As aerosol amount and type are key factors in the “atmospheric correction”
required for remote-sensing chlorophyll
We run the MISR research retrieval algorithm (RA) with the corrected MISR
reflectances to generate MISR-retrieved Chl and compare the MISR Chl
values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and
Storage System) in situ observations.
Where Chl
The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS–Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.
Among the geophysical quantities routinely produced from the NASA Earth
Observing System's Multi-angle Imaging SpectroRadiometer (MISR) instrument
are aerosol optical depth (AOD) and aerosol type. MISR measures upwelling
shortwave radiance from Earth in four spectral bands centered at 446 (blue),
558 (green), 672 (red), and 866 nm (near-infrared, NIR) at each of nine view
angles spread out in the forward (
Flow chart describing the MISR RA aerosol/Chl retrieval process.
A second factor directly affecting the quality of almost every MISR geophysical data product is the accuracy of the instrument's radiometric calibration. As the MISR data record now exceeds 17 years of near-global coverage about once per week, the advantages of further refining the MISR calibration have increased multifold. This applies to determining AOD trends and is especially true in the context of MISR's ability to retrieve aerosol type (Kahn and Gaitley, 2015). In addition to AOD and aerosol type, retrievals of ocean bio-optical properties from space are extremely sensitive to the calibration of the instrument, because only 5 to 20 % of the top-of-atmosphere (TOA) reflected signal in the blue and green spectral bands, where ocean color is retrieved, arises from scattering related to ocean under-light (e.g., Fig. 2; more generally, Gordon and Wang, 1994). We find that not only the absolute radiometric calibration, but also the MISR blue/green ratio, critical for ocean color applications, has changed over time.
This paper is organized as follows: Sect. 2 reviews the datasets used in our analysis and the methodology adopted, Sect. 3 presents the Chl retrievals and initial validation of the results, example retrievals are shown in Sect. 4, and conclusions are given in Sect. 5. MISR radiometric calibration corrections, including details of the observed temporal trends, are described in Appendix A.
The aerosol/Chl retrieval algorithm (RA) is summarized as a flow chart in
Fig. 1. An in-depth description of the main RA components can be found in
Limbacher and Kahn (2014, 2015). Briefly, the algorithm finds the set of
aerosol optical models, associated aerosol amounts, and Chl values that
minimize the difference between the observed TOA reflectances (identical to
bidirectional reflectance factors, BRFs, described in Appendix A, but without
the solar-zenith
normalization) and simulated values that are stored in a look-up table (LUT).
The overall aim is to derive AOD and Chl over 1.1 km retrieval regions,
conditioned on aerosol-type mixtures that produce TOA reflectances meeting certain
Prior to Limbacher and Kahn (2014), the MISR RA simulated ocean surface was modeled as an isotropic (wind-speed-dependent only) Fresnel reflector (Cox–Munk), with whitecap reflectance included. In Limbacher and Kahn (2014), we made adjustments to the whitecap reflectance and added an ocean under-light term that includes molecular and particulate attenuation. In Limbacher and Kahn (2014, 2015), we used wind and ocean color constraints from the Cross-Calibrated Multi-Platform (CCMP; Atlas et al., 2011) and GlobColour (Barrot et al., 2010) products (and a climatology where these products were unavailable), respectively, to prescribe the ocean's color.
