made to R
rs
(λ), the same statistical analysis
was performed for the 665 MODISA images, but with the focus on R
rs
(λ) instead of Chl. Our rationale is that
conclusions drawn from the R
rs
(λ) statistics over clear waters may be extended globally (including
coastal waters).
Figure 10 shows that similar to the Chl distributions, except for the pixels immediately adjacent to cloud
edges (i.e., 3 × 3 masking), all other rings have similar histogram distributions, meaning that statistically pix-
els in the ring between 3 × 3 and 7 × 5 are not different from those between 7 × 5 and 9 × 5 masking. The
importance of this finding is that because R
rs
(λ) serves as the inputs for nearly all bio‐optical inversion algo-
rithm (including Chl algorithms), the straylight flag may be relaxed from the current 7 × 5 to the new 3 × 3
pixels for possibly all ocean color data products including Chl, of course pending further evaluations on spe-
cific data products. Indeed, results from the same analysis over all MODISA images over the Gulf of Mexico
Figure 4. Comparison between OCx (xaxis) and OCI1 and OCI2 (yaxis) for three ocean color sensors. The gray dashed line of each panel indicates the 1:1 line.
The results were generated using the entire global level‐3 daily R
rs
data sets in 2010 for MODIS and SeaWiFS and in 2014 for VIIRS, respectively. Here OCII
(0.25–0.4) is termed as OCI1′in Table 1. Note that OCI and OCx do not merge exactly on the 1:1 line even after the upper bound of the transition zone because the
weighted mixing in equation (7) is based on Chl
CI
.
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Figure 5. Comparison between Chl images (approximately 1,000 km wide) derived from three algorithms for MODIS data
collected on 30 August 2004 over the South Pacific gyre (subscene from the inset figure).
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during 2010 indicate that even when pixels are restricted to waters with Chl > 0.4 mg m
−3
, pixels in every
ring (except the 3 × 3 ring) around cloud edge have near‐identical histogram distributions in either spectral
R
rs
(λ) or Chl
OCX
(Figure 11), suggesting that a 3 × 3 straylight mask is sufficient even for productive waters.
Based on these results, the relaxed 3 × 3 straylight flag was applied to global level‐2 OCI2 data for March and
July 2005 to generate new global mean Chl
OCI2
maps with a 9‐km resolution grid, which were compared
with the current (default) Chl
OCI2
maps. The results are shown in Figure 12. Some of the data gaps in the
default Chl
OCI2
maps are filled in the new maps after relaxing the straylight mask from 7 × 5 to 3 × 3 pixels.
These are particularly apparent in the equatorial Pacific and Atlantic within the Intertropical Convergence
Zone, the North Atlantic, the North Indian Ocean, and the Southern Ocean (all circled in red in Figure 12).
Figures 12e and 12f show the comparison between the monthly mean Chl
OCI2
derived from the default 7 × 5
masking and the new 3 × 3 masking. To reduce pepper noise from individual 9‐km pixels, spatial binning
was performed first. Although the changes in Chl
OCI2
between the two masking schemes are relatively small
(within a few percent), there are some spatially coherent patterns, for example, slightly lower Chl
OCI2
in the
Southern Ocean and waters off Peru (in July 2005) from the new masking scheme.
The increased coverage and different monthly mean Chl from the relaxed straylight masking are all due to
the increased number of valid observations in every 9‐km grid cell. Figure 13 shows the comparison between
number of valid level‐2 pixels used to calculate the monthly mean from the 7 × 5 and 3 × 3 masking schemes.
Globally, the relative increases in the number of valid level‐2 pixels are ~39% for both months, but the
increases can be more significant in many regions, often reaching >100% as indicated by the green and
colder colors in Figures 13e and 13f. In the North Indian Ocean, the increases reached 400% in July 2005
(i.e., the ratio is ~ <0.2 in the maps).
4. Discussion
4.1. Need for More Field Data From Ocean Gyres
Each time satellite data are reprocessed with new calibration or new algorithm the resulting data products
are changed, and most times with improvements. To date, data from all sensors have been reprocessed sev-
eral times by NASA, with the OCI1 algorithm incorporated during the most recent reprocessing (2014.0 and
Figure 6. Comparison of global monthly Chl maps derived from (left column) OCx and (middle column) OCI2 for different months of 2010, which were derived
using daily level‐3R
rs
composites. The right column shows their relative differences.
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2018.0) for its advantages over the traditional OCx algorithms. The results shown here demonstrate that
OCI2 may be a better choice. This is because that while its absolute accuracy is similar to OCI1 as gauged
by the in situ validation statistical measures and its cross‐sensor consistency is also comparable to OCI1,
its applicability range is extended to 0.4 mg m
−3
with smoother algorithm transition between 0.25 and
0.4 mg m
−3
than OCI1.
