Launched in December 1999, NASA's Multi-angle Imaging
SpectroRadiometer (MISR) has given researchers the ability to observe the
Earth from nine different views for the last 22 years. Among the many
advancements that have since resulted from the launch of MISR is progress in
the retrieval of aerosols from passive space-based remote sensing. The MISR
operational standard aerosol (SA) retrieval algorithm has been refined
several times over the last 20 years, resulting in significant
improvements to spatial resolution (now 4.4 km) and aerosol particle
properties. However, the MISR SA still suffers from large biases in
retrieved aerosol optical depth (AOD) as aerosol loading increases. Here, we
present a new MISR research aerosol (RA) retrieval algorithm that utilizes
over-land surface reflectance data from the Multi-Angle Implementation of
Atmospheric Correction (MAIAC) to address these biases. This new
over-land and over-water algorithm produces a self-consistent aerosol and surface
retrieval when aerosol loading is low (AOD
Over land, with AERONET AOD (550 nm) direct-sun observations as the
standard, the root mean squared error (RMSE) of the MISR RA combined
retrieval (
Over water, comparing AERONET AOD to the MISR RA combined retrieval
(
In general, better aerosol particle property constraints can be made at lower AOD over water compared to our over-land retrievals. It is clear from the results presented that the new MISR RA has quantitative sensitivity to FMF and SSA (and qualitative sensitivity to non-sphericity) when retrieved AOD exceeds 1, with qualitative sensitivity to aerosol type at lower AOD, while also eliminating the AOD bias found in the MISR SA at higher AODs. These results also demonstrate the advantage of using a prescribed surface when aerosol loading is elevated.
The first of three Along-Track Scanning Radiometer (ATSR) instruments was
launched in July 1991, bringing to the attention of the research community
some of what multi-angle remote sensing offers (e.g., Flowerdew and Haigh,
1995; North et al., 1999). As NASA began to develop its Earth Observing
System in the late 1980s, it also chose to pursue a multi-angle imaging
approach by selecting the Multi-angle Imaging SpectroRadiometer (MISR) as
one of five instruments to be launched on its flagship Terra spacecraft.
MISR was designed to image Earth's surface and atmosphere at nine angles
(70.5, 60.0, 45.6, 26.1
The initial concept for the MISR aerosol and over-land surface retrieval algorithm was developed by Diner and Martonchik (1984a, b, 1985). The method is inherently multi-angle; it assumes that aerosol amount and properties are constant over a retrieval region and uses empirical orthogonal functions (EOFs) in view angle to characterize the directional surface bidirectional reflectance factor (BRF) contributions to the top-of-atmosphere BRF. Implementation of this approach in the operational MISR standard aerosol (SA) retrieval algorithm is described by Martonchik et al. (1998, 2002, 2009). Substantial advances to the SA involved adding a separate process that assumes the shape of the surface angular BRF is independent of wavelength (Diner et al., 2005) and reducing the size of the retrieval regions from 17.6 to 4.4 km (Garay et al., 2020). Still, even with the upgrades described above, the MISR SA continues to show a significant negative bias in AOD when aerosol loading is elevated (Kahn et al., 2005, 2010; Kahn and Gaitley, 2015). In addition to this bias in AOD, it is also likely that SA-retrieved aerosol particle properties are negatively impacted at high AODs over land, as errors in the retrieved surface BRF will likely manifest themselves as errors in both AOD and aerosol type.
Among most EOS-era satellite imagers, aerosol property information is a unique contribution the MISR instrument can make. As such, a research aerosol (RA) retrieval algorithm was developed in parallel with the SA, focused primarily on deriving as much information as possible about particle microphysical properties (e.g., Kahn et al., 2001; Limbacher and Kahn, 2014, 2019). This means the RA includes a broader range of particle optical model options in the algorithm climatology than the MISR SA. It results in more subtle particle property distinctions under favorable retrieval conditions, for example, in smoke and volcanic plumes, when the AOD is sufficiently high (e.g., Flower and Kahn, 2020; Junghenn Noyes et al., 2020). However, especially at low AOD, when particle type discrimination is poorer, having a larger particle-type climatology can increase AOD uncertainty.