For the current analysis, we continue using CCMP data for 10 m wind speed
where available, otherwise we use the MISR version 22 standard aerosol retrieval algorithm (SA)
wind data, which comes from monthly averaged values of QuikSCAT and
the Special Sensor Microwave/Imager
(SSM/I; Michael Garay,
personal communication). We set the surface pressure to 1013.25 mbar, as we
find a number of cases where the MISR standard product surface pressure over
ocean is aliased from nearby mountains. This results from the different
footprint sizes of the SA vs. the RA; the SA has a 17.6 km footprint,
whereas the RA has a 1.1 km footprint. Additionally, instead of prescribing
Chl, we now retrieve it directly in the algorithm by inverting our ocean
color model. To do this, we use all four MISR spectral bands to
simultaneously retrieve aerosol and Chl, with equal weighting, whereas the
SA (Martonchik et al., 2009) and past versions of the RA retrieved only
aerosol amount and type and used only the red and NIR bands, where the ocean
surface is darkest, except at high AOD. However, empirical camera weighting
is applied here to mitigate the effects
of sunglint, and different uncertainties are assigned to the 36 MISR channels
when evaluating the
Colored dissolved organic matter (CDOM) absorption is assumed to co-vary with Chl (Morel and Gentili, 2009). Relationships connecting Chl to absorption and backscattering coefficients can be found in many places; the ones we used (Chen et al., 2003; Devred et al., 2006; Morel and Prieur, 1977; Morel, 1988) are summarized in Sayer et al. (2010). For our ocean under-light model, we modify the absorption of light by seawater for the blue spectral band from Morel and Prieur (1977), which was used previously in the RA, to more recent results from Lee et al. (2015).
The following equation gives a bidirectional water-leaving radiance:
The effect of including under-light, assessed by comparing the
MISR-observed TOA reflectances with model-simulated values, not
including
As we aim to extract both surface and aerosol information from the MISR data,
we apply new camera weights when calculating the
Figure 2 illustrates the impact of including under-light in the MISR RA, for the blue- and green-band retrieval TOA reflectance results. For this figure, MISR aerosol retrievals over dark water were performed using the multi-angular data for the NIR band only, because the ocean surface tends to be darkest at this wavelength (i.e., where under-light makes its smallest spectral contribution). When the retrieved aerosol properties are used in the forward radiative transfer model to simulate the MISR top-of-atmosphere reflectances in the blue and green bands, but under-light is not included, there are large discrepancies in the modeled TOA reflectances compared to the original MISR observations (Fig. 2a, b). However, when under-light is accounted for in the simulations (in this illustration using coincident Moderate Resolution Imaging Spectroradiometer (MODIS)–Terra Chl as input), the biases are substantially reduced, as shown in the lower two panels of Fig. 2. As the MODIS-constrained Chl was included when the aerosol retrieval was performed (using only the multi-angle NIR data), this example demonstrates the magnitude of the surface contribution to the TOA reflectances in the blue and green spectral bands. If surface contributions are not explicitly included, the aerosol retrievals would be skewed, and the spectral dependence of the anomaly could impact the derived aerosol type (e.g., Kahn and Gaitley, 2015), especially when the blue or green bands are included in the aerosol retrieval. In Sect. 4 we demonstrate the use of MISR to constrain Chl self-consistently with the retrieval of aerosol over ocean.
The MODIS TOA reflectances are key to several radiometric calibration adjustments detailed in Appendix A. As in Limbacher and Kahn (2014), MODIS–Terra equivalent reflectance data are used as a baseline to compare against MISR, especially for the nadir camera. We use the latest MODIS collection 6 TOA reflectances (Sun et al., 2012) with additional corrections implemented via an algorithm provided by Alexi Lyapustin (Lyapustin et al., 2014; elaborated in Limbacher and Kahn, 2015). Primarily, we are interested in the following MODIS bands: 9 (443 nm, as compared to MISR's 446 nm blue), 4 (555 nm, as compared to MISR's 558 nm green), an average of bands 13 and 14 (effectively 672 nm, as compared to MISR's 672 nm red), and 2 (856 nm, as compared to MISR's 866 nm NIR). In the current study, MODIS reflectances are used only to remove flat-fielding artifacts in the MISR imagery and to make modifications to the ghosting parameterization described in Limbacher and Kahn (2015), so the absolute calibration accuracy of MODIS is not critical here. The most critical assumptions are that MODIS swath-edge and scan-angle issues are minimal for the scenes of interest and that pixel-to-pixel relative precision is high. Fortunately, because the MISR swath samples about 380 km around the center of the 2300 km MODIS swath, the effects of MODIS swath-edge and scan-angle artifacts on the coincident data are minimal.
