AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-5741-2018MODIS Collection 6 MAIAC algorithmMODIS Collection 6 MAIAC algorithmLyapustinAlexeialexei.i.lyapustin@nasa.govhttps://orcid.org/0000-0003-1105-5739WangYujieKorkinSergeyHuangDongLaboratory for Atmospheres, NASA Goddard Space Flight Center,
Greenbelt, Maryland, USAUniversity of Maryland Baltimore County, Baltimore, Maryland, USAUniversities Space Research Association, Columbia, Maryland, USAScience Systems and Applications, Inc., Lanham, MD 20709, USAAlexei Lyapustin (alexei.i.lyapustin@nasa.gov)18October201811105741576526April201817May201819September201825September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/5741/2018/amt-11-5741-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/5741/2018/amt-11-5741-2018.pdf
This paper describes the latest version of the algorithm MAIAC
used for processing the MODIS Collection 6 data record. Since initial
publication in 2011–2012, MAIAC has changed considerably to adapt to global
processing and improve cloud/snow detection, aerosol retrievals and
atmospheric correction of MODIS data. The main changes include (1) transition
from a 25 to 1 km scale for retrieval of the spectral regression coefficient
(SRC) which helped to remove occasional blockiness at 25 km scale in the
aerosol optical depth (AOD) and in the surface reflectance, (2) continuous
improvements of cloud detection, (3) introduction of smoke and dust tests to
discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water
processing, (5) general optimization of the LUT-based radiative transfer for
the global processing, and others. MAIAC provides an interdisciplinary suite
of atmospheric and land products, including cloud mask (CM), column water
vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background,
smoke or dust) and fine-mode fraction over water; spectral bidirectional
reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS)
bidirectional reflectance distribution function (BRDF) model and
instantaneous albedo. For snow-covered surfaces, we provide subpixel snow
fraction and snow grain size. All products come in standard HDF4 format at
1 km resolution, except for BRF, which is also provided at 500 m resolution
on a sinusoidal grid adopted by the MODIS Land team. All products are
provided on per-observation basis in daily files except for the BRDF/Albedo
product, which is reported every 8 days. Because MAIAC uses a time
series approach, BRDF/Albedo is naturally gap-filled over land where missing
values are filled-in with results from the previous retrieval. While the BRDF
model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported
for both land and ocean bands 1–12. This paper focuses on MAIAC cloud
detection, aerosol retrievals and atmospheric correction and describes MCD19
data products and quality assurance (QA) flags.
Introduction
Simple and fast swath-based processing with a Lambertian surface model is the basis
of the Moderate Resolution Imaging Spectroradiometer (MODIS) Dark
Target
(DT) (Levy et al., 2013) and VIIRS (Visible Infrared Imaging Radiometer Suite; Jackson et al., 2013) aerosol retrievals
and atmospheric correction (AC) (Vermote and Kotchenova, 2008). In swath data,
the satellite footprint and its location are orbit dependent and change with
scan angle, making it difficult to characterize the surface BRDF. The above
algorithms rely on prescribed spectral surface reflectance (SR) ratios to
make aerosol retrievals. The SR ratios represent statistically average
relationships with relatively large variance. When the surface brightness
increases and sensitivity of the top of atmosphere (TOA) radiance to
aerosols decreases, this lack of accurate knowledge of surface reflectance
becomes a major issue.
The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm
uses a physical atmosphere–surface model where the model parameters are
defined from measurements (Lyapustin et al., 2011a, b, 2012a, b) with minimal
assumptions. Instead of swath-based processing, we start with gridding MODIS
L1B measurements to a fixed 1 km grid and with accumulating a time series of
data for up to 16 days using a sliding window technique. This allows us to
observe the same grid cell over time, helping to separate atmospheric and
surface contributions with the time series analysis and characterize surface
bidirectional reflectance distribution function (BRDF) using multi-angle
observations from different orbits. Besides BRDF retrieval, the fixed
surface representation (grid) allows us to characterize and store unique
surface spectral, spatial, thermal, etc. signatures for each 1 km grid cell,
helping to increase the accuracy of the entire processing, from cloud and snow
detection to aerosol retrievals and atmospheric correction (AC).
Since its introduction in 2011–2012, we have significantly changed and
improved several key parts of MAIAC, namely cloud and snow detection,
characterization of the spectral regression coefficient (SRC) and aerosol
retrieval, and transformed the algorithm from regional to global. The
intermediate versions of MAIAC were continuously tested by the land and air
quality communities using our processing of MODIS data with the NASA Center
for Climate Simulations (NCCS) and product release via NCCS ftp portal
(ftp://maiac@dataportal.nccs.nasa.gov/DataRelease, last access 9 October 2018). Analysis by Hilker et
al. (2012, 2014, 2015), Maeda et al. (2016) and others showed a dramatic (up
to a factor of 3–5) increase in the accuracy of MAIAC surface reflectance
compared to MODIS standard products MOD09, MOD035 over the tropical Amazon.
