AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1425-2017Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenesSayerAndrew M.andrew.sayer@nasa.govhttps://orcid.org/0000-0001-9149-1789HsuN. ChristinaBettenhausenCoreyHolzRobert E.LeeJaehwaQuinnGregVeglioPaolohttps://orcid.org/0000-0002-4721-8651Goddard Earth Sciences Technology And Research (GESTAR), Universities Space Research Association (USRA), Columbia, MD, USANASA Goddard Space Flight Center, Greenbelt, MD, USAAdnet Systems, Inc, Bethesda, MD, USASpace Science and Engineering Center, University of Wisconsin, Madison, WI, USAEarth Systems Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USAAndrew M. Sayer (andrew.sayer@nasa.gov)13April20171041425144415July201617August201623March201724March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1425/2017/amt-10-1425-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1425/2017/amt-10-1425-2017.pdf
The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to
continue the record of Earth Science observations and data products produced
routinely from National Aeronautics and Space Administration (NASA) Moderate
Resolution Imaging Spectroradiometer (MODIS) measurements. However, the
absolute calibration of VIIRS's reflected solar bands is thought to be
biased, leading to offsets in derived data products such as aerosol optical
depth (AOD) as compared to when similar algorithms are applied to different
sensors. This study presents a cross-calibration of these VIIRS bands against
MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS
Level 1 (version 2) reflectances between approximately +1 and -7 %
(dependent on band) are needed to bring the two into alignment (after
accounting for expected differences resulting from different band spectral
response functions), and indications of relative trending of up to
∼ 0.35 % per year in some bands. The derived calibration gain
corrections are also applied to the VIIRS reflectance and then used in an AOD
retrieval, and they are shown to decrease the bias and total error in AOD across
the mid-visible spectral region compared to the standard VIIRS NASA
reflectance calibration. The resulting AOD bias characteristics are similar
to those of NASA MODIS AOD data products, which is encouraging in terms of
multi-sensor data continuity.
Introduction
Launched in late 2011, the Suomi National Polar-Orbiting Partnership (S-NPP)
satellite is a precursor to the Joint Polar Satellite System (JPSS), which
represents the next generation of the USA's operational Earth observation
satellites. One of the instruments aboard S-NPP (and the JPSS series) is the
Visible Infrared Imaging Radiometer Suite (VIIRS;
, ), designed to
continue the types of observations made by the Defence Meteorological
Satellite Program (DMSP) Advanced Very High Resolution Radiometers (AVHRR,
data record 1978 onwards) and Earth Observing System (EOS) sensors such as
the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, 1997–2010) and Terra/Aqua
Moderate Resolution Imaging Spectroradiometers (MODIS, 2000 onwards). All of
these instruments are passive broad-swath imaging radiometers, measuring top
of atmosphere (TOA) radiance in a set of reflective solar bands (RSBs) and
(except SeaWiFS) thermal emissive bands (TEBs).
Data from DMSP and EOS-era instruments have been used for a broad variety of
Earth science applications, including the study of tropospheric aerosols, and
a number of algorithms have been developed to create aerosol optical depth
(AOD) data products from these sensors over both land (e.g.
) and water (e.g.
) surfaces. In
all these algorithms, aerosol information is determined using a subset of the
available RSBs, while TEB data are used mainly for identifying pixels
containing water or ice clouds. The measurement capabilities of VIIRS and
MODIS are similar, which has motivated the adaptation of EOS-era National
Aeronautics and Space Administration (NASA) algorithms to VIIRS, with the
goal being to move toward a multi-sensor consistent long-term data record
from a combination of MODIS and VIIRS measurements. In the case of AOD, VIIRS
versions of the Satellite Ocean Aerosol Retrieval (SOAR) algorithm, applied
previously to SeaWiFS observations over water
(), and the Deep Blue (DB) algorithm, applied
previously to SeaWiFS and MODIS measurements over land
(), are shortly to be released to the public.
VIIRS versions of the MODIS “Dark Target” land algorithm, and over-water
algorithm, are also in development ().
The National Oceanic and Atmospheric Administration (NOAA) Interface Data
Processing Segment (IDPS) also generates a number of data products from VIIRS
in near-real time to support their operational needs, including AOD over
oceans and dark land surfaces (); these
are based on the same scientific principles to the NASA algorithms and have
a similar data quality (;
). However, the algorithms are not
identical (hence have different contextual biases) and operate in
forward-processing mode only. Thus as algorithm or calibration updates are
made, discontinuities arise in the data records as data are not reprocessed
retrospectively to provide a self-consistent time series. Both agencies also
generate their own similar but slightly different Level 1 (L1; measured RSB
reflectance/TEB radiance) data sets. L1 data are sometimes further denoted
L1a (uncalibrated) or L1b (calibrated) data, although in practice additional
corrections are sometimes applied to the “standard” L1b data before
processing to geophysical data products (which are known as Level 2, L2), and
so the term L1 is used here for simplicity.
VIIRS has similar on-board calibration capabilities to MODIS, and NASA L1
requirements for the RSBs are 2 % accuracy in reflectance (for a reference
scene brightness) and 2.5–3 % (dependent on band) polarisation
sensitivity. Recent work () indicates that the trending of the
radiometric calibration since launch in the NASA VIIRS L1 products remains
well characterized. Trending is monitored using a solar diffuser (SD) and SD
stability monitor (SDSM), together with periodic orbit manoeuvres to view the
moon as another stable calibration source (). These data form the basis of the
calibration applied in the NASA MODIS and VIIRS standard L1 data files, used
in data processing by the Atmospheres and Land scientific discipline teams,
and the analyses are performed routinely by the MODIS Characterization
Support Team (MCST) and VIIRS Characterization Support Team (VCST); MCST and
VCST (referred to as “NASA calibration” for simplicity) are composed of many
of the same people, leading to consistency in approach. Similarly,
corrections to account for polarisation sensitivity, which are important for
ocean colour studies and AOD retrievals using wavelengths in the blue
spectral region, are fairly mature (e.g. ).
However, the SD analyses account for only the drift in calibration through
the mission (i.e. a relative trending) and do not address the absolute
calibration. As mentioned above, NOAA IDPS operates in forward-processing
only; various studies have indicated that early in the mission the IDPS
products did not meet the desired RSB absolute calibration accuracy,
suggesting calibration biases in excess of 5 % in some cases, although at
present in the IDPS products all bands are believed to meet performance goals
following various calibration analyses and improvements
(, ;
, ;
; ). This
has not yet been done for the NASA L1 products, which is a motivation for the
present study. Depending on the magnitude and spectral correlation of
calibration biases, significant offsets can be introduced in derived data
sets such as AOD, sometimes larger than the ∼ 0.03 often taken as a
realistic minimum typical AOD retrieval uncertainty for low-AOD open-ocean
scenes using this type of sensor ().
These calibration discrepancies must therefore be reduced if the goal of a
long-term aerosol data set with as consistent as possible error
characteristics from the MODIS and VIIRS sensors is to be achieved.
These prior studies of VIIRS RSB absolute calibration have largely been
performed by comparing near-simultaneous observations from VIIRS and MODIS
Aqua, typically over bright targets such as deserts, Dome C in Antarctica, or
deep convective clouds. MODIS Aqua has been used as it is considered to have
better absolute calibration than VIIRS and well-characterized stability
().