The SeaBASS dataset (Werdell and Bailey, 2002;
Although we validate our Chl retrieval against the SeaBASS dataset for
Chl < 1.5, we also cross-compare our Chl results with those
from MODIS–Terra (OBPG, 2014; OB.DAAC, 2014) to increase the number of
coincidences (especially needed for Chl < 1.5) and because MISR
and MODIS share a common platform. This ensures that the solar geometry is
the same for MODIS and MISR and minimizes potential collocation errors. To do
this, we compare MISR RA-retrieved Chl with the corresponding
MODIS–Terra-retrieved values (Hu et al., 2012). Details of the algorithm
used to generate the MODIS data can be found at
Although the main purpose of this paper is to demonstrate and validate our Chl retrieval, we also compare the new algorithm against AErosol RObotic NETwork observations for a few selected scenes. AERONET sun photometers (Holben et al., 1998) provide very accurate measurements of AOD (Eck et al., 1999) and Ångström exponent (ANG). The almucantar inversions (Dubovik and King, 2000) can provide constraints on particle sphericity (Dubovik et al., 2006; which we convert to fraction mid-visible AOD assigned to non-spherical particles, or Fr. Non-Sph), and for aerosol single-scattering albedo (SSA), provided the aerosol loading is high (AOD at 440 nm > 0.4), the scattering angle range for the inversions is large, and the aerosol is relatively uniform over the range of view angles used for the inversion (Holben et al., 2016).
Collocation of the MISR and SeaBASS observations is of course critical to
achieving meaningful comparisons. So for each SeaBASS–MISR coincidence, the
corresponding location within an MISR orbit is identified as a block
(180 blocks per orbit), line (128 along-track lines per block), and sample
(512 across-track samples per block) at Any MISR–MODIS data where the MODIS Chl flag data is masked (at level 3)
according to Any MISR–MODIS data where the MISR aerosol retrieval acceptance criterion is
violated. In this case the criterion, Any MISR–MODIS data where MISR 446 nm AOD > 1.0. AOD above this
value over ocean tends to occur only in cases of dust, smoke, pollution
plumes, or unmasked clouds. As the surface signal is very small for these
cases, especially for the off-nadir cameras, MISR should have little or no
sensitivity to Chl in these situations. Any MISR–MODIS data where the MISR Chl Any MISR–MODIS data where in situ Chl > 10 mg m
For comparisons with SeaBASS, we average (in log
Figure 3 shows scatterplots for MISR and MODIS–Terra-retrieved Chl vs.
SeaBASS coincident in situ Chl. Points left of the black vertical line in
Fig. 3, and Table 1, demonstrate MISR sensitivity to retrieving chlorophyll
MISR (red points) and MODIS (blue) Chl plotted against SeaBASS
validation data for Chl
Statistics of chlorophyll
Statistics of MISR vs. MODIS regional chlorophyll
Because the SeaBASS validation dataset contains very few matchups with MISR,
in part due to the relatively narrow MISR swath, we compare MISR 1.1 km
Chl retrievals with collocated MODIS 1 km Chl retrievals over much
larger regions surrounding the MISR–SeaBASS coincidence locations, using the
method described above. We compare to MODIS–Terra for this regional context
exercise due to the assessments already performed on the MODIS Chl data
with the much larger number of MODIS–SeaBASS coincidences (e.g., Franz et
al., 2012). As such, we compare the MISR RA Chl data with all valid pixels
for which MODIS Chl
Figure 4 shows comparisons between the MISR RA and MODIS-retrieved Chl, for
MODIS Chl
MISR–MODIS Chl scatter-density plot for Chl
MISR imagery acquired on 26 August 2003, 15:51 Z: Terra orbit 19620, MISR blocks 60–63, along the US east coast. Plots compare the MISR SA (at 17.6 km resolution, top row) to the RA that includes retrieved Chl (at 1.1 km resolution, row 2). AOD and particle properties correspond to the MISR green band (558 nm). AERONET direct-sun and inversion values are shown for the COVE and Wallops stations as embedded circles. AERONET Fr. Non-Sph may not be informative when aerosol extinction is dominated by the fine mode. In the lower left, MISR An and Df RGB images are shown for context. MISR-retrieved Chl and MODIS-retrieved Chl are shown in the bottom right two panels.