Since 2014, all major studies of Amazon tropical forests, which used MODIS
data, relied on MAIAC processing (e.g., Saleska et al., 2016; Lopes et al.,
2016; Alden et al., 2016; Guan et al., 2015; Bi et al., 2015, 2016; Jones et
al., 2014; Maeda et al., 2017; Wagner et al., 2017). Recently, Chen et al. (2017) reported an improvement in the leaf area index (LAI) retrievals with
the MODIS LAI/FPAR algorithm when using MAIAC instead of standard MODIS MOD09
input. A high accuracy, high 1 km spatial resolution and high retrieval
coverage made MAIAC aerosol optical depth (AOD) a focus of numerous air
quality studies, e.g., Chudnovsky et al. (2013), Kloog et al. (2014), Just et
al. (2015), Di et al. (2016), Stafoggia et al. (2016), Tang et al. (2017) and Xiao
et al. (2017) to name a few. Currently published validation studies (Martins
et al., 2017; Superczynski et al., 2017) show a high MAIAC AOD accuracy over
American continents and an improved accuracy and coverage over North
America compared to the operational VIIRS algorithm (Superczynski et al.,
2017). An emerging comparative aerosol validation analysis over North
America (Jethva et al., 2018) and southern Asia (Mhawish et al., 2018) shows that MAIAC has a comparable or better accuracy than the DT
algorithm over dark surfaces and generally improves accuracy over the Deep Blue (DB) algorithm (Hsu et al., 2013) over bright surfaces.
The MAIAC MODIS Collection 6 (with enhanced calibration (C6+), which added
polarization correction of MODIS Terra, removed residual trends of both
Terra and Aqua, and cross-calibrated Terra to Aqua (Lyapustin et al.,
2014b) processing is ongoing on the MODIS Adaptive Processing System
(MODAPS). It is expected to be completed in the spring of 2018, creating a new
MODIS product MCD19 accessible via the Land Product Distributed Active Archive
Center (LP DAAC). MAIAC offers an interdisciplinary suite of products for
the Land, Atmosphere, Cryosphere and Applications communities including
cloud/snow mask over land, high spatial resolution (1 km), aerosol optical
depth and type, surface bidirectional reflectance factors (BRF) and BRDF,
and snow grain size and subpixel snow fraction for the snow-covered
regions.
The goal of this paper is to give a systematic description of the MAIAC
Collection 6 algorithm and its products, along with current limitations, and
provide quality assurance discussion and recommendations for use. For
practical reasons, here we focus on MAIAC cloud detection, aerosol retrieval
and atmospheric correction over land; snow detection, over-water processing and smoke plume height retrieval will be described elsewhere. This paper is
structured as follows: an overview of MAIAC processing is given in Sect. 2; Sect. 3 describes the MAIAC radiative transfer model and lookup tables
followed by MAIAC processing components, including cloud/snow detection and
aerosol-type selection (Sect. 4), determination of SRC (Sect. 5) and aerosol
retrieval procedures (Sects. 6–7), shadow detection (Sect. 8) and atmospheric
correction (Sect. 9). Sect. 10 describes the MAIAC (MCD19) product and its
quality assurance (QA) specification.
Overview of MAIAC processing
The block diagram of MAIAC processing over land, which implements a sliding
window algorithm, is shown in Fig. 1:
Block diagram of MAIAC algorithm.
The received L1B data are gridded (Wolfe et al., 1998), split into 1200 km tiles and placed in a queue with the previous data. We are using the
area-weighted gridding method, which achieves better agreement with the
ground tower data over heterogeneous surfaces compared to the nearest-neighbor resampling method (Zhang et al., 2014). The 1 km MODIS bands are
gridded to 1 km resolution, the 500 m bands (B1–B7) are gridded to 1 km and
500 m and 250 m bands (B1–B2) are gridded to 250 m. The 500 and 250 m bands
are nested in the 1 km grid. For convenience, here is the list of MODIS bands
B1–B12, where MAIAC reports BRF at 1 km: 0.645 (B1), 0.856 (B2), 0.465 (B3),
0.554 (B4), 1.242 (B5), 1.629 (B6), 2.113 (B7), 0.412 (B8), 0.442 (B9),
0.487 (B10), 0.530 (B11), 0.547 (B12).
MAIAC uses different spatial scales for processing, e.g., 1 km grid cells
(or pixels), 25 km blocks and 150 km mesoscale areas. Specialized C++
classes and structures handle processing in different time–space scales.
The queue (Q) (Lyapustin et al., 2012a) holds between 4 (at the poles) and
16 (at the equator) days of imagery. For every observation, MODIS data are
stored as layers for the required bands along with the retrieval results. A
dedicated Q memory accumulates ancillary information for each 1 km grid cell
for cloud/snow detection, aerosol retrieval and AC. Q memory stores the
following data at 1 km resolution: the reference clear-sky image for bands
B1, B3, B7 (see Lyapustin et al., 2008); spectral BRDF for bands B1–B8;
BRDF-normalized (to nadir view and solar zenith angle, SZA = 45 ∘)
bidirectional reflectance factors BRFn for bands B1, B2, B7; 2×2standard deviation of 500 m pixels for B1, B3; normalized difference
vegetation index (NDVI); 11 µm brightness temperature (Tb11, band
31); 4 µm (B22)–11 µm (B31) (Tb4-11=Tb4-Tb11) and 11–12 µm (B32) (Tb11-12=Tb11-Tb12) spectral thermal contrasts. Below, we will use notation
q.Tb11 for the queue brightness temperature as an example.
Prior to processing, we compute the covariance of the latest measurements with
the reference clear-sky image (Lyapustin et al., 2008) in bands B3, B1 and
B5 for 25×25 km2 blocks. When covariance is high, indicating
high probability for the confidently clear conditions, we later set the
corresponding flag q.iFlag_HighCov =1 used in the cloud
detection (see test C4, Sect. 4.1).