Previously, in the RA, the surface was characterized either by
Fresnel-reflecting dark water with whitecaps and under-light contributions
or by a more complex surface specified from external sources (Kahn et al.,
2001; Chen et al., 2008). The MISR RA has also provided validation and
suggested upgrades to the SA. Initial sensitivity studies established that
three to five bins in particle size, two to four bins in particle
single-scattering albedo (SSA), and spherical vs. randomly oriented
non-spherical particle properties could be distinguished from MISR data,
provided the mid-visible aerosol optical depth (AOD) exceeds about 0.15–0.2
(Kahn et al., 1997, 1998, 2001; Kalashnikova and Kahn, 2006). A high bias
in retrieved low AOD values, along with limitations in the MISR radiometric
calibration, the algorithm climatology of particle optical models, and the
surface assumptions in these early algorithms (Kahn et al., 2010) were
subsequently addressed. The advances initially focused on over-water
retrievals. They included modernizing the code, allowing for regional
coverage with pixel-level (1.1 km) retrievals, and improving the particle
optical models, along with better pixel selection, cloud screening, and
uncertainty assessment (Limbacher and Kahn, 2014). The MISR radiometric
calibration applied in the RA was revised based on empirical image analysis,
aimed primarily at improving sensitivity to particle properties (Limbacher
and Kahn, 2015). Further refinements included self-consistently retrieving
aerosol and chlorophyll
The current paper takes a further step in the advancement of the MISR RA by incorporating over-land aerosol retrievals with the surface optical model either retrieved self-consistently within the algorithm or prescribed from the MODerate resolution Imaging SpectroRadiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product (Lyapustin et al., 2018; Lyapustin and Wang, 2018). MAIAC accumulates MODIS observations over 4–16 d (depending on latitude) to produce multi-angle data for the surface retrieval and reports the bidirectional reflectance distribution function (BRDF) at 1 km spatial resolution. The current paper is organized as follows: Sect. 2 describes the RA over-land and over-water retrieval algorithms in detail for both the prescribed and retrieved surfaces. It introduces the bounded-variable least squares (BVLS) approach adopted for the prescribed surface version of the algorithm, a new retrieved surface aerosol algorithm (over both land and water), and modifications to the aerosol optical model climatology and other differences from earlier RA versions. The aerosol quantities reported here are AOD at 550 nm, fine-mode AOD fraction at 550 nm, coarse-mode effective radius (in micrometers), fine-mode effective radius (in micrometers), SSA at 550 nm, brown-smoke AOD fraction at 550 nm (analogous to SSA spectral slope), and non-spherical AOD fraction at 550 nm (Junghenn Noyes et al., 2020). Section 3 presents the results: detailed validation of the over-land and over-water MISR RA retrievals against coincident AERONET sun-photometer data and inversions. Conclusions are given in Sect. 4.
The current MISR RA, presented in this paper, is essentially composed of two sets of retrieval algorithms, both of which derive aerosol loading and properties at 1.1 km resolution: the retrieved surface algorithm retrieves the Lambertian water-leaving radiance over water and applies a spectrally invariant angular shape similarity assumption to derive the surface BRF over land (Diner et al., 2005), whereas the other algorithm prescribes the surface BRF for both land (from MODIS-MAIAC) and water (using a static set of remote sensing BRFs). The MISR top-of-atmosphere (TOA) BRFs used for this study are identical to the set of MISR BRFs used in our 2019 turbid water aerosol retrieval paper (Limbacher and Kahn, 2019) and represent 4 years of MISR data interspersed between 2000 and 2016 (over select AERONET direct-sun aerosol validation sites; Holben et al., 1998).