Further, the two sensors' fields of view intersect on a regular basis. Even
if MODIS Aqua's calibration is not perfect, tying VIIRS to MODIS Aqua does
mean that any calibration-related biases in derived data sets should look
similar in both sensors, thus increasing the level of data product
consistency between the two.
Additionally, these studies have typically only considered a subset of VIIRS
RSBs, excluding some which are required for the AOD retrieval algorithms
which have been applied to EOS-era measurements. They used NOAA L1 data from
the first few years of the VIIRS mission; these results may not necessarily
be transferable to the NASA calibration, or to more recent years of
observations, since the underlying L1 source data are not the same (and, as
mentioned, various updates to the NOAA IDPS products have been made in
forward-processing). The differences in derived calibration corrections over
these different target types are in some cases non-negligible. This is likely
due to some combination of the different versions of NOAA L1 products used,
as well as difficulties accounting for the slight spectral and directional
differences between MODIS and VIIRS observations over these diverse surface
types. In some cases it is not documented exactly how differences between the
sensors' spectral/directional characteristics were accounted for (i.e.
whether discussed discrepancies include or exclude the level of difference
expected due to sensor differences). Finally, the main focus of the majority
of these prior studies is bright targets, while AOD retrieval is largely
performed over dark scenes, and so residual forward model biases or
non-linearities in detector response may limit the applicability to dark
scenes.
Absolute (whether direct or vicarious) calibration using an atmospheric
correction ground data source such as Aerosol Robotic Network (AERONET;
) is another option, but this has a
disadvantage of also calibrating out some forward radiative transfer model
errors (i.e. derived calibration coefficients may not be transferable to
other applications since they include biases in the radiative transfer model
and/or retrieval algorithms as well as the sensor), which has the side effect
of removing the independence of the calibration from the data source
typically used to validate the derived geophysical data product (in this
case, AOD). As one example, the NASA Ocean Biology Processing Group (OBPG)
take a vicarious calibration approach, in which calibration gains are adjusted to
make water-leaving radiance retrievals consistent with ground truth data,
that is sensor-independent and results in a high level of inter-sensor data
consistency but does mean that residual errors in the atmospheric correction
algorithm propagate into derived vicarious gains
(); analysis of the resultant errors in
the output AOD from the atmospheric correction process indicates that they
are not negligible (). Additionally, at
present the OBPG have not performed this analysis for the VIIRS near-infrared
(NIR) and shortwave infrared (SWIR) bands (B. Franz and R. Eplee, personal
communication, 2016). Thus, even though the OBPG analyses take the NASA L1
data from VCST as a basis, they cannot necessarily be used directly in other
retrieval algorithms. performed an absolute
calibration of some VIIRS bands (again, except SWIR) using ancillary AERONET
Ocean Color (AERONET-OC) data, bypassing the retrieval stage of the OBPG
calibration methodology (i.e. thus keeping the data independent of its
validation data), although they found that the quality of this absolute
calibration could be limited by the quality of aerosol constraints from the
AERONET-OC data in clean conditions.
The purpose of this study is to describe a cross-calibration of L1
reflectance from S-NPP VIIRS RSBs against MODIS Aqua over dark water scenes.
Section summarises some relevant features of the
sensors, and Sect. presents in detail the
calibration methodology applied. Section
illustrates the results of the analysis, and Sect. shows
the improvement in retrieved AOD resulting from the calibration exercise by
applying the SOAR algorithm to VIIRS scenes passing over AERONET sites.
Sensor characteristics
MODIS (; ) and
VIIRS (; )
are both spaceborne broad-swath single-viewing multispectral passive imaging
radiometers. VIIRS records data in 22 moderate-resolution bands (M bands)
across the visible and thermal infrared spectral regions with a nominal pixel
size of 750 m at the centre of the swath. MODIS has a total of 36 bands
covering the same spectral region, with nominal pixel sizes of 250 m–1 km
at the centre of the swath (dependent on band). Each of these VIIRS M bands
has a central wavelength close to one or more MODIS bands. Table shows the band pairs used in this analysis, although MODIS
has additional bands, including some others across the visible spectral
region of interest here. Note, however, that some of the MODIS bands designed
for ocean colour applications saturate at radiances found over land or cloudy
scenes; some of the VIIRS RSBs bands are dual-gain and so do not saturate in
many of these cases. In this analysis (and also in MODIS routine atmosphere
and land data product generation) the relevant so-called MODIS “land bands”
(MODIS B1-B7), where a close match is available, are used instead of these
ocean colour bands, even when the latter have a closer central wavelength.
For simplicity, wavelengths of MODIS/VIIRS band pairs will be referred to
using the notation given in the right column of Table ,
although full sensor relative spectral response functions (RSRs) were used
for all radiative transfer calculations presented in this work. The RSRs for
the bands used are shown in Fig. , clearly illustrating that
some band pairs are more similar between sensors than others.
VIIRS moderate-resolution (M) bands and band centres of similar
MODIS bands used in this study. The final column indicates the shorthand
notation adopted for each respective band pair in this study.
Relative spectral response functions for VIIRS (red) and MODIS
(blue) bands used in this study (cf. Table ).
VIIRS additionally has five imagery-resolution bands (I bands) with a nominal
pixel size of 375 m and band centres close to some M-band positions, and a
Day/Night Band (DNB), which is an enhanced follow-on to the DMSP Operational
Line Scanner (OLS; ). Neither the I bands nor DNB
are used in the present DB or SOAR algorithms, so they will not be discussed
further. Likewise, the VIIRS and MODIS TEBs will not be discussed further.
As mentioned previously, stability of MODIS/VIIRS RSBs is monitored and
maintained using the SD, SDSM, and lunar rolls (). As
a result the RSB absolute calibration for each band is tied to the measured
reflectance (ρi) rather than radiance, where
ρi=πD⊙2∫0∞Lλ(λ)Φi(λ)dλμ0∫0∞Eλ(λ)Φi(λ)dλ.
In the above Lλ is the spectral radiance passing into the satellite
field of view, Eλ the downwelling solar spectral irradiance at TOA,
and Φi the sensor RSR for band i, all functions of wavelength
λ. The factor D⊙ is the Earth–Sun distance in astronomical
units (variable throughout the year) and μ0 the cosine of the solar
zenith angle, which affect the total solar radiation received. Lλ
and consequently ρi depend on the surface–atmospheric states and
observation geometry, omitted here for simplicity of notation. Equation () is often simplified by considering the total
radiance Li observed by band i:
Li=∫0∞Lλ(λ)Φi(λ)dλ.
Subscripted λ is used to refer to a spectral quantity and
subscripted i to the value integrated across sensor band i. Analogously,
precomputing the spectrally integrated solar irradiance across the band,
which then only varies as a function of day of year, gives
E0,i=∫0∞Eλ(λ)Φi(λ)dλD⊙2,
leading to the more common form
ρi=πLiE0,iμ0.
The radiative transfer codes used in the DB and SOAR algorithms operate in
units of Sun-normalised radiance, L/E0 (sometimes written I/F0 in
alternative notation; in both cases, dropping the subscripted i when
talking in the general case), to minimise numerical instabilities at large
solar zenith angles (i.e. as 1/μ0 tends to infinity). As a result the
discussion in the present study also uses units of Sun-normalised radiance,
which does not affect the adoption of the results presented herein for other
applications. Working in Sun-normalised rather than total radiance also has
the advantages of accounting for the effects of the sensors' different RSRs
on E0 for each band and slightly different solar zenith angles at the
times of MODIS and VIIRS observations.