The column water vapor is computed for the last tile using MODIS near-IR
channels B17–B19 located in the water vapor absorption band 0.94 µm
(Lyapustin et al., 2014a). This algorithm is a modified version of Gao and
Kaufman (2003). It is fast, unbiased and has an average accuracy of ±(5–15) % over the land surface (ATBD; Martins et al., 2017), whereas
the standard NIR CWV product (MOD05) has a known wet bias of 5 %–20 % (e.g., Albert et al., 2005; Prasad and Singh, 2009; Liu et al., 2013).
Column water vapor is retrieved over cloud-free land pixels and over clouds.
In the latter case, it represents water vapor above the cloud.
The cloud mask box includes dynamic land–water–snow classification,
determination of aerosol type (background vs. smoke or dust) and cloud
detection. The smoke/dust test is based on the enhanced shortwave absorption
and effective particle size (Lyapustin et al., 2012c) and requires knowledge
of spectral surface BRDF. At 1 km resolution, brightness and spatial
contrasts of smoke plumes can be high, leading to competition between
smoke and cloud detection. With optimal combination of different cloud tests
and smoke detection, which was found experimentally based on large-scale
MODIS data processing, MAIAC provides aerosol retrievals for most smoke
plumes with minimal cloud leak.
When snow is detected, MAIAC derives surface reflectance using regional
climatology AOD and computes snow grain size (SGS) and subpixel snow
fraction (SF) as best fit to obtained spectral reflectance. The
reflectance is modeled as a linear mixture of snow BRDF and land BRDF, which
is stored in the queue from retrievals prior to snow detection. Snow
reflectance is modeled with a semi-analytical model (Kokhanovsky and Zege,
2004), with consideration for surface roughness (Lyapustin et al., 2010).
Assuming a background soot concentration, the snow reflectance depends only
on SGS, which is computed from measured spectrum along with SF. The
algorithm is analytical, fast and robust due to a large difference between
spectra of regular land types and snow. The derived SGS may have a large
uncertainty because of rapid metamorphism of aging snow and pollution from
the atmosphere, which reduce snow reflectance. We validated SGS retrievals
over pure snow (Lyapustin et al., 2009). The algorithm description and
validation of snow fraction using high-resolution Landsat data will be given
elsewhere.
Using knowledge of spectral BRDF and SRC at 1 km, MAIAC retrieves AOD at
1 km resolution. The following post-processing uses several filters to detect
residual clouds and smooth the noise from gridding uncertainties. This step
significantly increases the quality of AOD and the atmospheric correction.
The combination of MAIAC cloud mask and AOD filters (6) detects majority
of clouds. The next step (7) detects cloud shadows using a geometric approach
and our knowledge of BRDF.
The spectral regression coefficient (SRC) required for
aerosol retrieval is determined.
In cloud-free and clean-to-moderately-hazy (AOD0.47<1.5)
conditions, MAIAC atmospheric correction computes spectral BRF at 1 km resolution (bands B1–B12) and at 500 m resolution (bands B1–B7). By combining
BRF from the latest observation with previous values stored in the queue,
MAIAC performs a BRDF inversion in 1 km bands 1–8, providing three parameters of the
Ross-thick Li-sparse (RTLS) BRDF model (Lucht et al., 2000).
Aerosol layer height for detected smoke pixels is evaluated using a thermal
technique (Lyapustin et al., 2018d).
At the end of the processing, the Q memory is updated for cloud-free pixels
under clean atmospheric conditions.
As ancillary data, MAIAC uses static DEM, a 1 km land–water mask for deep and
static water, and 6 h NCEP ozone and wind speed.
Radiative transfer model and lookup tables
MAIAC radiative transfer (RT) model uses a semi-empirical Ross-thick
Li-sparse (RTLS) BRDF model (Lucht et al., 2000) used in the operational
MODIS BRDF/Albedo (MOD35) algorithm (Schaaf et al., 2002). This is a linear
model, represented as a sum of Lambertian, geometric-optical and volume
scattering components:
ρ(μ0,μ,ϕ)=kL+kGfG(μ0,μ,ϕ)+kVfV(μ0,μ,ϕ).
It uses predefined geometric functions (kernels), fG and fV, to
describe different shapes as a function of view geometry (μ0, μ, ϕ – cosines of solar and view zenith angles, and
relative azimuth). The kernels are independent of the land conditions. The
BRDF of a pixel is characterized by a combination of three kernel weights,
K={kL,kG,kV}T.
MAIAC RT model is based on the semi-analytical Green's function (GF)
solution for the TOA reflectance (Lyapustin and Knyazikhin, 2001). When
combined with the linear RTLS model, the GF solution provides an explicit
expression for the TOA reflectance as a function of the RTLS model
parameters (Lyapustin et al., 2011a):
R(μ0,μ,ϕ)=RA(μ0,μ,ϕ)+kLFL(μ0,μ)+kGFG(μ0,μ,ϕ)+kVFV(μ0,μ,ϕ)+Rnl(μ0,μ).