Modified TOA BRFs (with no solar angle dependence) are computed from the MISR radiance data according to the
following:
Like most operational aerosol retrieval algorithms, the MISR RA uses a pre-built lookup table (LUT) of radiative transfer (RT) output in lieu of running RT code on the fly. Previous versions of the MISR RA relied on either modified linear mixing (Abdou et al., 1997) or external mixing of the phase functions (e.g., Limbacher and Kahn, 2019) to create aerosol mixture analogs from component particle optical analogs represented in our LUT. Although both approaches tend to yield more accurate modeled TOA BRFs at higher AOD, external mixing requires the generation of massive LUTs containing thousands of mixtures to fully account for the range of aerosol properties found in nature, and modified linear mixing requires a significant computational cost to generate reasonably accurate upwelling radiances. To improve our sensitivity to aerosol type, we have built a new LUT of aerosol model components (Table 1) that when linearly mixed with each other should more accurately account for the variability of aerosols seen in nature. This new component LUT contains TOA modeled BRF data as a function of spectral band, solar and viewing geometry, AOD, aerosol optical model (or component), surface pressure (for over-land retrievals), and prescribed 10 m wind speed (for over-water retrievals). The 6-hourly wind speeds are obtained from CCMP v2.0 data (Mears et al., 2019) and are spatially and temporally interpolated to the MISR domain and overpass time. The LUT values are interpolated during the retrieval process to the appropriate solar and viewing geometry, surface pressure, and wind speed.
Microphysical and optical properties of the new RA aerosol component climatology. Column 1 represents the component number, column 2 describes the aerosol analogs, and columns 3–7 represent minimum radius, maximum radius, lognormal characteristic radius, lognormal characteristic width, and effective radius (respectively). Column 8 is the Ångström exponent (calculated using all four MISR bands at 446–867 nm), column 9 is 550 nm single-scattering albedo (SSA), and the last column is the absorption Ångström exponent (AAE, calculated using all four MISR bands at 446–867 nm). Spherical aerosol component optical properties are modeled according to Mie theory, and all components are modeled with a lognormal particle size distribution. BlS corresponds to our black-smoke optical analogs, and BrS corresponds to our brown-smoke optical analogs. Red-colored rows correspond to models used only in the prescribed surface retrievals, whereas the one blue-colored row corresponds to the model only used by the retrieved surface aerosol retrieval. Purple-colored rows correspond to models used in both algorithms.
The pink square on the left shows the bins corresponding to our fine-mode (FM) non-spherical aerosol models; the pink cube in the center left demonstrates how our 10 fine-mode spherical components are organized onto a rectangular grid. The center right blue square shows the two size bins for the coarse-mode non-spherical components, whereas the blue square on the right shows the same two size bins for our coarse-mode spherical components. These four discretized grids are then used to additionally retrieve the fine-mode fraction (FMF) and non-spherical fraction (both at 550 nm).
Because the two sets of aerosol retrieval algorithms diverge from this point, Sect. 2.1.1 describes the prescribed surface algorithm (PSA) and Sect. 2.1.2 delves into the retrieved surface algorithm (RSA).
As the name suggests, the MISR RA prescribed surface algorithm requires
external data on both surface angular spectral BRF and surface
albedo for each individual MISR pixel. The process is summarized as
Fig. S1 in the Supplement. Over water, we assume that the remote sensing
BRF is Lambertian (once glint is subtracted), with the prescribed
remote sensing BRFs given in Sect. 2.1. Because we do not use an
over-water surface BRF database (analogous to MAIAC over land), our
over-water prescribed surface results will likely be prone to error when
aerosol loading is low. However, as described in Sect. 2.1.3 below, the combined
surface algorithm addresses this limitation. Over land, the spectral albedo
and angular dependence come from MAIAC data that are bias-corrected to
remove artifacts that can originate in part from differences between the
MISR and MODIS spectral band passes. A simple linear model was used for
surface BRF (and albedo) corrections in each MISR band, with the
following slopes
As our sensitivity to aerosol particle properties should be enhanced when
optical loading is high, specifically because we are prescribing the surface
BRF, the discrete set of mixtures used by the retrieved surface
algorithm (Sect. 2.1.2) might be insufficient to describe the variability of
aerosols seen in nature. Instead, we convert our component LUT (Table 1)
into four regular grids composed of 10 fine-mode (FM) components and 4
coarse-mode components (as shown in Fig. 1). Rather than retrieve
the non-spherical fraction independently for the fine and coarse modes, we
instead retrieve the total non-spherical fraction for the combined fine
Two coarse-mode grids are also created, one corresponding to spherical