Both sensors suffer from a “bow-tie distortion” which affects the size,
shape, and overlap of pixels from nadir to scan edge
(, ).
Essentially, as the detector scans across-track pixels become broader and
elongated and pixels from consecutive scans overlap, which has consequences
for retrieval characteristics as a function of scan angle and can affect
statistics of AOD retrievals (). VIIRS
incorporates several design features to reduce this distortion. The VIIRS
native pixel size is actually smaller than the nominal M-band size in the
across-track direction. The scan is divided into three regions (in both
directions). From nadir out to a scan angle of 31.72∘, three pixels
are aggregated across-track; from 31.72 to 44.86∘ two pixels
are aggregated; and from 44.86∘ to the edge of scan
(56.28∘, corresponding to a view zenith angle around 75∘)
no aggregation is performed. This limits across-track distortion at the end
of each aggregation zone to a factor of 2 compared to a factor of about
6 without this oversampling and aggregation. Additionally, at the outer two
aggregation zones, two and four pixels, respectively, are deleted from the edge
of scan (so-called “bow-tie deletion”) to minimise the degree to which
consecutive scans overlap (although not all overlap is removed by this).
S-NPP and Aqua are both in Sun-synchronous orbits with daytime equatorial
local solar crossing times at the centre of swath around 13:30 UTC. However,
orbit altitudes (averages of 705 km for Aqua and 839 km for S-NPP) and
inclination are different, which mean that the sensors are not always
observing the same location (near-simultaneous observations within 10 min
occur roughly every other day, covering only a segment of the world each
time). Both satellites' orbital repeat cycles last 16 days. The MODIS swath
width is approximately 2330 km, providing near-global daily daytime
coverage (there are gaps between consecutive orbits at low latitudes) while
VIIRS has a sufficiently broad swath (3040 km) that consecutive orbits
overlap, even at the Equator. Both sensors have some degree of overlap
between consecutive orbits at mid- and high latitudes.
Cross-calibration methodology
This analysis seeks to calibrate L1 reflectance in VIIRS bands M01–M11
(spectral range 412–2250 nm) against the corresponding MODIS Aqua bands
shown in Table . Band M09 is not considered; this band (for
both MODIS and VIIRS) is located in a spectral region of strong water vapour
absorption and so is typically used in threshold tests to detect high cloud
tops (cirrus or deep convective clouds) rather than in geophysical retrieval
algorithms (e.g. ), and the quality of its
absolute calibration in MODIS is unclear. All corrections presented herein
represent scaling factors which should be applied for NASA VIIRS L1 data to
make it radiometrically consistent with NASA MODIS Collection 6 L1 data and
account for the fact that the sensors have different spectral response
functions (i.e. correcting for the unexpected portion of any observed
discrepancy between the two). The steps of this exercise are listed here and
outlined in detail in the following subsections:
selection of appropriate MODIS/VIIRS pixels to consider;
correction for the effects of absorption by trace gases in the
atmosphere;
forward radiative transfer modelling to predict the TOA reflectance which should be observed by VIIRS, given that observed by MODIS
(i.e. calculating the expected level of difference);
aggregation of results to a monthly timescale and derivation of cross-calibration scaling coefficients (i.e. comparing the observed
and expected level of difference and making a correction to bring these in line).
Data description and selection of appropriate pixels
The NASA VIIRS data processing is facilitated by Science Investigator-led
Processing Systems (SIPS) for each science discipline. In support of this,
the VIIRS Atmospheres SIPS at the University of Wisconsin
(http://sips.ssec.wisc.edu) have created “matchfiles” of collocated
VIIRS and MODIS Aqua observations to more easily compare the two sensors.
These matchfiles form the basis of the present analysis, and have been
created from the MCST MODIS Aqua Collection 6 data (at 1 km nominal pixel
size, the MYD021KM product) and VCST VIIRS Version 2.0 data (the VL1BM
product). These are the current versions of the L1 data for both sensors used
in routine processing of MODIS, which will be used for the first
processing of the VIIRS Deep Blue/SOAR L2 data products (as well as other
NASA VIIRS data products produced by the Atmospheres and Land SIPS). The time
period considered is from March 2012 (several months after the S-NPP launch,
at which point the VIIRS M-band RSB/TEB data were considered ready for use)
to 26 July 2016 inclusive. On 27 July 2016 MCST made a change to MODIS
calibration in some bands in forward-processing mode, which could introduce a
discontinuity, and so the analysis was stopped at this time (A. Angal,
personal communication, 2017).
The matchfiles contain L1 RSB/TEB data, geolocation, and land–sea mask
information for MODIS and VIIRS, with VIIRS pixels mapped into MODIS pixels
(due to a combination of native spatial resolution and bow-tie distortions,
VIIRS pixels are typically smaller than MODIS ones). Because of this, the
matchfiles contain the mean and standard deviation of VIIRS RSB
reflectance/TEB radiance within the area of each MODIS pixel, as well as that
corresponding to the nearest VIIRS M-band pixel to the centre of each MODIS
pixel. The files also contain the MODIS Collection 6 cloud mask for each
MODIS pixel (the MYD35 data product; an updated version of that described by
).
Two additional ancillary data sets were used in the analysis. The first is
the Goddard Earth Observing System Model Version 5 (GEOS-5) Forward
Processing for Instrument Teams (FPIT), available from
http://gmao.gsfc.nasa.gov/GEOS, at 0.5∘ latitude,
0.625∘ longitude, and 3-hour temporal resolution. Surface winds and
O3 and H2O total column abundances were extracted and interpolated
linearly to each pixel in the matchfiles. The second is climatologies of
oceanic chlorophyll-a concentration (Chl) derived from the SeaWiFS
record, available from http://oceancolor.gsfc.nasa.gov. These are
provided at a native spatial resolution of 9 km and one data set for each
of the 12 months of the year; to fill gaps, for the present analysis
these were degraded to 0.25∘ spatial resolution by taking a median
average and remaining gaps were filled using the nearest available month of the
year in time (for a gap in March, data from February and April are
used; if also missing then data from January and May, etc). The resulting
gap-filled climatology was also interpolated linearly (in units of log10Chl) to each pixel in the matchfiles.
Matched MODIS/VIIRS pixels were then selected for further analysis if they
passed all of the following criteria:
The difference in observation time was under 10 min, to minimise changes in the surface–atmospheric state between observations.
The view zenith angle and scattering angle differences were both smaller than 3∘, to minimise uncertainties related to the geometric
dependence of the scenes viewed (e.g. surface reflectance, atmospheric absorption, and scattering phase matrices). Note that the observation
time threshold mentioned above also effectively acts as a threshold on solar zenith angle difference.
The MODIS land mask classification was “deep inland water”,
“moderate or continental ocean”, or “deep ocean”, and the climatological
Chl <1 mgm-3, to remove cases where the ocean surface reflectance model used (see later) may be less appropriate.
The MODIS cloud mask classification was “confidently clear”, and the pixel was at least 5 km away from any pixel classed as “confidently cloudy” or “uncertain”.
This distance threshold removes approximately 80 % of “confidently clear” pixels, although it decreases the likelihood of errors resulting from cloud
movement between the MODIS/VIIRS overpass times or classification errors (i.e. cloud contamination) and 3D radiative transfer effects.