Here, RA is atmospheric path reflectance. Functions FL,FV,FG,Rnl
depend on view geometry and aerosol properties. They are also weakly
nonlinear functions of K parameters, which account for multiple
reflections of sunlight between the land surface and the atmosphere. This
dependence is analytical and is conveniently handled by the second iteration
during the atmospheric correction. The details for computing functions
Fj, Rnl are given by Eqs. (1)–(25) in Lyapustin et al. (2011a). In brief, they are
expressed via eight basic functions which represent different hemispheric
integrals from downward path radiance, atmospheric Green's function (or
bidirectional upward diffuse transmittance) and RTLS kernels (1, fV,
fG). These functions, along with path reflectance, are precomputed and
stored in the lookup table (LUT). Throughout this paper, AOD refers to the
blue wavelength (AOD0.47).
Equation (2) is used in MAIAC cloud detection, selection of aerosol type and in
the atmospheric correction. For SRC and aerosol retrieval, we also use
Lambertian equivalent reflector (LER) approximation,
R(μ0,μ,ϕ)≅RA(μ0,μ,ϕ)+ρ(μ0,μ,ϕ)Td(μ0)Tu(μ)/1-sρ(μ0,μ,ϕ),
where T is the total downward (d) and upward (u) transmittance, and s is
spherical albedo of atmosphere. Over the water, Eq. (3) is modified to
account for the diffuse reflectance ρw of underlight,
representing water-leaving radiance:
R(μ0,μ,ϕ)≅RA+s(μ0,μ,ϕ)+ρw(μ0,μ,ϕ)Td(μ0)Tu(μ),
where RA+s contains Fresnel reflectance from wind-ruffled ocean surface
and a whitecap contribution (Koepke, 1984) in addition to the atmospheric
path reflectance.
Since Lyapustin et al. (2011a), MAIAC aerosol models and LUTs were
simplified considerably. We abandoned approach of mixing fine- and coarse-aerosol fractions in favor of using regional aerosol models based on AERONET
climatology (Holben et al., 1998) (e.g., Dubovik et al., 2002; Eck et al.,
2013). C6 MAIAC uses eight regional aerosol models and the respective LUTs (see
Sect. 6) over land, including a separate dust model. Since MAIAC processing
is tile-based and inherently regional, it only reads the required regional
LUT or LUTs without overloading operational memory. This allows us to
discretize the world map in sufficient detail to account for the regional
aerosol variability.
Each LUT is computed with full multiple scattering: all functions are first
computed using LUT-generation software based on scalar code SHARM
(Lyapustin, 2005), and the atmospheric path reflectance is then replaced
with vector solution from code IPOL. The discrete ordinates code IPOL was
recognized as the best overall among 10 different vector codes which
participated in the recent intercomparison study (Emde et al., 2015).
Each LUT is generated for the standard P=1 and reduced (P=0.7) pressure
levels (normalized to the standard pressure 1013.25 mB) in order to account
for surface height variations using linear interpolation. Because Rayleigh
optical depth rapidly decreases with wavelength, computations with P=0.7
are done for wavelengths shorter than 0.66 µm.
As before, the spectral gaseous absorption used in the LUT radiative
transfer was obtained based on the line-by-line calculations (Lyapustin,
2003) for MODIS spectral response functions. The computations include
absorption of six major atmospheric gases (H2O, CO2, CH4,
NO2, CO, N2O) calculated for the HITRAN 2008 (Rothman et al.,
2009) database using the Voigt vertical profile and the Atmospheric
Environmental Research (AER) continuum absorption model (Clough et al.,
2005). The LUT is generated for a fixed value of column water vapor, W0=0.5 cm.
In the MODIS red band, where WV absorption is maximal, the atmospheric path
reflectance is also generated for WV = 6 cm, and linear interpolation is used
to account for the WV variations.
For the pressure and WV correction, the surface-reflected signal is
multiplied by the two-way direct transmittance of the well-mixed gases and
water vapor tg(P)t(W0,W):
tg(P)=exp-(1-P)τgm,m=μ-1+μ0-1,t(λ,W)=exp-a(λ,W)mb(λ,W),tW0,W=t(λ,W)/t(λ,W0).
Above, m is an atmospheric air mass, and parameters a and b are obtained by
fitting the angular dependence of the water vapor in-band transmittance.
Expression (5) is a modified form of equation for the broadband
transmittance of water vapor (Schmid et al., 2001).
Finally, LUTs are computed for a relatively sparse angular grid
(Δμ0=Δμ=0.05 for the range μ=0.4–1 (0–66.42∘), μ0=0.15–1 (0–81.37∘) and Δϕ=9∘) and 12 AOD values, {0.05,0.1,0.2,0.3,0.4,0.55,0.75,1.,1.4,2.0,2.8,4.0}, giving the size of
45.7 MB per regional aerosol model. Rayleigh LUT (AOD = 0) is generated
separately.
Ordinarily, generating LUT-based TOA reflectance for shortwave channels
requires two 3-D interpolations in angles at P=1, 0.7, with the following
linear interpolation in pressure for a number of required functions per
pixel. To optimize MAIAC processing, we introduced intermediate-scale
radiative transfer RT containers for 5 km boxes. Each box is characterized by
an average view geometry, mean water vapor and surface pressure,
representing the average height. For each box, we compute the required
functions for 13 AOD LUT nodes. After that, specific MAIAC processing for
any 1 km pixel within a given 5 km box only requires an additional linear
interpolation in AOD using functions from the RT container and an
analytical WV correction for the surface-reflected signal. The 5 km RT containers (RT5) are generated for boxes with cloud-free pixels and
are stored as a layer in the queue. This approach allows us to use the same
RT container repeatedly at different stages of MAIAC processing, which
reduces computational cost by at least a factor of 25. While theoretically
such approach may create biases at short wavelengths (B3 (0.47 µm) and
B8 (0.412 µm)) on the boundaries of boxes with a sharp height gradient, a
very extensive near-global testing did not reveal any noticeable difference
in AOD or surface BRF compared to the accurate 1 km pixel-level interpolation
in view geometry and pressure.