aerosol (at 0.57 and 2.8
Once we have converted our component LUT into four regular grids (fine and
coarse grids, spherical and non-spherical grids), the algorithm then needs a
starting point to begin iterating towards a solution. This initial guess is
set to the following: AOD
We then calculate the derivatives of Eq. (2) with respect to all seven
aerosol-related parameters and set up our linear system of equations. The
weighted linear system of equations
Solving for the change in our retrieved parameter vector
(
Once the algorithm has converged to a solution, it converts the four
particle property grids back into a one-dimensional list of 550 nm aerosol
mixture fraction (for all 17 components), while also reporting 550 nm AOD,
the prescribed surface albedo, and cost. This can be done because our list
of 17 component aerosol particle analogs exactly maps to the bins shown in
Fig. 1. To decrease file size, which is still
Although MODIS-MAIAC-retrieved surface BRF allows the MISR RA to
retrieve AOD and aerosol properties over land when aerosol loading is
elevated, the quality of MISR RA retrievals is negatively impacted when the
MAIAC surface is assumed and aerosol loading is low to moderate (AOD at 550 nm
As in Limbacher and Kahn (2019), we use the same exponential weighted average of discrete
aerosol mixtures (at their best-fitting AOD) to identify aggregate aerosol
and surface properties. However, the discrete aerosol mixtures we use for
this technique have been updated to reflect our new component climatology.
As in Sect. 2.1.1, we break up our components into fine- and coarse-mode
components. Here, we only consider a small subset of the total number of
components for our retrieval. The six fine-mode components used for this
retrieval correspond to component numbers 1, 3, 9, 10, 15, and 16, whereas
the two coarse-mode components are 12 and 17. These components were selected
in a way that allows the algorithm to maintain sensitivity to parameters
such as single-scattering albedo (when AOD is elevated), while acknowledging
that we are unlikely to have sensitivity to fine-mode brown-smoke fraction
for AOD
The addition of an over-land retrieval to the RSA represents a relatively
simple extension and upgrade of our existing over-water retrieval that
allows for shallow, turbid, and eutrophic water, as described in Limbacher and Kahn (2019). For
both the over-land and over-water RSAs, we first redefine the surface
BRF as follows:
To retrieve the surface BRF for any given AOD and aerosol model, we
rewrite our cost function using Eqs. (2) and (4) by applying the
shape similarity assumption (Eq. 5):
For every AOD and aerosol model in our LUT, we first estimate the modified
surface albedo (
For our over-water retrieval, this is the only step required to estimate the
modified surface albedo for a given AOD and aerosol mixture. However, over
land, we must solve for the shape similarity coefficient (
For our over-land retrieval, we then iterate through Eqs. (7) and (8)
twice, as the algorithm typically converged after two iterations (based on
prior experience), which results in further refinement of both
Following Fig. S2 and as summarized above, we retrieve the modified surface
albedo and shape similarity coefficient
for all 104 discrete aerosol mixtures and 26 AODs found in our RT LUT (Table 2). To iterate towards the optimum AOD for each of those 104 aerosol
mixtures, the algorithm also temporarily saves information such as cost
function (104 mixtures
Updated LUT values and dimensionality. Each column lists the values of the variable in the heading that are
included in the LUT. The number of values is given in parentheses at the
top, The overall dimensionality of the LUT is eight, although it is broken
up into a seven-dimensional over-land LUT (no wind speed dimension;
Once the optimum AOD and surface BRF properties have been calculated
for each aerosol mixture, normalized mixture weights are calculated
according to
Over water, this algorithm retrieves seven pieces of information about aerosol
loading and properties as well as four pieces of information about the surface spectral
BRF (
The prescribed and retrieved surface approaches are described in Sects. 2.1.1 and 2.1.2. Over land, the combined surface approach uses PSA AOD from the algorithm described in Sect. 2.1.1 to identify the optimal retrieval type for a given pixel. If PSA AOD is less than 0.75, the CSA selects the AOD and aerosol properties from the RSA. If PSA AOD is greater than 1.5, the combined surface retrieval selects the AOD and aerosol properties from the PSA. If PSA AOD falls between 0.75 and 1.5, the CSA linearly interpolates AOD and aerosol properties between the RSA and PSA. The logic behind this combined surface algorithm is twofold. When aerosol loading is low, errors in the surface BRF based on the PSA tend to produce significant high biases in AOD and errors in aerosol particle properties. Conversely, when aerosol loading is high, the RSA is unable to properly separate the surface and atmospheric contributions, leading to a substantial low bias in AOD (Kahn et al., 2010, among many others). Empirically, we find that this approach with these domain boundaries also yields optimal results when compared to AERONET, as shown in Sect. 3 below.