The relative standard deviation of VIIRS 670 nm reflectance within MODIS pixels was < 25 %, to remove residual inhomogeneous scenes.
Solar zenith angles were smaller than 70∘, to minimise shadow length and parallax effects and ensure a strong daytime RSB signal.
The latitude was equatorward of 60∘, as ship-based observations suggest that background oceanic aerosol optical characteristics at polar
latitudes can differ from those at lower latitudes ().
Sun glint contribution to Sun-normalised radiance from GEOS-5 winds and the model of was < 0.01
for both sensors, as over-ocean radiative transfer modelling is subject to higher uncertainties in glint hotspots.
Total column H2O was less than 3 cm, to decrease uncertainties related to trace gas absorption (because this can have a large absorption in
NIR and SWIR bands and exhibits fairly large spatiotemporal variability). Note that, although the bulk of matched pixels occur in the mid- and high
latitudes, this constraint removes most of the potential matchups in tropical regions (since these often have water vapour amounts in excess of this threshold).
Figure shows an example of the results of applying the
above filters to matchfiles created from a pair of MODIS/VIIRS granules,
separated by 1 min in acquisition time.
Example MODIS/VIIRS match up for two near-coincident granules
(beginning 1 min apart). The S-NPP VIIRS granule is outlined in red and
MODIS Aqua in blue. Suitable matched pixels are shown in green.
Correction for trace gas absorption
The next step is to correct the TOA MODIS/VIIRS RSB reflectances for the
effects of absorbing trace gases. Note that the corrections described here
are also applied in NASA VIIRS DB/SOAR processing. An assumption commonly
made in atmospheric and/or surface retrieval algorithm processing is that the molecular
absorption can be decoupled from other contributions to the TOA signal, and
so corrected for by applying a “brightening” factor to the observed TOA
reflectances. In the visible spectral region, this is justified because the
optical depths of the absorbing species are fairly small, and (particularly
in the case of O3) the bulk of the absorption is located higher in the
atmosphere than the main other contributors to the signal (Rayleigh
scattering, aerosols, and surface reflectance), so corrections for this
absorption can be developed with high accuracy ().
The computational advantage of performing such a correction is that it vastly
decreases the dimensionality of radiative transfer lookup tables (LUTs) used
in the retrieval process, since individual gas species and their variable
vertical profiles do not need to be built in to them.
This analysis uses the same approach taken in operational MODIS aerosol
processing (Appendix A of ), with the additional
step that, following , the effective column
H2O amount is taken as half the total column H2O to better account for
the atmospheric vertical structure. In brief, gas absorption is calculated by
the Line-By-Line Radiative Transfer Model (LBLRTM;
), which includes the High Resolution
Transmission (HITRAN) gas absorption data base (http://hitran.org),
used in combination with the MODIS and VIIRS RSRs to calculate the absorption
by atmospheric trace gases as a function of their amount and vertical profile
shape. The effective air mass factors for absorption, which includes the
effects of the Earth's curvature and typical gas profiles, are obtained from
. GEOS-5 data (discussed previously) are used to
obtain O3 and H2O column amounts, while climatological abundances are
used for the other species considered (CO, CO2, N2O, NO2, CH4,
O2, SO2), since their contributions to the total absorption are weak
and/or their spatiotemporal variability comparatively low. Note that NO2
variability, and consequently absorption, can be significant
() for sensor bands in the blue spectral region
(412 nm, and to a lesser extent 440–490 nm). However, the exclusion of
pixels close to land masses (Sect. ) means that this
is not an issue for the data considered here, since background oceanic levels
are low due to a lack of strong sources, and the short lifetime of NO2
means that long-range transport is fairly small.
The gas absorption corrections are calculated for and applied to each pixel
and band. After applying these corrections, the MODIS/VIIRS RSB L1 data are
effectively that which would be seen by the sensors in the absence of these
trace gases in the atmosphere, removing one cause of differences in TOA
reflectance between the two instruments. This step is important because,
despite the similarity of band central wavelengths (Table ),
RSR shapes can be sufficiently different (Fig. ) that
differences in the level of gas absorption for nominally similar bands can be
non-negligible in some cases. Thus, not taking gas absorption into account
could lead to biases in the cross-calibration exercise. This is illustrated
in Fig. : while some bands, such as MODIS B4/VIIRS M04
(both centred near 550 nm), are very similar and gas corrections are tightly
correlated and close to 1:1, others show more difference in magnitude and/or
spread, illustrating the importance of accounting for trace gas absorption
accurately when comparing L1 data from the two sensors.
MODIS/VIIRS spectral trace gas absorption corrections for suitable
matched pixels in January 2016.
Forward radiative transfer modelling of predicted VIIRS reflectance
The next step is to determine, given the observed MODIS TOA reflectance, what
reflectance VIIRS should see for each pixel. Because of the differences
between sensor RSRs (Fig. ), and the slight differences in
observation geometry between the two sensors for a given pixel, this requires
a radiative transfer forward model, and the results of the analysis will be
sensitive to the assumptions made in that forward model. This analysis uses
the VLIDORT radiative transfer model (),
which is the same as is used in the NASA VIIRS SOAR data set and allows for
a detailed description of aerosol properties and surface bidirectional
reflectance distribution function (BRDF), a pseudospherical atmosphere, and a vector treatment of the atmospheric radiation field, which is
important for accurate radiative transfer at short visible wavelengths (e.g.
). It is also able to account for the
full RSRs of the sensors when performing calculations. This radiative
transfer model has some advancements over those used previously by DB/SOAR;
it
has been benchmarked against standard results with good performance
(), and versions have also been used for
other aerosol remote sensing applications (e.g.
).
Aerosol optical model
As the comparison is restricted to open-ocean scenes, it is a reasonable
assumption that most of the AOD is contributed by “clean” (i.e. little
continental influence) maritime aerosols (e.g.
). For this reason, the “pure marine” aerosol
optical model of is used. This model
was based on AERONET inversions () from a
variety of sites, was applied previously in SeaWiFS SOAR processing
(), and is also applied in VIIRS SOAR data
processing. Real and imaginary aerosol refractive indices were taken from
, as there are few measurements of aerosol optical
properties across the whole VIIRS spectral range. Specifically, the fine mode
uses the “water-soluble” component refractive indices and the coarse mode the
“coarse-mode sea salt” component, both for aerosols at 70 % relative
humidity. In the radiative transfer simulations the aerosol is assumed to
occupy a homogeneous vertical layer from the surface to 1 km altitude; as
the aerosol is close to non-absorbing, and the data are further filtered for
low-AOD conditions (discussed further later), the vertical structure has
little influence on the modelled TOA signal.
The standard assumption made in the analysis is that the aerosol fine mode
fraction (FMF) of optical depth at 550 nm is 0.4, which is a typical value
determined from observations in a variety of global oceans
(). However, to assess the uncertainty
resulting from this assumption (discussed later), radiative transfer
simulations are also performed for FMF = 0.2 and FMF = 0.6.
Surface reflectance model
The ocean surface BRDF is an updated version of the treatment used by
for SeaWiFS, and the same model discussed herein
is also applied for SOAR VIIRS processing. The BRDF model draws on the
widely used method of and includes contributions
from oceanic whitecaps, Sun glint, and scattering from within the water
(“underlight”, using the basic formalism of ). Both
the whitecap and underlight terms have been updated since the SeaWiFS
application. The wind speed dependence of the whitecap formulation has been
updated using the formulation of , which
tends to slightly decrease the whitecap contribution to the BRDF at most wind
speeds, since and other studies suggest
that the older formulation used previously () may
overestimate the whitecap fraction.