Prior to processing of a new MODIS observation, we compute a spectral
deviation of measurements (M) from the expected theoretical (T) clear-sky
(AOD = 0) TOA reflectance:
δλ=RλM-RλT(τa=0).δλ is computed for five bands (B1, B3, B8, B5, B7) and is
indicative of atmospheric perturbations from clouds and aerosols as
illustrated in Fig. 2 for band B3. Because MAIAC freezes BRDF retrievals when
snow is detected such that the queue BRDF always represents snow-free land
reflectance, δλ also contains spectral signatures of
snow and is used in snow detection.
Illustration of MAIAC time series processing for the
mid-Atlantic USA 250 km region with New York City in the lower-left corner.
The rows show MODIS observations for different days of the year (DOY) for
2012. The two bottom rows show DOY 314 from Terra and Aqua. The columns
present the MODIS TOA RGB image, MAIAC products (cloud mask, AOD0.47, RGB
BRF, column water vapor) and some of the internal fields used in the
processing (deviation from clear-sky δ0.47, cirrus band
reflectance R1.38, thermal contrast, dTb4-11 and its atmospheric
part, dTb4-11A). Columns 3, 5–9 are displayed using the rainbow
palette with the (min–max) values shown in the heading in parentheses. The
cloud mask uses the following legend: cloud (red), possibly cloudy (yellow),
cloud shadow (dark red), clear land (blue), clear snow (white), clear water
(light blue), clear water, detected sediments (grey), glint over water (dark
grey).
Using an estimate of Jacobian in the blue band (B3), ∂R/∂τa≈(RT(0.05)-RT(0))/0.05, it is easy to obtain an
initial assessment of AOD,
τ0a=δ0.47(∂R/∂τa)-1,
which appears quite accurate except over bright surfaces. To guide aerosol
retrievals, we also evaluate a theoretical uncertainty of AOD in response to
uncertainty in the surface reflectance at 0.47 µm which is assumed as
δρ=max{0.002;0.04RTLS(μ0,μ,ϕ)}. This estimate is based on the extensive evaluation of
MAIAC retrieval accuracy, although it may overestimate the uncertainty over
bright surfaces. Given δρ and neglecting other contributions,
e.g., from variation in the aerosol model, the AOD uncertainty is as follows:
δτa=δR(∂R/∂τa)-1,
where δR=RT(0;RTLS+δρ)-RT(0;RTLS) is computed for
the perturbed BRDF. As the surface becomes brighter, the sensitivity of
measurements to aerosol (∂R/∂τa) decreases and AOD
uncertainty grows (Eq. 9). This uncertainty is used in MAIAC as a measure of the
surface brightness guiding the aerosol retrieval algorithm (see Sect. 6.2
and 6.3).
For optimization, the RT5 container is initially filled everywhere for
AOD = 0, 0.05 in order to compute deviation from the clear-sky (Eq. 7) and
evaluate initial AOD and its uncertainty (Eqs. 8–9). The rest of the RT5
container (AOD ≥ 0.1) is filled only for the boxes containing cloud-free
pixels after the detection of reliable clouds (tests C1–C4).
Cloud mask
The cloud mask box in Fig. 1 consists of dynamic land–water–snow
(LWS) classification and cloud mask tests combined with the aerosol-type
selection. MAIAC uses both local (pixel-level) and contextual information
from the surrounding area. The latter comes from the 150 km mesoscale boxes
where we evaluate minimum and maximum values of brightness temperature (Tb11),
reflectance in MODIS cirrus band (B26) r1.38, column water vapor, number
of (internally) detected fire hotspots and the number of previously
detected snow pixels based on the Q information. This nonlocal information
appears very useful, for instance, for choosing more or less conservative
pixel-level snow or smoke detection algorithm, etc.
MAIAC needs to know the state of the surface (land/water/snow/ice) to select
the proper processing path. For this purpose we developed the dynamic
land–water–snow classification (LWSC) from daily observations. It uses
several tests and a decision tree. The LWSC logic and details of snow
processing will be described separately.
The conventional cloud mask algorithms (e.g., Ackerman et al., 1998, 2010)
make cloud detection and classification based on groups of tests
identifying cloud types. As MAIAC does not require cloud typing, its tests
are applied sequentially, and processing terminates as cloud is detected.
The MAIAC cloud mask algorithm is only the beginning of cloud detection, which is
consecutively enhanced by filters following aerosol retrieval and then by
the atmospheric correction component of MAIAC.
Reliable clouds
The first group of tests, which have low interference with the smoke/dust
detection, includes the bright, cold/high and spatial variability tests.
Cloud detection tests are numbered (C).
Bright cloud test. Measured reflectance exceeds theoretical value at maximal LUT AOD = 4 with
a certain threshold:RM>RmaxT+thresh, where thresh=0.1for the(C1)Sahara
region and 0.03 otherwise.The test uses the shortest wavelength MODIS channel B8 (0.412 µm), where
reduction of TOA reflectance by absorbing aerosols (smoke/dust) and the
difference in reflectance with nonabsorbing clouds is maximal.