One large difference between OCI2 and OIC1 is their Chl retrievals for low concentration waters
(<0.05 mg m
−3
), mostly over ocean gyres. These account for about 14.7 ± 2% of the global ocean area accord-
ing to SeaWiFS monthly statistics between 1998 and 2010. From a statistical point of view, however, it is
Figure 7. Comparison of Chl distributions from global oligotrophic oceans (geographic extent defined by SeaWiFS Chl climatology ≤0.1 mg m
−3
, where actual Chl
during a specific month can be 0.4 mg m
−3
(corresponding to log10(Chl) = −0.4) or higher) obtained from three algorithms. The comparison was made (left col-
umn) between SeaWiFS and MODISA and (right column) between VIIRS and MODISA.
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difficult to know which algorithm yielded better (i.e., more accurate) results due to the extreme data scarcity
from field measurements and the general uncertainties associated with collecting in situ Chl in these very
clear waters. The significant data spread for Chl < 0.05 mg m
−3
in Figure 1 also suggest possible errors in
the field‐measured Chl or R
rs
(λ) because bio‐optical properties over ocean gyres are expected to be very
stable between Chl and R
rs
(λ). The reason for data scarcity is possibly due to the remote locations, while
the reason for erroneous data may possibly be due to difficulty in obtaining reliable in situ R
rs
(λ) data and
due to the requirement of large volume of waters to be filtered to obtain a measurable signal from these
extremely low‐Chl waters. Indeed, even after applying additional quality control to the NOMAD R
rs
(λ)
data using a recently developed scoring system (Wei et al., 2016), it is still impossible to determine which
Figure 8. Mean Chl ratios over global oligotrophic oceans (geographic extent defined by SeaWiFS Chl climatology
≤0.1 mg m
−3
, where actual Chl during a specific month can be 0.4 mg m
−3
(corresponding to log10(Chl) = −0.4) or
higher) between different sensors using three different algorithms. (a) MODISA/SeaWiFS. (b) VIIRS/MODISA. In (a), the
long‐term mean monthly ratios are 0.91 ± 0.04, 0.99 ± 0.03, and 1.00 ± 0.03 (N= 102) for OCx, OCI1, and OCI2,
respectively. In (b), the long‐term mean ratios are 0.89 ± 0.04, 0.98 ± 0.04, and 0.98 ± 0.05 (N= 50) for OCx, OCI1, and
OCI2, respectively.
Figure 9. Histograms of (a) Chl
OCI2
and (b) Chl
OCx
from pixels near cloud edges after applying different masking meth-
ods. The statistics were derived from 665 MODISA images in 2010 over the North Atlantic gyre (60°–40°W, 15°–35°N).
Note that except for pixels in the first “ring”(i.e., within 3 × 3 pixels of cloud edge), pixels from all other rings, including
those outside the default 7 × 5 straylight mask, have similar histogram distributions.
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measurements from ocean gyres are more trustable than others, leading to no difference in the statistical
regressions. Therefore, more high‐quality Chl and R
rs
(λ) data need to be collected from ocean gyres,
especially from waters with Chl < 0.05 (or even <0.03 mg m
−3
), in order to determine the lower bound of
Chl in algorithm development. Currently, given the data scarcity and the slight difference between OCI1
and OCI2 for extremely clear waters, it is difficult to tell which algorithm is closer to the truth for these
waters and what is the lowest possible Chl in global natural waters. However, some indirect inference
Figure 10. Histograms of (a) R
rs,443
, (b) R
rs,547
, and (c) R
rs,667
from pixels near cloud edges after applying different
masking methods. The statistics were derived from 665 MODISA images in 2010 over the North Atlantic gyre (60°–40°W,
15°–35°N).
Figure 11. Histograms of (a) R
rs,443
, (b) R
rs,547
, (c) R
rs,667
, and (d) Chl
OCx
from pixels near cloud edges after applying
different masking methods. The statistics were derived from all MODISA images in 2010 over the Gulf of Mexico
(98°–80°W, 18°–31°N) for Chl > 0.4 mg m
−3
.
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suggests that 0.01 mg m
−3
might be more reasonable than 0.02 mg m
−3
. For example, the maximum Secchi
depth measured in the South Pacific gyre was ~70 m (Doron et al., 2011). Using the “Case‐1”bio‐optical
model (Morel & Maritorena, 2001) and the new Secchi depth theory (Lee et al., 2015), this value more
likely corresponds to Chl value of ~0.01 mg m
−3
rather than 0.02 mg m
−3
. However, without more field
measurements of high‐quality Chl, this can only be used as indirect evidence to support OCI2. Indeed, the
lowest Chl value in the algorithm development data set (NOMAD) is 0.012 mg m
−3
, indicating the need
of more data around this value.