Underlight is calculated using an empirical relationship based on Chl to
estimate absorption and scattering from pigments and co-varying materials.
This relationship was developed for so-called “Case 1” (largely open-ocean)
waters (). Within the underlight component of
the reflectance model, several updates have been made to the assumed water
absorption/scattering properties (previously taken from
). found that prior
estimates of the absorption coefficient of water at visible and ultraviolet
wavelengths were too high, and so the coefficients
have been adopted instead over their available spectral range (300–550 nm).
The results of
are used for 550–725 nm, which results in smooth
continuity with the results of , and
those of are used for longer wavelengths (although above 700 nm
water absorption is so strong that ocean reflectance depends negligibly on
chosen data source). The water scattering coefficient was also updated
according to and chlorophyll absorption
spectrum updated according to and
. At the same time, the spectral range of the
parametrisation has been extended to 300–900 nm (from the prior
400–700 nm). Directional aspects of the underlight contribution (so-called
f/Q ratio) have also been updated according to .
The combined effect of these coefficient updates, relative to prior
implementations of the same basic model (,
) is an increase of up to a few tens of percent
in the underlight contribution to ocean reflectance for the blue and green
spectral region (550 nm and shorter wavelengths), which translates to a few
percent in TOA reflectance.
LUT creation and application
To use MODIS observations as a predictor for VIIRS, VLIDORT has been used to
construct a pair of LUTs of MODIS and VIIRS reflectance for a variety of
surface and atmospheric conditions for each band. The node points are shown
in Table , and their spacing has been chosen such that the
linear interpolation error between node points is less than 1 % (relative)
error in Sun-normalised radiance, with the average bias across conditions
negligible.
Summary table showing the node points used in the MODIS/VIIRS
intercalibration LUT.
ParameterNodesSolar zenith angle4∘ spacing from 0 to 84∘View zenith angle4∘ spacing from 0 to 76∘Relative azimuth angle9∘ spacing from 0 to 180∘Near-surface wind speed1, 3, 6, 9, 12, 15 ms-1Chl0.01, 0.032, 0.1, 0.32, 1 mgm-3AOD at 550 nm0, 0.04, 0.08, 0.12, 0.16, 0.2, 0.24FMF at 550 nm0.2, 0.4, 0.6
The LUT is used by looping over each matched MODIS/VIIRS pixel pair and band,
using the measured MODIS TOA Sun-normalised radiance to estimate the AOD at
the reference wavelength of 550 nm (based on the ancillary MODIS geometric
information, wind speed, Chl climatology, and assumed aerosol optical
model). This derived AOD is then used (together with the VIIRS geometric
information) to predict the Sun-normalised radiance VIIRS would be expected
to see if its absolute calibration were equal to that of MODIS.
For each
pixel, this process is repeated for each band independently and for each of
the three aerosol FMF assumptions (0.2, 0.4, 0.6). This dynamic AOD
estimation, rather than assuming e.g. a single AOD across all scenes,
ensures that the spectral MODIS TOA Sun-normalised radiance is matched
exactly for each pixel and band, which decreases the uncertainties involved
in cross-calibrating the two sensors. One important point to note is that, as
the calibration of VIIRS is being tied to that of MODIS, what is most
important here is not so much the absolute accuracy of the radiative transfer
modelling or AOD estimation step but rather the accuracy of the
spectral/directional extrapolation between MODIS and VIIRS wavelengths and
geometries.
A further filtering step takes place at this stage. Pixels are only retained
when, for each band and for each of the FMF assumptions, an exact match to the
MODIS reflectance is found with a 550 nm AOD from 0 to 0.2. This removes
residual cases where the forward model may be inappropriate, e.g.
contamination by clouds/cloud shadows or continental (e.g. smoke, dust)
aerosols where the aerosol optical model assumption may be significantly in
error or cases where the ancillary data (Chl, wind speeds, trace gas
abundances) are significantly in error. This decreases the available data
volume but helps to ensure that the remaining pixels correspond to clean
open-ocean cases where the transfer between MODIS and VIIRS spectral and
directional characteristics has been achieved with high fidelity and retains
identical spatiotemporal sampling for all bands. Note that the standard
deviation of 550 nm AOD estimated independently using this aerosol optical
model from each band is 0.01 or less between bands in most cases, indicating
that any spectral biases in the radiative transfer model are small.
At the end of this stage, each suitable pixel and band have associated MODIS
and VIIRS TOA RSB observations, plus an estimate of the TOA signal which
VIIRS would be expected to see, under the assumption that MODIS Aqua's
calibration is correct. An example of the results, composited from the points
obtained in January 2016, is shown in Fig. . This
shows the expected spectral dependence of the TOA signal over open ocean: a
darkening as wavelength increases, because Rayleigh scattering, oceanic
surface reflectance, and aerosol scattering all decline at longer
wavelengths. MODIS at 470 nm is notably brighter than VIIRS at the VIIRS
band near 490 nm, and to a lesser extent for the 650/670 nm pair, for these
reasons. The theoretical predicted VIIRS TOA signal is much closer to the
observed VIIRS than observed MODIS for these bands, illustrating again the
importance of accounting for the differences in sensor RSRs rather than just
comparing the TOA signals directly (i.e. differences resulting from sensor
spectral characteristics may be larger than those resulting from sensor
calibration errors).
Median spectral observed TOA Sun-normalised radiance for MODIS
(blue) and VIIRS (red) bands and (black dashed) modelled expected VIIRS
signal, after the AOD estimation and filtering steps described in Sect. . Data are from January 2016.
Aggregation to monthly timescales and calculation of cross-calibration correction
The final step in the analysis is to aggregate the pixel-level results to a
monthly timescale and use these to derive cross-calibration coefficients.
The rationale for a monthly time step is that results derived from
observations on a single day are likely to have correlated errors (in terms
of forward model and ancillary data), as they are drawn from a limited
spatial and temporal snapshot of the world. In contrast, averaging to a month
should provide sufficient sampling that errors can be averaged out to a large
extent. Aggregation also minimises the influence of remaining outliers. At
the same time, monthly timescales remain sufficiently short that any
longer-term behaviour, such as seasonality or drifts in the relative
calibration, can be examined.
For each month and band, the remaining pixels are sorted by the theoretical
VIIRS L/E0 (i.e. the signal VIIRS is expected to report, given the
MODIS observations and the spectral/directional differences between the
sensors' observations, as modelled through VLIDORT) and divided into 50
equally populated bins. For each bin, the median theoretical and actual VIIRS
L/E0 are recorded. Binning data, and use of medians rather than means,
decreases the sensitivity to outliers caused by real scene changes, cloud
contamination, or radiative transfer errors. An example of this process for
January 2016 is shown in Fig. .