Cold (high) cloud test. Measured brightness temperature Tb11 is lower by 30∘ or more
than the expected value for this pixel (either q.Tb or a maximal mesoscale
value TbmaxMeso), combined with high cirrus band reflectance
or high thermal contrast dTb4-11:Tb11<283ANDTb11+30<min(q.Tb,TbmaxMeso)(C2)AND(R1.38M>0.03ORdTb4-11>10).The 30∘ difference corresponds to an altitude difference of
∼4.5 km for an average lapse rate of 6.5∘ km-1.
High cloud test. This is for pixels with elevation below 2.5 km.H<2.5kmANDR1.38M>0.035ANDdTb4-11(C3)-q.dTb4-11>5.
Spatial variability test.2×2 standard deviation of 500 m pixels
nested in a 1 km grid cell significantly exceeds the clear-sky threshold
(q.σ) stored in the queue (Lyapustin et al., 2012c):σ>q.σμ+thresh,wherethresh=(q.σ)max.
Here, the multiplier μ approximately accounts for the pixel
growth and higher overlap between scan lines with scan angle and the
resulting reduction of contrast. The threshold (thresh) depends on the surface
variability and represents maximal contrast over a given pixel and its
nearest neighbors. If a fire hotspot is detected in the mesoscale range of
given pixel, the threshold is increased by a factor of 2–3.5 depending on the pixel's
proximity to the hotspot. Also, the threshold is increased in confidently clear
conditions (q.iFlag_HighCov = 1) by a factor of 2 to avoid
false cloud detection over high-contrast areas, e.g., urban areas.
Test (C4) is applied globally over land using MODIS red band B1. It works
well over darker soils and vegetated surfaces, and is successful at capturing
many small popcorn cumulus clouds, which is a major issue and source of
error in remote sensing. Over deserts, the surface is bright in the red
band, and the contrast with clouds is significantly reduced. In these cases,
being selected as q.NDVI<0.2, the test (C4) is repeated for the blue (B3) band
using the fixed threshold thresh=0.012.
Finally, to “clean” the cloud boundaries where the contrast is often
reduced due to lower subpixel cloud fraction, we repeat the above procedure
using the reduced threshold (0.6). This second iteration is applied to
pixels which are direct neighbors of the detected clouds.
Smoke/dust detection
The smoke test described in Lyapustin et al. (2012b, c) uses MODIS red,
blue and deep blue (DB) bands B1 (0.646 µm), B3 (0.47 µm) and B8
(0.412 µm). The developed test (1) isolates atmospheric aerosol
reflectance and (2) compares the measured reflectance at shortest wavelength
(0.412 µm) with that predicted from the red–blue region using the
background aerosol model. For absorbing aerosol containing both black and
brown carbon, the measured aerosol reflectance at 0.412 µm is lower
than predicted due to both (1) more absorption caused by more multiple
scattering at 0.412 µm and (2) increased shortwave absorption (by brown
carbon for smoke and by iron compounds for dust) from increasing imaginary
refractive index at 0.412 µm compared to the red–blue region.
The smoke test first computes an aerosol reflectance in the red, blue and DB
channels by subtracting the Rayleigh (path) reflectance and the full
surface-reflected signal at TOA from the measurement:
RλAer=RλMeas-RλMolec-RλSurf(τa).
The smoke/dust tests are numbered (S). The last term is computed using τ0a (Eq. 8) evaluated with the
background aerosol model and known spectral surface BRDF. Assuming a power-law
spectral dependence, RλAer∼λ-b, we compute
the equivalent Ångström exponent b, or the size parameter (SP) using the red
and blue channels,
SP=R0.646Aer/R0.466Aer,
and the absorption parameter (AP) as a ratio of measured and predicted
aerosol reflectance,
AP=R0.412Aer,Meas/R0.412Aer,Pred,whereR0.412Aer,Pred(S3)=R0.466Aer0.4660.412b.
The idea behind this test is similar to the OMI aerosol index (AI) detection
(Torres et al., 1998, 2007): to the first-order approximation, the clouds,
which have spectrally neutral behavior or nonabsorbing aerosols, would
give AP values close to unity, whereas the absorbing aerosols would
result in lower AP values. Theoretical simulations (Lyapustin et al.,
2012b, c) show a robust aerosol–cloud separation at AOD0.47>0.5 based on AP–SP indices.
As specific aerosol absorption is a function of many parameters, including
the type of burning material and smoldering-to-flaming-fraction ratio for
smoke or mineral composition including hematite content for mineral dust,
we first define the approximate parameterized cloud properties based on
theoretical simulations:
APCloud≅0.97-0.06(2-μ-μ0)andSPCloud(S4)≅1.15+0.15(2-μ-μ0).
Then the smoke/dust tests are implemented based on separation from the
clouds as follows:
IfAPij<APCloud-0.03ANDSPij<SPCloud(S5)ANDdTb4-11-q.dTb4-11<THS→Smoke;IfAPij<APCloud-0.03ANDdTb4-11-q.dTb4-11(S6)>THD→Dust.
As smoke generally does not exhibit thermal contrast, the thermal threshold
is low, THS=1.5 K. This is not true near the fire hotspots: based on
extensive analysis of MODIS data, we parameterized the threshold in this
case as a function of AOD, THS=2.5+0.5AOD.
To detect most dust for the Sahara region where dust is the dominant aerosol
type, the threshold is set to be low THD=1.5; for other dust regions,
the threshold is increased to THD=3.