Same as Fig. 5, but for data acquired on 31 August 2003, 11:26 Z: Terra orbit 19690, MISR blocks 96–98, in the mid-South Atlantic Ocean near Ascension Island.
We present here two examples of individual MISR RA joint surface and
atmosphere retrievals and comparisons with the corresponding MISR SA
retrievals, MODIS Chl results, and embedded AERONET AOD measurements and
particle property retrievals. Figure 5 presents both the SA and RA aerosol
retrievals, along with the MISR RA and MODIS Chl results for a region of
the Atlantic along the east coast of the US that includes the Chesapeake Bay
and two coastal AERONET sites, in
August 2003. Weakly to non-absorbing, relatively small, pollution particles
are expected in this region and season, as confirmed by the AERONET inversion
results. Both the SA (Diner et al., 2008; Kahn et al., 2010) and RA also
identify the scene as dominated by small, spherical particles. Although the
RA finds weakly or non-absorbing particles spread fairly uniformly over the
entire scene, the SA appears to incorrectly identify part of the scene as
contaminated by moderately absorbing aerosol. The MISR SA best estimate
preferentially selects lower SSA (Fig. 5,
Figure 6 captures a scene in the mid-South Atlantic Ocean near Ascension
Island, where smoke advected from southern Africa is commonly found. Both the
SA and RA identify much of the scene as dominated by small, spherical
absorbing aerosol, consistent with both the Ascension Island AERONET station
and expectation. The scene is covered in broken cloud, typical of much of
this ocean region, which makes aerosol remote-sensing retrievals especially
challenging. Both the SA and the RA results exhibit 3-D light-scattering
effects near cloud edges. Here the difference between the SA and RA
retrieval-region sizes has significant consequences: the SA appears to have
more coverage, whereas the cloud-edge anomalies are more localized in the
higher-resolution RA retrievals, and the SA shows substantially more SSA (and
hence retrieved aerosol-type) variability (
In Limbacher and Kahn (2014), we detailed extensive modifications to the MISR
research aerosol retrieval algorithm over ocean that reduced the 0.024 AOD
high bias for AOD
Validation of the MISR RA-retrieved Chl, with all radiometric corrections
applied, was performed in part by comparison with coincident SeaBASS in situ
observations. Further comparisons were made against the previously validated
MODIS–Terra ocean color Chl retrievals, because of the relatively small
MISR–SeaBASS coincident dataset. Results show that the MISR RA can retrieve
Chl reliably if the MODIS-reported Chl
Obtaining MISR Chl retrievals can help fill in the glint-contaminated regions in the single-view MODIS–Terra swath near the solar equator, as only a few of MISR's nine view angles will be contaminated by glint in any one location, allowing the others to be used for the aerosol/Chl retrieval. In addition, these MISR Chl results are derived self-consistently with aerosol amount and type in a physical retrieval, which from the ocean color perspective provides a more robust “atmospheric correction” for the surface retrieval. This work formally opens the door for the use of MISR data in ocean color, complementing the better-constrained and more extensive spectral coverage of MODIS ocean color retrievals. With the improved ocean surface boundary condition, the MISR multi-angular data should also allow for better-constrained aerosol products, particularly non-sphericity and single-scattering albedo. In the future, it might be possible to ingest collocated MISR and MODIS–Terra reflectances and use the strengths of each instrument in a complementary manner.
The main
data used for the temporal trending analysis are the MISR L1B2 Terrain
projected data (MISR Science Team, 2015d). The MISR RA uses MISR
L1B2
Ellipsoid projected data (MISR Science Team, 2015b) as its primary input.