Bin-median observed and theoretical predicted VIIRS TOA
Sun-normalised radiance (L/E0) for suitable matched pixels in January
2016. The dashed line indicates a 1:1 relationship.
The cross-calibration gain correction (to make VIIRS radiatively consistent
with MODIS Aqua for a given Earth scene) derived for this month of data is
simply the mean ratio between these binned values, and VIIRS L1 reflectances
can be “corrected” to be unbiased with respect to MODIS Aqua by multiplying
the TOA signal (whether in reflectance or L/E0 units) for the band in
question by this number. This correction represents the scaling factor to
apply to the VIIRS L1 reflectance data to make it radiatively consistent with
MODIS Aqua, accounting for the expected differences due to the differences in
RSRs. It is important to emphasise again that this is a correction to the
VIIRS reflectances, not VIIRS-derived AOD, that is not tied to the SOAR AOD
retrieval algorithm, and this correction accounts for the RSR
differences but does not attempt to act as a “shift” of the VIIRS bands
(e.g. the VIIRS band M03 centred near 490 nm should still be treated as
such with its full spectral response function; the correction is not trying
to shift it to be a pseudo-MODIS band 3 centred near 470 nm).
This ratio approach is inherently making the assumption that the calibration
correction is a simple gain scaling factor, that it is linear, and that there
is no offset between the two for the darkest scenes; this is generally
expected to be the case on physical and engineering grounds, and as a result
the assumption is common in such calibration exercises
(; ,
; ,
; ;
; ). If
linear least-squares regression is performed on the binned data (not shown)
rather than simply taking the mean of the ratios between the bin-median
values, then the effective gain coefficients are similar and offsets are
close to zero, although there is a little more month-to-month variation since
two free parameters are being determined (offset and gradient) and a
deviation in one of these parameters is countered by a deviation of opposite
direction in the other. Thus, the numerical effects on corrected TOA
reflectance, or L/E0, is negligible whether the bin-ratio or linear
regression technique is used.
Further, linear coefficients of determination between the binned theoretical
and actual VIIRS signals are close to unity (R2>0.99), indicating that the
assumption of linearity is justified in this case. Note that this does not
preclude detector non-linearities across the whole range of VIIRS
brightnesses because the present analysis is restricted to scenes over
ocean, which are (particularly for the SWIR bands) fairly dark.
Derived gain corrections, time series, and uncertainty
Performing the steps detailed in Sect. results in
data distributed regionally and seasonally as shown in Fig. . Some regions are sampled frequently and
others never, due to the intersection of the two satellites' 16-day repeat
cycles. Seasonal variations are caused predominantly by variations in cloud
cover and land (more land in the Northern Hemisphere) and secondarily by
changes in solar angle (which affects the Sun glint location, latitudes of
daylight, as well as scattering angle differences).
Seasonal spatial distribution of matched MODIS/VIIRS pixels used for
the vicarious calibration exercise, aggregated to a 2.5∘ horizontal
grid size.
The distribution of these points in time is shown in Fig. . As well as seasonal variability (caused by the
aforementioned factors), the available data volume is larger from 2014
onwards than in 2012 and 2013. This can be explained by the satellites'
orbital times. Figure shows the equatorial local solar
crossing times of the ascending (i.e. daytime) nodes for both sensors. While
both are often quoted as a nominal 13:30 UTC crossing time, neither orbits at
exactly this time. Aqua's orbit crosses around 13:35 UTC, with small seasonal
variability, and is very tightly controlled as it flies as part of the
A-Train constellation. S-NPP was initially closer to 13:25 UTC and this has
gradually changed through the mission as a result of spacecraft orbital
adjustments, with the two platforms' crossing times within 5 min from
mid-2014 to mid-2015. As a result, the 10 min time difference threshold
imposed on the analysis (Sect. ), imposed to
minimise the influence of changes in the scene viewed between the two
sensors' overpasses, is more restrictive in 2012 and 2013 (aside from the
influence of seasonality, fewer matchups during this time than later in the
mission). Note that these changes in crossing time remain within the
missions' tolerance requirements. It is also worth noting that these changes
in Equator crossing time do not strongly influence the geographic
distribution of matched pixels.
Time series of the number of matched MODIS/VIIRS pixels per month
after all quality checks and filtering.
Variation of S-NPP (red) and Aqua (blue) satellite equatorial local
solar crossing times for the ascending (daytime) orbital nodes. The dashed
line indicates the nominal 13:30 UTC crossing time often used as shorthand
when discussing these platforms.
Compositing the monthly results (e.g. Fig. )
gives the time series shown in Fig. . The
three SWIR bands show more noise than the others, which is expected since
both ocean and atmosphere are quite dark at these wavelengths, and the
resulting ratios of small numbers are inherently less stable than ratios for
brighter bands. Several bands show seasonal oscillation and it is unclear at
present to what extent this is caused by both sensors' individual radiometric
stability and to what extent this may be related to seasonal changes in
geographical sampling (as it is possible that assumptions made in the
analysis may be more or less appropriate in different regions, leading to
residual geographic error). Nevertheless, these seasonal oscillations, where
present, tend to be small (amplitude < 0.01, i.e.
< 1 %).
Time series of monthly mean VIIRS gain correction factors derived
for each band (cf. Fig. ). Red lines
indicate the mean value for a month and the shaded grey area the standard
deviation within a month.
In addition, several bands show an apparent trending in the cross-calibration
scaling factor over the five-year period. For band M01, this is equivalent to
VIIRS becoming increasingly relatively brighter than MODIS; in this specific
instance, it is thought that VIIRS is the more stable of the two sensors (B. Franz and G. Meister, personal communication, 2016). For the other bands, it is
not clear at present which, or both, of the sensors is degrading. For VIIRS
bands M07, M08, and M10 the change over the 5-year period exceeds 1 %
and so the temporal dependence is probably worth accounting for until
residual trending of both sensors can be analysed and corrected for by the
respective instrument teams. Stability of both sensors is monitored using the
on-board solar diffuser and SDSM, as discussed previously. For the bands in
question, additional polynomial detrending analyses were performed and
implemented for the MODIS C6 reprocessing
(); however, the most recent years of
MODIS data had not yet been collected at that time, so it is possible that
any additional degradation has deviated from these prior models.
The mission-averaged gain correction factors are shown in Table , along with (for bands M07, M08, and M10) linear trends.
These trends were calculated from least-squares linear regression of the
monthly gains with the time ordinate taken as years since the start of
1 January 2010, and the uncertainties presented with these parameters are
the standard least-squares linear regression uncertainties. A linear model
was used based on visual examination of the data, although there is no
particular reason to expect a linear change as opposed to any other specific
functional form or that this behaviour will continue in years to come, so
these trends should be interpreted with caution.
Derived cross-calibration gain correction scaling factors.
Mission-averaged gains are presented, as well as linear trends, for those
bands where the estimated gain change over the mission to date is larger than
1 % and statistically distinguishable from zero at the 90 % confidence
level. For the trends, times t are defined in terms of years since 1 January
2010. Figures in parentheses indicate the 1 standard deviation
uncertainty estimate.
For the mission-averaged gains, the total uncertainty σtot was
estimated as the quadrature sum of four components:
σtot2=σtemp2+σhet2+σaer2+σgas2.
These components were estimated as follows:
Temporal variability (σtemp): this component was taken as the standard deviation of the monthly derived gains, and
incorporates both the effects of changes in the gain with time, as well as noise in the monthly values (from e.g. sampling, residual
errors in the radiative transfer or ancillary data).
Scene heterogeneity (σhet): as noted previously, VIIRS M bands are at a finer spatial resolution than the MODIS data
used in the matchfiles, and so the matchfiles contain both the mean and nearest-to-pixel-centre VIIRS reflectance within each MODIS pixel.