The dust test (S6) often misclassifies thin cloud edges as dust. For this
reason, we avoid the 2-pixel zone adjacent to the detected clouds and limit
the dust test to the dust regions only (see Sect. 6.1).
Final cloud mask
The final cloud test combines analysis of cirrus band reflectance
R1.38M (B26) and thermal contrast dTb4-11. This test has evolved
during several years of development. Initially, we followed MODIS cloud
detection (Ackerman et al., 2006) and used the cirrus test alone.
Contrary to MODIS, which uses a single global threshold R1.38M>0.035 except in winter and at high elevations, we set a
dynamic threshold as a function of the retrieved column water vapor. This
way, we could decrease the cloud detection threshold down to 0.008, still
well above the noise level in band 26, and detect either very thin cirrus or
lower clouds with partial absorption by water vapor above the cloud. Figure 2 gives an illustration of the cirrus band reflectance showing both high
and weak but spatially coherent signal from lower clouds, which may not be
easily detectable in the RGB bands.
Figure 2 also shows the atmospheric thermal contrast dTb4-11A=dTb4-11-q.dTb4-11. Ackerman et al. (2006) mentions the high information content of
dTb4-11 for cloud detection, but also states that it is hard to use
globally due to its significant variability from the land surface. By
characterizing surface component q.dTb4-11 on clear days, MAIAC can
separate an atmospheric variation dTb4-11A, which significantly
raises information content of this spectral thermal signature for the cloud
detection. Analysis of near-global MODIS data showed that R1.38M
and dTb4-11A usually carry similar information for cloud detection,
but sometimes it is complementary to that of the cirrus channel (see
Fig. 2), so the joint test gives a better cloud detection.
The C6 MAIAC R1.38M-dTb4-11A test works as follows.
Detect clouds with high dTb4-11A:dTb4-11A>thresh,wherethresh=10.The threshold is increased to 14 in two cases. (1) A snow-covered surface
usually has a very low thermal contrast (q.dTb4-11). When snow melts,
exposed bare soil may exhibit a much higher contrast; thus, the threshold
increase helps to avoid a commission error of cloud detection. Snow ablation is
identified when R2.13M>R0.64M and snow have
been detected previously for a given pixel, but was not detected currently.
(2) It is increased under confidently clear conditions (q.iFlag_HighCov = 1).
Detect clouds with high product:dTb4-11A(R1.38M/0.005)>thresh,where 0.005 is close to the noise level of B26, and thresh is set to 25 for the
Sahara region, 15 for bright surfaces (R2.13M>0.3 OR
q.dTb4-11>5) and 6 otherwise. This test is designed for
conditions in which neither the cirrus band reflectance nor the thermal
contrast are high enough to reliably detect clouds, but their product can do
it.
11–12 µm difference dTb11-12A test (dTb11-12A=dTb11-12-q.dTb11-12).
This test is only applied within 2-pixels on
the border of detected clouds. According to Ackerman et al. (2006), the dTb11-12
difference is positive and increases for clouds and decreases for dust,
but not universally. The dTb11-12A test is set as follows:dTb11-12A>0.5ANDdTb4-11A>2.This concludes the tests within the cloud mask block. The following test is
applied during the atmospheric correction routine.
Over dark dense vegetation (DDV), defined as q.NDVI > 0.75, the low
B1 reflectance of the surface, often associated with a high degree of
homogeneity on a 1 km scale, allows for enhanced subpixel cloud detection
during stable surface conditions. This filter consists of two tests: (a) comparison of geometrically normalized B1 reflectance with the queue value
at 1 km:BRFn,B1/q.BRFn,B1>1.35.
Over dense vegetation with the red-band reflectance as low as 0.02–0.03,
this test can detect clouds with a reflectance difference of ∼ 0.007–0.01.
Over homogeneous 1 km DDV pixels defined as q.σB1<0.006,
the nested 500 m pixels should have a similar reflectance to that of the 1 km grid cell. The second test (b) checks measured subgrid variability and
detects subpixel clouds based on high ratio of 500 m BRF in B1 to the 1 km value computed from the RTLS model,ρ500,B1/RTLSB1>1.8.
The thresholds in tests 8–9 were selected based on extensive processing of
MODIS data and have a low commission error.
Spectral regression coefficient
Retrieval of spectral regression coefficient (SRC, box 8), or spectral SR
ratios b37=B3/B7 and b34=B3/B4, is a central component of MAIAC
required for aerosol retrievals. It runs independently and provides
separation between atmospheric and surface contributions.
The C6 MAIAC SRC retrieval has changed completely. The early version
(Lyapustin et al., 2011b) used a multiday minimization for all cloud-free
pixels in the 25×25 km2 area. While this approach was
successful overall, it could generate an occasional random SRC bias for the
whole block, creating AOD “blockiness” at 25 km scale, which further
propagated into the surface reflectance. To resolve this instability, we
developed a new pixel-based approach which is much simpler and gives more
accurate AOD. The new approach uses the minimum reflectance method: SRC
(b37) is found as a minimal ratio of surface reflectance, e.g., ρ0.47∗/ρ2.13∗, over the 2-month period. For each observation, an apparent LER ρ∗ is
computed from TOA measurements using Eq. (3), assuming some regional background
aerosol level, e.g., AOD0.47∼0.05. As the uncompensated
aerosol increases ρ0.47∗ in the blue band,
where most surfaces are dark, selection of the minimal value over time
provides a reliable SRC estimate. This technique is cloud-resistant as
residual clouds increase the ratio ρ0.47∗/ρ2.13∗; however, it is sensitive to undetected
shadows, and therefore SRC retrieval is preceded by shadow detection
(box 7, Fig. 1).