Additionally, MISR ancillary data, MISR L1 geometric parameter data, and ancillary
MISR L2 data are required to run the RA (MISR Science Team, 2015a, c, e). MISR SA
L2 aerosol output can be found in the MISR MIL2ASAE_L2 files (MISR Science
Team, 2015e). These data can be obtained from the NASA Langley Atmospheric
Sciences Data Center (ASDC) at
As mentioned in the introduction, instrument calibration can affect retrieval
products such as AOD, aerosol type, and ocean surface properties (Limbacher
and Kahn, 2015). Calibration includes determination of (1) the absolute
radiometric scale as well as (2) the relative band-to-band response among the
four MISR spectral bands, (3) the camera-to-camera response among the nine
MISR cameras, (4) flat-fielding across the MISR imagery (i.e., CCD
detector-based gain errors, which show up as across-track biases in
reflectance), and (5) temporal trends in these quantities. Considerable
effort has been expended to assess MISR radiometric calibration (Chrien et
al., 2001) and to meet the standards of approximately 3 % absolute and
1 % channel-to-channel, established prelaunch. Previous work
involved prelaunch laboratory studies (Bruegge et al., 1999),
on-board-calibrator analysis and lunar calibration, along with vicarious
calibration over bright land targets (Bruegge et al., 2004, 2007, 2014),
symmetry tests comparing the forward and aft-viewing cameras across the solar
equator (Diner et al., 2004), and over-ocean dark target vicarious
calibration (Kahn et al., 2005). Cross-calibration analysis has been
performed over bright and dark land and ocean surfaces with the Moderate
Resolution Imaging Spectroradiometer, that flies aboard the Terra satellite
with MISR (Lyapustin et al., 2007), and MODIS combined with the MEdium
Resolution Imaging Spectrometer (MERIS), the AirMISR (airborne MISR)
instrument, the Landsat-7 ETM
In the RA pre-processing, all MISR L1B2 reflectance data are first averaged
to 1.1 km. The reflectances are then rotated to the L1B1 format, as
described in Limbacher and Kahn (2015), and updated stray-light and
flat-fielding corrections are applied before being rotated back to L1B2
format. Compared to Limbacher and Kahn (2015), we modify the stray-light
corrections in the following way:
The primary ghost term has been divided into a discrete ghosting component
(reflected images of features in the scene) and an unstructured
veiling-light component.
This revised primary ghost has a band-and-camera-dependent along-track
offset applied, as indicated by MISR lunar observations acquired on 14 April
2003 (e.g., Bruegge et al., 2004). The primary ghost image is also stretched or squeezed across-track (for the
near-nadir A cameras only), based on further comparisons with MODIS–Terra,
following the same approach as our earlier work. Via ray tracing, it was found that the “secondary ghosting” term in
Limbacher and Kahn (2015) distributes light uniformly from the left- or
rightmost All stray-light terms are now represented as convolutions, which are much
quicker to compute than applying the functions pixel by pixel as was done in
our earlier work. The magnitudes of all stray-light terms have been adjusted as a result of
adding the unstructured veiling-light component. The stray-light model for the An camera (all four bands) is used for all
off-nadir cameras. Only the along-track offset and primary ghost stretching
are varied by camera.
We then correct for temporal degradation in the MISR calibration (see Appendix A2 below) and revise the band-to-band calibration. We change the band-to-band calibration by increasing the red reflectance 0.75 % and decreasing the near-infrared reflectance 0.75 %, adjustments that are within the calibration uncertainty and are required to match a global set of coincident, spectral aerosol optical depth validation data (Limbacher and Kahn, 2014, 2015). We also apply corrections to the radiance data to smooth out apparent anomalies in the instrument gain (C. Bruegge, personal communication, 2016).
We characterize here temporal trends in the instrument calibration, again
using an empirical image-analysis approach. Bruegge et al. (2014) identified
temporal trends in the MISR bidirectional reflectance factor (BRF; computed
as described in Step 1a below) data, based on a time series of mean BRFs for
a region approximately
The first challenge to performing the temporal trend analysis is finding
suitable homogeneous regions. The following was done to select study regions
within each of the three sites: (a) the spectral
coefficients of variation (standard deviation divided by the mean) were
calculated for rolling
De-seasonalization example for Libya-4. Data are normalized such
that the mean value of each time series is unity. Dashed black lines indicate
Normalized, de-seasonalized TOA BRF time series plots, for the four spectral bands of the MISR Aa camera. Data are normalized such that the mean value is unity. These data present all of the data for the three desert sites used (Libya-1, Libya-4, and Egypt-1), excluding outliers, processed through Step 4b of Appendix A2.