Over water, the CSA is used with the same AOD constraints as described above. However, because our prescribed surface could be very inaccurate (and result in low-quality aerosol retrievals for the PSA), the algorithm instead uses the RSA AOD (from the algorithm described in Sect. 2.1.2) to determine the algorithm type to be used for the final aerosol result (PSA, RSA, or CSA). Even though the RSA suffers from an AOD low bias at high AOD, the RSA still appears to retain sensitivity to AOD even when AERONET AOD exceeds 3, which makes this algorithm suitable for determining the algorithm type used. Due to the low numbers of high AOD MISR–AERONET coincidences over water, CSA AOD bounds (0.75 and 1.5) may need to be modified when we have more data or if we begin using a surface BRF dataset for our prescribed surface over-water retrievals.
The updated LUT containing RT output was created using SCIATRAN version 3.8
(Rozanov et al., 2014,
The non-spherical dust optical models used in the RA were created following
Lee et al. (2017), except with the MISR spectral bands. The non-spherical dust's phase
matrix (for all spectral bands) is derived by integrating the
single-scattering properties of individual non-spherical particles over both
size and shape distributions. Thus, representative size and shape distributions
as well as the spectral refractive indices for dust are determined from Aerosol
Robotic Network (AERONET; Holben et al., 1998) inversion data at Cape Verde for heavy
dust events (coarse-mode AOD
With hundreds of sites scattered worldwide, AERONET sun photometers directly
measure spectral AOD (Holben et al., 1998) at an uncertainty of
Although AERONET almucantar inversions (Dubovik and King, 2000) represent retrievals of
aerosol properties such as sphericity and SSA rather than direct
measurements, they provide an opportunity to compare with aerosol particle
properties retrieved from imagers such as MISR over diverse regions and
temporal ranges that can span more than a decade. Because almucantar
inversions are performed far less frequently than AOD is sampled, we limit
potential coincidences to within
Comparison of MISR RA over-land 550 nm AOD retrievals with AERONET
direct-sun 550 nm AOD. The
Panel
As explained in the previous section, we use a MISR surface height (from the SA digital elevation model) is within 200 m of the given AERONET station height. At least seven of nine MISR cameras contain valid BRF data. MISR pixels must be masked as land. MISR prescribed and retrieved cost functions are both MISR combined surface AOD is The second derivative of the prescribed surface cost function with respect to AOD is The normalized difference vegetation index (NDVI) using the prescribed surface
albedos is Blue BRF max The MISR retrieved surface AOD standard deviation among all QA pixels is
Quality flag 1 just makes sure that we compare pixels at roughly the same
elevation to each other (as dust and other aerosols tend to be concentrated
in layers) and is only used when comparing AERONET AOD to MISR retrieved
AOD. The reasoning here is that the total column loading will likely differ with surface elevation, but aerosol particle properties
will not vary as much. Quality flag 2 makes sure that a retrieval has enough
“good” input data to give high-quality output, and quality flag 3 uses our
previously computed land–water mask as we are only comparing the land
algorithm to AERONET for the current validation exercise. Quality flag 4
uses the RSA and PSA cost functions to screen out poor-quality (mostly
cloud-contaminated) retrievals. Quality flag 5 indicates that results with a
combined retrieval AOD greater than 9 are likely cloud. As we saw in
Limbacher and Kahn (2019), the second derivative of our cost function can be a good indicator
of retrieval quality. A larger second derivative corresponds to a steeper
minimum in our cost function with respect to AOD; we use 10 as a lower bound
here in quality flag 6 as this tends to mask out some lower-quality results
(mostly clouds). Quality flag 7 primarily masks unmasked water and clouds
using the MAIAC prescribed surface albedos (these are input into the PSA).