On the other hand, even without resolving the puzzle of which one is closer to the truth for low‐Chl waters,
OCI1 or OCI2, they both can be used to study changes induced by climate variability. This is because each
algorithm is self‐consistent, therefore even if the absolute Chl value may be slightly off the changes detected
by the algorithm are still trustable. In this regard, algorithm consistency is more important than algorithm
accuracy. This is exactly why the improved cross‐sensor consistency from either OCI1 or OCI2 (over OCx) is
important in order to form a seamless, multisensor climate data record, which can then be used to assess
long‐term ocean changes in response to climate variability (e.g., Antoine et al., 2005; Gregg et al., 2005;
Polovina et al., 2008; Signorini et al., 2015).
4.2. Data Quality, Quantity, and Uncertainty
In addition to Chl algorithm refinement, this work also proposed a practical scheme to recover some of the
previously masked cloud‐free data through relaxing the cloud‐adjacent straylight mask. Although these data
exist at level‐2, they are discarded in global level‐3 composites using a 7 × 5 mask. Our results showed that
Figure 12. (a and b) Level‐3 monthly global Chl
OCI2
(mg m
−3
) at each 9‐km grid for March and July 2005, respectively, when the default 7 × 5 straylight masking
scheme was applied together with other quality controls to the individual level‐2files to derive the global composites. (c and d) Same as in (a) and (b) but
relaxed straylight masking (3 × 3) was applied together with other quality controls. The red eclipses highlight regions where considerable amount of missing data
from the former have been recovered in the latter. (e and f) Their relative (%) differences (relaxed/default –1.0) are shown, respectively. To remove pepper noise,
the images were spatially binned before calculating the difference, leading to the apparently reduced data gaps than shown in the default‐flag maps.
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such a mask was probably too conservative, and a reduction to a 3 × 3 mask would recover many pixels with
similar quality to those nearby pixels. Globally, these recovered pixels represent about 40% of the previous
valid retrievals, thus indicating a significant increase in data quantity. In some regions during certain
seasons, the increases could reach several hundred percent.
Such increases have profound impacts on both data availability at daily scale and data product uncertainties
at longer (e.g., weekly, monthly, seasonal) scales. First, most global or regional studies have used monthly
mean products to study spatial/temporal changes. Because product uncertainties induced by independent
and normally distributed errors are inversely proportional to square root of number of observations, the
increased number of valid retrievals will lead to reduced data product uncertainties. This type of uncertainty
reduction has been demonstrated in Qi et al. (2017) using global ocean color data products. For a 39%
increase, uncertainties will be reduced by 15% (=1 –1/sqrt(1.39)). For some regions, the reduction can be
much higher. For regions with significant and persistent cloud cover (e.g., Intertropical Convergence
Zone, North Indian Ocean during summer), the new masking scheme may fill some of the monthly data
gaps (i.e., from 0 observation to at least 1 observation) to lead to more spatially coherent patterns. For the
same reason, when applied to level‐2files or short‐term (three‐day, seven‐day) image composites, the new
scheme would provide more spatial coverage to guide field measurements in near real time.
The increases from the new masking scheme are in relative terms. Overall the number of valid retrievals is
very low (~5% for both MODISA and MODIST; Feng & Hu, 2016a). After a 39% increase this becomes ~7%.
Clearly, future studies should focus on whether there is additional room for further increases of data quan-
tity without losing quality, for example, through relaxing the Sun glint masking threshold, through new
Figure 13. (a and b) Number of valid MODISA Chl observations at each 9‐km grid for March and July 2005, respectively, when the default 7 × 5 straylight masking
scheme was applied together with other quality controls to the individual level‐2files to derive the global composites. A number of 200 indicates that
during the month there are 200 valid 1‐km data points in the 9‐km grid. (c and d) Same as in (a) and (b) but relaxed straylight masking (3 × 3) was applied together
with other quality controls. (e and f) Ratios between (a) and (c) and between (b) and (d), respectively. Several regions of low ratios (<0.2) are outlined, meaning
that the relative increase in valid observations can reach >400% if the relaxed masking scheme is used. Overall the global average increase is about 40%.
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algorithms to define straylight pixels (e.g., Jiang & Wang, 2013), or through machine learning using the
spectra of low‐quality pixels (e.g., Chen et al., 2019).
Although the focus of this paper is on Chl data products, statistics of cloud‐adjacent pixels showed that the
7 × 5 straylight mask might be relaxed to 3 × 3 even for R
rs
(λ). An extensive evaluation for the entire
Figure 14. Histograms of (a) R
rs,443
, (b) R
rs,551
, (c) R
rs,671
, and (d) Chl
OCI2
from pixels near cloud edges after applying
different masking methods. The statistics were derived from all VIIRS images in 2013 over the North Atlantic gyre for
Chl < 0.25 mg m
−3
.