Decadal trend values (in percent) aggregated over three stable desert sites for the 36 MISR channels. The first four rows present the decadal trends for all four MISR wavelengths and nine cameras. The second four rows represent the 95 % confidence intervals (CI) for the corresponding trends. The final row gives the number of events for each camera.
MISR calibration drift per decade (in percent) for all four
wavelengths and nine cameras. The data used to generate this plot were
aggregated from three pseudo-invariant desert sites (Libya-4, Libya-1, and
Egypt-1). The mean decadal trends and the 95 % confidence intervals
(Student's
The central coordinate of each study site is imaged repeatedly by MISR along
at least two distinct paths having different sub-spacecraft ground tracks and
therefore different viewing geometries at the site. (A “path” is one of
233 ground tracks that the Terra satellite covers, repeatedly,
once every 16 days.) Therefore, the
following procedure was applied separately to each path and camera
(6 paths Calculate median patch reflectance for each orbit.
Perform Earth–Sun and solar zenith normalization according to
BRF Calculate the median (and mean) BRF and standard deviation over a region 25 km in radius surrounding the central latitude and longitude coordinate. If the wavelength-maximized coefficient of variation is less than 0.02, save
the median BRF for use in the time series, otherwise discard the data. Median BRF values for at least 193 orbits, and up to 229 orbits, were
retained for 6 paths, 4 spectral
bands, and 9 cameras at this step. Remove outliers for each path/site and spectral
band.
Arrange the saved median BRFs by acquisition date, fit a line to the values,
and subtract the linear trend from the data (to be added back after outliers
are removed and the data are de-seasonalized). Aggregate the data by day of year (DOY) and smooth the sorted, de-trended
BRFs using a 21-point (i.e., Identify BRFs that fall outside 2 Remove the identified outliers from the original data. This step removed 3–14 % of data outliers from each time series. De-seasonalize the data for each site and spectral band.
Fit a line to the original, time-ordered BRFs (for all 13 years), with
outliers removed, and again linearly de-trend the data. Reaggregate the data by DOY and divide the BRFs by their 21-point ( Rearrange the data by time and add back the linear trend from Step 3a. Step 3 is illustrated in Fig. A1 for the Libya-4 site. Normalize the data.
Normalize the data so the time-series mean for each spectral band at each
site is 1.0, which retains the linear trends in each time series but allows
data from multiple sites and paths to be compared. The result is
216 normalized time series, one for each MISR camera and band, for two paths
at each of the three sites. These time series are then aggregated across all paths to produce 36 time
series, one for each MISR channel (Fig. A2).
The linear percent change per decade and its 95th percent confidence interval
are then calculated for each channel, and the results are presented in
Table A1 and Fig. A3. The trends are all negative, as might be expected due
to sensor degradation over time. They are smallest in the blue band for all
but the forward-viewing 70.5
The authors declare that they have no conflict of interest.
We thank Chris Proctor and NASA's Ocean Biology Processing Group for providing the MODIS–Terra ocean color products and the SeaBASS group (and cruise PIs) for compiling and providing their in situ ocean color datasets. We thank our colleagues on the Jet Propulsion Laboratory's MISR instrument team and at the NASA Langley Research Center's Atmospheric Sciences Data Center for their roles in producing the MISR Standard datasets, and we thank Brent Holben at NASA Goddard and the AERONET team for producing and maintaining this critical validation dataset. We also thank Carol Bruegge, Sergey Korkin, and Andrew Sayer for many helpful discussions, and Alexei Lyapustin for providing MODIS code, as well as Andrew Sayer, James Butler, and Carol Bruegge for comments on an early version of the manuscript. This research is supported in part by NASA's Climate and Radiation Research and Analysis Program under H. Maring and NASA's Atmospheric Composition Program under R. Eckman. Edited by: A. Kokhanovsky Reviewed by: A. Lyapustin and two anonymous referees