Here, NDVI is calculated as follows: NDVI
Applying the flags described in Sect. 3.1 and requiring at least 10
quality-assessed retrievals (out of 2304 potential, from
MISR RA vs. AERONET direct-sun over-land statistics. The rows under “AOD comparison” indicate the type of MISR
retrieval being compared to AERONET. The rows under “ANG comparison”
indicate the MISR RA AOD constraints being placed on the comparison with
AERONET (MISR AOD must be
Figure 2 shows the MISR–AERONET over-land 550 nm AOD comparisons for the
RSA (a and d), PSA (b and e), and the CSA (c and f). Comparisons are plotted in both
linear and log space, as it is easier to evaluate the lower AOD comparisons
with a log–log plot. Additionally, the red lines on the log–log plots
correspond to
Figure 3a presents a larger AOD scatterplot image of the MISR RA CSA with
the different algorithm regimes color-coded. Because the different regimes
are selected based on the MISR prescribed surface AOD (not the combined
AOD), the background color codes are approximate. Comparing to Fig. 2, Fig. 3a
demonstrates that the combined approach picks the best pieces from both
algorithms. This algorithm also eliminates the tendency for the MISR RA (and
the MISR SA) to significantly underestimate AOD when aerosol loading is
elevated. Figure 3b shows that a prognostic error of
Figure 4 compares the MISR CSA ANG to AERONET ANG for MISR-retrieved AOD
greater than 0.05 (panel a), 0.20 (panel b), 0.50 (panel c), and 1.0 (panel d). Statistics for
these plots are also provided in Table 3. Figure 4 (all panels) shows two
clusters of ANG for AERONET, one at
Two-dimensional histograms of the MISR RA combined surface algorithm
over-land ANG compared to AERONET ANG. The
Scatterplots of the MISR RA combined surface algorithm over-land 550 nm particle properties compared to AERONET 550 nm retrieved particle
properties. The
Using the retrieval quality flags indicated in Sect. 3.1 combined with a
4 h averaging window (we still require at least one direct-sun data point
within 30 min of the MISR overpass) and 10-pixel minimum (same as in
Sect. 3.1.1), we found that a significant number of the high-AOD cases
used for our inversion comparison were mistaken as clouds and screened out.
The cause of this excessive masking is due to quality flags 6–8 (Sect. 3.1). For this inversion comparison we now ignore those three flags unless at
least one of those flags is triggered (i.e., condition not met) and
the fraction of good retrievals (out of 2304 potential retrievals) is less
than 0.1. This new method allows us to increase the number of QA cases
designated as “good” with AOD
MISR RA vs. AERONET almucantar inversion statistics over land. Same as Table 3, except for MISR RA vs. AERONET inversion statistics
over land. All MISR data correspond to the combined surface algorithm. Note
that AERONET inversion results are not ground truth; they represent
retrieval results. The AERONET team cautions against the use of results when
blue-band AOD
A comparison of MISR RA 550 nm FMF and AERONET 550 nm FMF is presented in
Fig. 5a (
Even though the current version of the MISR RA now includes fine-mode
non-spherical components, the algorithm tends to dramatically underestimate
the retrieved non-spherical fraction compared to the value retrieved from
AERONET. This is in part due to the mixtures available to the RSA, as the
algorithm is more dominated by fine-mode spherical analogs than either
non-spherical or coarse-mode analogs. Just like FMF, MISR sensitivity to the
non-spherical fraction over land dramatically improves as AOD increases. This is also the case for AERONET inversions, but probably occurs at lower AOD.