Figure 15. Histograms of (a) R
rs,443
, (b) R
rs,551
, (c) R
rs,671
, and (d) Chl
OCx
from pixels near cloud edges after
applying different masking methods. The statistics were derived from all VIIRS images in 2013 over the Gulf of Mexico
(98°–80°W, 18°–31°N) for Chl > 0.4 mg m
−3
.
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MODISA time series of 2003–2016 indicated that although the new masking scheme resulted in slightly
different (up to 3%) global statistics in R
rs
(667), the global statistics of R
rs
(λ) for all MODISA ocean
bands remained nearly unchanged (https://oceancolor.gsfc.nasa.gov/analysis/global/at140_at135/; AT135
= 7 × 5 masking, AT140 = 3 × 3 masking). However, the distributions of regional R
rs
(λ) have changed
due to increased number of observations (e.g., Figures 12 and 13). This has significant implications for all
other ocean color data products (e.g., inherent optical properties such as absorption and backscattering coef-
ficients), as R
rs
(λ) is the common input to algorithms for these products. Therefore, similar analyses may be
conducted on these products in the future. Likewise, whether the new straylight masking scheme is applic-
able to sensors other than MODISA should also be evaluated. For example, analyses of VIIRS data over the
North Atlantic gyre and Gulf of Mexico for both clear waters (Chl < 0.25) and more productive waters
(Chl > 0.4 mg m
−3
), respectively, showed that statistically, there is little difference between pixels within
the 3 × 3 −7 × 5 rings and outside the 7 × 5 straylight mask (Figures 14 and 15). These results indicate that
VIIRS straylight mask may also be relaxed from 7 × 5 to 3 × 3 in order to increase data quantity without com-
promising data quality.
Unlike other data recovering schemes that rely on a model to change the R
rs
(λ) values in order to improve
coverage (Chen et al., 2016) or a machine learning approach to use R
rc
(λ; Rayleigh corrected reflectance) to
improve coverage (Chen et al., 2019), the new masking scheme does not change R
rs
(λ), therefore is straight-
forward to implement and test for Chl retrievals. Once proven feasible, the scheme is expected to have a
major impact on ocean color data records from either a single sensor or merged multiple sensors, for exam-
ple, by the Ocean Color Climate Change Initiative supported by the European Space Agency (Brewin et al.,
2015) or by a NASA‐funded multisensor data merging effort (Maritorena et al., 2010).
Finally, regardless of all its advantages, the CI design is still empirical, and uncertainties due to variable
contributions from colored dissolved organic matter or other nonliving water constituents are embedded
in the empirical Chl data products, and such contributions may vary across different ocean basins (Szeto
et al., 2011; Figure 5 of Hu et al., 2013). Although the impact of such uncertainties may be small when
evaluating relative long‐term Chl changes of individual ocean basins, they may represent a small but vari-
able bias in the absolute Chl values across ocean basins. Such uncertainties might be reduced through an
empirical correction scheme using the curvature from the 412‐, 443‐, and 488‐nm bands (e.g., Hu et al.,
2014), yet its efficiency still remains to be tested.
5. Conclusion
The original OCI algorithm designed to retrieve Chl of global ocean surface waters from satellite ocean color
measurements has been revisited using more field data. Compared to the original OCI algorithm (OCI1), the
new OCI2 algorithm shows similar improvements over the traditional OCx for oligotrophic waters in terms
of accuracy, image quality, and cross‐sensor consistency between SeaWiFS, MODISA, and VIIRS, but at the
same time leads to smoother transition in the intermediate Chl range (0.25–0.4 mg m
−3
). Therefore, OCI2
may be a better choice than OCI1 for global data processing. More importantly, a relaxed straylight masking
scheme (from 7 × 5 to 3 × 3 pixels) leads to significantly increased data quantity without losing data quality
for both Chl and R
rs
(λ) when tested with MODISA and VIIRS over the North Atlantic (oligotrophic waters)
and Gulf of Mexico (more productive waters). The increases in data quantity over global oceans reach 39%.
Future efforts should focus on collection of high‐quality data in ocean gyres with Chl < 0.05 mg
−3
in order to
further improve data product accuracy for these extremely clear waters, and focus on evaluation of other
ocean color data products near cloud edges from the mainstream ocean color sensors.
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Acknowledgments
This study was supported by the U.S.
NASA (NNX14AM63G, NNX15AB13A,
NNX13AO53G) for MODIS and VIIRS
algorithm refinement and for data
product improvement. Satellite data
were provided by NASA Ocean Biology
Processing Group (OBPG) while VIIRS
level‐1 data were provided by NOAA.
We thank all researchers who
contributed to the NASA SeaBASS data
archive. All field and satellite data can
be obtained from the NASA OB.DAAC
through http://oceancolor.gsfc.nasa.
gov. We thank the two anonymous
reviewers for their valuable comments
to improve this manuscript.
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