The potential error from scene heterogeneity was assessed by performing the same analysis with both mean and nearest-neighbour VIIRS reflectances
and taking half the absolute difference in calculated gains between the two.
Aerosol model assumption (σaer): as noted, restricting to open-ocean low-AOD scenes means that the dominant aerosol type is likely
to be marine aerosols, and the assumption was made of a global-average FMF = 0.4. The uncertainty resulting from this assumption was estimated by
repeating the whole analysis using FMF = 0.2 and 0.6 and taking half the absolute difference in the results.
Trace gas absorption assumption (σgas): this is estimated by considering the 68th percentile of the absolute difference in the
gain correction (on a monthly basis), which would result if the trace gas absorption correction for either sensor were systematically biased
relative to the other sensor by 10 % of the magnitude of the gas correction. The median of these monthly values is then reported as σgas
for each band, although the temporal variability is small.
These uncertainty estimates are designed to be conservative; e.g. temporal
standard deviation rather than standard error was used to calculate
σtemp on the grounds that it is uncertain to what extent the
uncertainties on individual monthly values are random vs. systematic. The
temporal variability and gas components tend to be the dominant contributors
in most bands. The term σhet is the smallest component for all
bands.
Aside from bands M01 and M02 (no significant adjustment) and M08 (slight
brightening), the effect of the cross-calibration is to darken the VIIRS
channels by up to ∼7 %. There are no other results which are directly
comparable with these since, as mentioned previously, prior analyses
(;
B. Franz et al., personal communication, 2016)
have used different versions of the L1 data (mostly, NOAA IDPS rather than
NASA VCST baseline products), have considered only a subset of bands, and
have in some cases exhibited contradictory results. Thus, some differences
are expected. However, if these gain changes bring VIIRS measurements closer
to the “truth”, then this should ideally be reflected in the validation of
geophysical data products applying these gains prior to their retrieval
algorithms.
Effect of calibration updates on AOD retrieval
This section illustrates the results of applying the cross-calibration
corrections in Table (including time-dependence, for the
relevant bands) to VIIRS data and processing them through the SOAR retrieval
algorithm to illustrate the effects on derived aerosol properties. The VIIRS
application of SOAR will be described in a subsequent study, although it is
basically an extension of the SeaWiFS application
() to incorporate some of the additional
features of VIIRS. Like many others, SOAR is a multispectral inversion using
LUTs of physically based radiative transfer results (e.g.
).
The algorithm uses VIIRS bands M03, M04, M05, M08, M08, M10, and M11 (i.e.
7 of the 10 bands analysed in this study) and provides AOD, FMF, and an
indication of best-fit aerosol optical model at a nominal pixel size of
6×6 km2 (8×8 M-band pixels). Spectral AOD is determined
through the retrieved AOD at 550 nm, together with the retrieved FMF and
aerosol optical model. The specific details are of secondary importance here,
as the main purpose is to illustrate the effects of the calibration change on
the retrieval. To demonstrate these effects, the SOAR algorithm has been
applied to VIIRS granules passing over six AERONET sites (Table ), using the standard NASA L1b products with and without
the cross-calibration gain corrections developed in this study. Comparing the
AOD retrievals with AERONET enables a characterisation of both how much the
spectral AOD retrieval is affected by the cross-calibration and whether
these changes have improved the retrieval or not.
Note that SOAR is a multispectral inversion, fitting all bands
simultaneously; the underlying radiative transfer is non-linear in AOD, and
the bands are not weighted equally. As a result, changes in an individual
band's calibration do not map linearly into retrieved AOD at a given
wavelength, and it makes the most sense to analyse the behaviour of the
retrieval system as a whole rather than attempt to assess or infer the
effect of changes in AOD at a given wavelength to the calibration of
individual bands. This is important to bear in mind when considering the
results.
Locations of the AERONET sites used and number of matchups obtained
at each.
These sites have been chosen based on their locations (coastal/island AERONET
sites with typically fairly low AODs, such that aerosol optical model
assumptions will be less important contributors to the retrieval error
characteristics) and the fact that, for at least part of the VIIRS mission,
the sun photometers deployed at these sites included a 1640 nm filter so
that AOD can be validated across the whole spectral range of bands used by
SOAR-VIIRS with minimal extrapolation (the majority of AERONET sun
photometers lack filters at wavelengths longer than 1020 nm). The analysis
protocol is the same as in and is described
briefly here. AERONET provides point observations of spectral columnar AOD
with a repeat frequency of approximately once per 10–15 min (in
cloud-free conditions) and an uncertainty of order 0.01–0.02, with the larger
uncertainties at shorter wavelengths
(). To mitigate the effects of
spatiotemporal aerosol variability on the comparison, AERONET Version 2 Level
2 (cloud-screened and quality-assured; )
observations are averaged over ±30 min around the time of the VIIRS
overpass and interpolated to the VIIRS M-band wavelengths using the
spectrally closest AERONET AOD (with the exception that the AERONET 870 nm
band is used in preference to 1020 nm due to increased uncertainties in the
latter) and the Ångström exponent over the appropriate spectral
region. Occasional missing AERONET AOD data at an individual wavelength are
gap-filled in the same way. As it lies outside the AERONET spectral range,
M11 (2250 nm) AOD is estimated from the AERONET AOD at 1640 nm and an
Ångström exponent calculated over the spectral range 870–1640 nm.
This spectral interpolation/extrapolation introduces negligible additional
uncertainty into the AERONET values. These AERONET AOD data are compared with
averaged SOAR-VIIRS retrievals passing algorithm quality assurance checks
() whose pixel central locations lie within
±25 km of the AERONET site.
Figure provides summary validation statistics for AOD at
550 nm, composited across the AERONET sites. Using the standard L1
calibration the bias is 0.033, similar in magnitude to that of the NOAA VIIRS
ocean AOD products (); applying the
cross-calibration gains removes a little over half of this bias (about
0.016) and also gives a root-mean-square (RMS) error smaller by about 0.01.
It additionally brings another 13 % of matchups in agreement with AERONET
to within the data set's expected level of uncertainty. The resulting bias on
mid-visible AOD is similar to or smaller than preliminary validation results
for the current Collection 6 MODIS over-water AOD retrieval algorithm, which
is conceptually similar to SOAR (). The correlation coefficient is similar,
indicating that the effect is more a shift in the AOD distribution than a
change in the scatter. The total data volume changes slightly, as the
calibration change affects cloud masking and quality assurance parts of the
SOAR algorithm in the two runs ().
Figure presents some similar summary statistics, but
for spectral AOD rather than AOD at 550 nm. Only the points where both runs
provide a valid matchup are considered in this figure (and these provide the
counts listed in Table ). Similar bias/RMS improvements,
and negligible changes in correlation coefficient, are seen at other visible
wavelengths. For the SWIR bands the change in AOD is smaller and there
remains a residual positive bias of around 0.02. It is unclear to what extent
this indicates problem with the retrieval forward model and/or MODIS Aqua's
calibration, or calibration biases at individual AERONET sites (which are
likely to be reasonably systematic, rather than random, for an individual
site and individual deployment). Nevertheless, the cross-calibration results
clearly improve the quality of the main retrieval data product (AOD at
550 nm) and provide similar error statistics to the most similar available
MODIS AOD product.