Imposing more stringent AOD constraints (
A comparison of MISR 550 nm over-land retrieved SSA and AERONET 550 nm SSA
is also presented in Fig. 5g–i. MISR SSA errors decrease
significantly with increasing AOD, resulting in an RMSE of 0.021 for the 111
coincidences with
We use the same temporal constraints for our over-water AERONET comparison
as were used over land. We apply the following series of tests to help
identify good-quality retrievals (for all The MISR surface height (from DEM) is within 200 m of the given AERONET station
height. At least seven of nine MISR cameras have valid BRF data. MISR pixels must be masked as water. The MISR retrieved surface cost function is The MISR retrieved surface AOD is MISR BRF NDVI (minimized over all nine cameras) of MISR BRFs is (MISR prescribed surface AOD The MISR retrieved surface AOD standard deviation among all QA pixels is
As for our over-land results, quality flag 1 ensures that we compare pixels
at roughly the same elevation to each other and is only used when comparing
AERONET AOD to MISR-retrieved AOD. Quality flag 2 ensures that a retrieval
has enough “good” input data to give high-quality output, which is
especially important over water where up to four cameras could be glint-contaminated. Quality flag 3 uses our previously computed land–water mask to
make sure that a given pixel is water. Quality flag 4 uses the retrieved
surface cost function to screen out poor-quality retrievals. Quality flag 5
screens out pixels with a retrieved surface AOD
MISR RA vs. AERONET direct-sun statistics over water. Same as Table 3, except for MISR RA over water.
Same as Fig. 2, except for the MISR over-water retrieval.
Same as Fig. 3, except for the MISR over-water retrieval.
As in our over-land comparison, we apply the flags listed above and require at least 10 quality-assessed retrievals (pixels) for each AERONET coincidence; otherwise, the spatially averaged MISR results are not included in the statistics. AOD and ANG statistics for the 4596 MISR quality-assessed and AERONET coincidences are shown Table 5.
Figure 6, the over-water equivalent to Fig. 2, presents the comparison of
MISR over-water AOD for all three retrieval types (RSA, PSA, and CSA) as
both a scatterplot in linear space (to emphasize higher AOD results) and
a log–log 2-D histogram (to compare lower AOD results). Specifically, the
MISR RSA over-water retrieval does not suffer from the same level of
degradation in results as AERONET AOD increases (there is still a small low
bias), which is why we use the RSA to identify the bounds for the CSA over
water. Compared to Fig. 2 and Table 3, results appear much more consistent
with AERONET AOD over water than over land, with a combined surface RMSE of
0.063 over water vs. 0.084 over land, MAE of 0.024 over water vs. 0.031
over land, and correlation coefficient of 0.935 over water vs. 0.935
over land. Although there is little improvement in the total statistics
between the RSA and CSA over-water surface results, this may be due to the
very limited number of MISR over-water–AERONET coincidences when AOD is
elevated (
Figure 7a shows the MISR CSA over-water AOD compared to AERONET AOD, with
colored rectangular boxes to indicate the retrieval regime of the MISR
combined retrieval. Figure 7b shows a plot of MISR–AERONET
68th percentile errors as a function of MISR CSA over-water AOD. The
line (
Figure 8 shows the comparison of MISR over-water CSA ANG with AERONET ANG as
a 2-D histogram for the same AOD bins presented in Fig. 4: MISR AOD
Same as Fig. 4, except for the MISR over-water retrieval.
As in our over-land comparison, we use the MISR CSA results with a 4 h
averaging window (requiring at least one valid direct-sun data point) and
10-pixel minimum. Although we are likely eliminating some high-AOD events,
it was not feasible to develop an additional cloud-screening metric to use
for the inversion comparison. The result of our quality assessment is 948
coincidences with MISR AOD
MISR RA vs. AERONET almucantar inversion statistics over water. Same as Table 4, except for MISR RA vs. AERONET inversion statistics
over water. All MISR data correspond to the combined surface algorithm.
Note that AERONET inversion results are not ground truth; they represent
retrieval results. The AERONET team cautions against the use of results when
blue-band AOD
Figure 9a–c show scatterplots of MISR over-water FMF compared to AERONET FMF
for AOD ranges presented in Table 6. MISR over-water FMF statistics are better than
the over-land results for MISR-retrieved AOD
Same as Fig. 5, except for the MISR over-water retrieval. Note the different AOD (compared to Fig. 5) bounds embedded in red.
Figure 9d–f show scatterplots of the MISR over-water non-spherical fraction
compared to the AERONET non-spherical fraction. Unlike with FMF, errors and
correlation improve with AOD for all bins. It is likely that the addition of
multiple non-spherical particle models (now included in both fine and coarse
modes for the MISR RA) is contributing to the improvement in retrieved
non-sphericity with AOD, with RMSE at 0.384 for the 0.2–0.5 bin, dropping to
0.3 for the 0.5–1.0 bin, and dropping further to 0.15 for AOD
Figure 9g–i show scatterplots of MISR over-water 550 nm single-scattering
albedo compared to retrievals of AERONET single-scattering albedo (with the
same AOD ranges as above). Although the correlation is quite a bit lower
than the results over land, RMSE and MAE are better for the water algorithm
compared to the over-land algorithm (for the same AOD bin). For instance,
for over-water AOD
In Limbacher and Kahn (2019), we demonstrated the MISR RA's ability to retrieve AOD and the Ångström exponent over ice-free water of any color (turbid, shallow, eutrophic, etc.). Using the same dataset we used in that study, we develop, test, and present a new version of the MISR RA capable of retrieving aerosol and surface properties over both land and ice-free water. We also test the approach of imposing a prescribed surface BRF at higher AOD using MODIS-MAIAC Ross-Thick Li-Sparse (RTLS) RTLS 8 d surface BRF kernels over land and static values over water. In addition to validating AOD and the Ångström exponent, we dig more deeply into this dataset by evaluating the retrieved fine-mode fraction (FMF), retrieved non-spherical fraction (Non-Sph Fr), and retrieved single-scattering albedo (SSA; all parameters at 550 nm).
Over land, using our combined surface algorithm, the dataset yields 11 563
quality-assessed MISR–AERONET direct-sun coincidences. The MISR RA over-land
550 nm AOD is highly correlated with AERONET 550 nm AOD (
Over water our combined surface algorithm yields 4596 MISR
quality-assessed and AERONET direct-sun coincidences. As with the over-land
retrieval, over-water AOD is highly correlated (
This paper represents the first iteration of a combined MISR RA over-land
All MISR RA validation data used for this paper are available at NASA's Atmospheric Science Data Center (ASDC). The data can be downloaded at
The supplement related to this article is available online at:
Originally developed by RAK, the MISR RA has been a joint effort of JAL and RAK since early 2011. The updated algorithm presented here was developed by JAL (with supervision by RAK), while the paper was produced with input from both JAL and RAK. JL developed the dust aerosol models used for this paper.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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, as well as Brent Holben at NASA Goddard and the AERONET team for producing and maintaining this critical validation dataset. We thank Alexei Lyapustin and the MAIAC team for the MODIS-MAIAC products used in this paper. We also thank the Rozanovs and SCIATRAN team for their work on the SCIATRAN product, as well as Meng Gao, Stefan Kinne, Alexei Lyapustin, and one anonymous reviewer for careful reading and constructive commenting on the paper.
This research has been supported by the National Aeronautics and Space Administration, Earth Sciences Division (Terra Project grant, MISR Project grant, Climate and Radiation Research and Analysis Program grant, and Atmospheric composition Program grant). This research is supported in part by NASA's Climate and Radiation Research and Analysis Program under Hal Maring, NASA's Atmospheric Composition Program under Richard Eckman, and the NASA EOS MISR and Terra projects.
This paper was edited by Otto Hasekamp and reviewed by Alexei Lyapustin, Meng Gao, Stefan Kinne, and one anonymous referee.