Statistics of SOAR-VIIRS 550 nm AOD validation against AERONET
(a) without and (b) with the cross-calibration gains derived in the present
study. Statistics are given on the panels: Pearson's linear correlation
coefficient R, the median VIIRS-AERONET AOD bias, the root-mean-square
(RMS) error, the number of matched points n, and the fraction of points f
in agreement with AERONET within ±(0.03 + 10 %) Statistics are composites
for all sites listed in Table .
Statistics of SOAR-VIIRS spectral AOD validation against AERONET
without (black) and with (red) the gain adjustments derived in the present
study. Panels show (a) correlation coefficients, (b) root-mean-square AOD
error, and (c) median AOD bias. Statistics are composites for all sites
listed in Table .
Discussion
Accurate and stable radiometric calibration is a necessary first step in
creating a high-quality space-based data record of atmospheric aerosols, or
indeed other geophysical variables. This is becoming increasingly important
as there is potential to combine data records from multiple similar satellite
sensors. This analysis has used near-coincident MODIS Aqua and S-NPP VIIRS
observations of cloud-free open-ocean scenes to develop cross-calibration
corrections for VIIRS M-band TOA reflectance, accounting for the differences
between the sensors' spectral response functions and viewing geometries, to
tie the VIIRS calibration to the MODIS standard. To be clear, these
corrections represent scaling factors to apply to the NASA VIIRS L1 version 2
TOA reflectance/radiance data to make the bands radiatively consistent with
MODIS Aqua, noting the fact that some differences are expected due to the
differences in RSRs and only attempting to correct for the unexpected
portion of the differences.
The analysis suggests that the standard NASA L1 (version 2.0) VIIRS
reflectance data require scaling by between approximately +1 and -7 %,
depending on the band, to bring them into radiative consistency with MODIS Aqua,
with indications of relative trending of up to ∼ 0.35 % per year (over
the March 2012–July 2016 time period analysed) in some bands. The relative
contribution of the two sensors to these drifts in relative calibration is
not yet clear. The precision of these gains is typically of order 0.5–1 %
for visible and NIR bands and 1–2 % for SWIR bands due to a combination
of trending and increased noise in the latter cases. Application of the
cross-calibration gains to the SOAR algorithm, to derive spectral AOD over
water pixels around six AERONET sites, illustrates that the calibration
adjustments do provide an improvement in retrieved AOD in the visible
spectral region (470–865 nm) although they do not address a bias in AOD
retrieved at SWIR wavelengths.
Even if MODIS Aqua's calibration is imperfect, as any sensor's necessarily
is, this analysis is consistent with prior indications that it is likely to
be better than that of VIIRS. Importantly, obtaining radiatively consistent
L1 data increases the likelihood of similar error statistics in downstream L2
data products, which facilitates the creation of long-term data records by
combining individual sensors, by minimising discontinuities between products.
Additionally, the analysis technique is independent of the AERONET validation
data used to evaluate the AOD retrieval algorithms, so the effect of the
calibration changes can be evaluated in a way that is not circular and also
has the advantage that the gain corrections can be applied directly to other
retrieval algorithms (i.e. errors in the SOAR algorithm or AERONET data are
not aliased into the corrections). Further, the analysis can be easily
repeated as additional versions of the source MODIS and VIIRS L1 data become
available, to assess whether the offsets, and relative stability, of the
sensors has changed. Conceptually it could also be applied to other sensors
with frequent orbital overlaps – although, as seen in this analysis, even for
sensors with nominally similar orbital overpass times, a shift in a few
minutes can have a large effect on the data volume available for analysis.
Several caveats remain. The scenes analysed here (cloud-free oceans) are for
brightnesses typical for aerosol retrievals, i.e. fairly dark, and detector
non-linearities may mean that for very bright scenes (e.g. optically thick
clouds or snow) the relative offsets between the two sensors may differ,
particularly for the SWIR bands where the open-ocean signal is low. However,
preliminary results from independent analysis of bright water clouds scenes
provide largely consistent results, suggesting non-linearities are small (K. Meyer, personal communication, 2017). The use of binned data and robust
statistics (medians) decreases the susceptibility to residual cloud
contamination and other sources of outliers, although it is possible that
these will make some residual contribution to the uncertainty. Another main
caveat is that, while similar, VIIRS' and MODIS' spectral response functions
are different, and these differences are large enough that the calibration
exercise must involve the use of radiative transfer models, as the expected
differences arising solely from these shifts in spectral response can be
larger than the calibration corrections. The effects of this are more severe
the more strongly the relevant sensor bands differ. This analysis has
accounted for these spectral response differences, which is possible since
the spectral dependence of atmospheric and surface scattering and absorption
can be accounted for with high accuracy over these cloud-free low-aerosol
oceanic scenes, although these differences do contribute to residual error
and uncertainty in the derived calibration corrections. The well-calibrated
hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) has been
used in the thermal IR domain to investigate the calibration and spectral
response of other sensors (), as its hyperspectral bands can be
combined to mimic closely the broader bands of MODIS and other sensors; there
is no current equivalent, however, for the visible through to SWIR spectral
domain. The proposed Climate Absolute Radiance and Refractivity Observatory
(CLARREO) Reflected Solar Spectrometer (), once
available, will add this important capability.
The source data sets used can be accessed via the links in the Acknowledgements or on the Assets tab. The VIIRS aerosol data products will be released to the public later in 2017;
sample results are available from the authors on request.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the NASA ROSES program and NASA's EOS program
managed by Hal Maring. More information about the Deep Blue aerosol project
can be found at http://deepblue.gsfc.nasa.gov. MCST and VCST are
thanked for their efforts to maintain and improve the radiometric quality of
MODIS and VIIRS data. The VIIRS Atmospheres SIPS at the University of
Wisconsin (http://sips.ssec.wisc.edu; ), in particular S. Dutcher, are
thanked for assistance and resources related to the creation of the
matchfiles and Deep Blue processing support. The AERONET team and site PIs
(P. Goloub, L. Gregory, B. Holben, M. Mallet, R. Wagener) are thanked for the
creation and stewardship of the sun photometer data record; AERONET data are
available from http://aeronet.gsfc.nasa.gov; . GMAO are thanked for the
meteorological data used in this analysis. The OBPG are thanked for useful
insights and suggestions relating to an initial version of this analysis, as
well as the creation of SeaWiFS chlorophyll products. D. Antoine (Curtin), B. A. Franz (NASA GSFC), Z. Lee (University of Massachusetts Boston),
and A. Vasilkov (SSAI) are thanked for useful discussions and data sets about the
current status of measurements of the optical properties of seawater and
bidirectional aspects of remote sensing reflectance, and R. Spurr (RT
Solutions) for additional development of the VLIDORT RT code and interface.
X. Xiong (NASA GSFC), K.-F. Chiang (SSAI), N. Lei (SSAI), A. Angal (SSAI), K. Meyer (NASA GSFC), C. Cao (NOAA), and A. Gilerson (CCNY/NOAA CRESST) are
thanked for discussions about MODIS and VIIRS characterisation and spacecraft
orbits. Data processing was facilitated by use of the GNU Parallel utility by
. Two reviewers (F. C. Seidel and one anonymous)
are acknowledged for helpful and constructive comments, which helped to
improve the clarity of the manuscript.
Edited by: A. Kokhanovsky
Reviewed by: F. C. Seidel and one anonymous referee
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