While the minimum reflectance method is a powerful generic technique, it
should be used with caution. For instance, the described algorithm can only
reduce SRC over time; it is also prone to accumulating very low erroneous
values. In reality, seasonal surface change and annual variation of the sun
zenith angle create both upward and downward patterns. As one of the
measures addressing these issues, MAIAC uses two independent lines of the SRC
retrieval (b1 and b2, Fig. 3) starting on odd and even months and
each taking 2 months to re-initialize. This way SRC is updated monthly and
can both decrease and increase over time. The 2-month initialization
period was selected for the MODIS observation frequency to empirically
account for possible periods of high cloudiness and/or high aerosol
concentration. Under favorable conditions, the SRC is updated as soon as the
new minimum is found, along with the update of both lines b1 and
b2. This way, the SRC used in aerosol retrievals can be updated more
frequently than once per month with the new low value and once a month in
the case of an increasing SRC trend.
Schematic illustration of MAIAC dynamic SRC retrievals
featuring two independent lines of update, b1 and b2.
The land surface is considerably brighter at 2.13 µm compared to the
blue wavelength. This results in spectral dependence of the BRDF shape: when
the surface is dark, the BRDF is well defined by the first order of
scattering, whereas in case of a bright surface, the photon can experience
several scatterings on microfacets of the surface roughness before
escaping into the atmosphere, which results in relative flattening of the
BRDF shape. For this reason, SRC depends on the view geometry. To account
for that, MAIAC SRC is computed for three angular bins carefully selected to
optimize aerosol retrievals over bright deserts where the AOD error
sensitivity is maximal. Current bins represent forward scattering (ϕ≤90∘), backscattering (μ<0.95, ϕ>90∘) and nadir direction (0.95<μ≤1, ϕ>90∘). The latter is introduced to
represent regions of the land hotspot for tropics/subtropics and
near-nadir views when the sun is near zenith. A linear interpolation between
bins is used within Δμ≤±0.01 of bin boundaries.
Before using the LER model, we studied the full radiative transfer with
anisotropic surface model where SRC is used to predict the blue-band BRDF
from the BRDF at 2.13 µm. That approach was computationally more
expensive and still required angular binning of SRC. Besides, we found that
it was also sensitive to the B7 BRDF errors over bright surfaces,
occasionally producing AOD outliers. Over bright surfaces, small errors in
the BRDF shape at 2.1 µm can result in relatively large errors in the
surface-reflected diffuse radiance because of high values of the BRDF shape
parameters (kv, kg). The BRDF errors can arise from uncertainties
of gridding, limitations of the RTLS model (not exactly matching the real
distribution), or rarely, unstable RTLS inversions. Using only TOA
measurements at two wavelengths, the LER approach eliminates uncertainty from the BRDF
model-based sources and provides a more stable AOD retrieval
with better AERONET comparison. The current C6 MAIAC uses the LER surface
model for both SRC and aerosol retrievals.
Aerosol retrievalsAerosol models
The geographic distribution of regional background aerosol models over land
used in MAIAC processing is shown in Fig. 4. MAIAC uses eight different models
listed in Table 1. Model properties are given in terms of volumetric size
distribution (e.g., Dubovik and King, 2000) with radius (Rv) and
standard deviation (σv) for the fine and coarse modes, their
ratio of concentrations (CvC/CvF), real (m) and imaginary
(k) refractive index, absorption Ångström exponent (AAE) defined with respect
to spectral dependence of k and spherical (Mie) aerosol fraction. The
imaginary refractive index is assumed to be spectrally dependent at
λ<λ0=0.66µm,k(λ)=k(λ0)(λ/λ0)-AAE and constant for longer
wavelengths. The aerosol models can be either static with fixed parameters,
typical of an arid environment, or dynamic (Remer and Kaufman, 1998) with
parameters depending on AOD. Growth of volumetric radius with AOD represents
hygroscopic growth of aerosol particles associated with AOD increase. It is
typical for regions with moderate-to-high humidity. Model parameters (size
distribution, ratio of volumetric concentrations, refractive index) are
generally representative of the AERONET regional climatology (e.g., Dubovik
et al., 2002) with empirical adjustments aimed at achieving a better match
of retrieved AOD to AERONET sun-photometer data.
Microphysical properties of MAIAC aerosol models: radius and
standard deviation of fine and coarse fractions of bi-lognormal volume size
distribution; ratio of volume concentrations (coarse to fine) as functions of
AOD (τ); real and imaginary refractive index (n= m-ik);
Ångström (AAE) parameter for k (k(λ)=k(λ0)(λ/λ0)-b,
for λ<λ0=0.66µm and k(λ)=k(λ0) for λ>λ0). Finally, the last
column shows the fraction of spherical particles where 1 represents spheres and 0
represent spheroids from the DLS model (Dubovik et al., 2006).
Map of background regional aerosol models specified in
Table 1. The transparent yellow shape approximates the dust regions.
Dynamic Model 1, based on the GSFC AERONET site, represents east coast USA
with high summertime humidity. The more arid climate of the western USA is
represented by Model 2, with some contribution of dust particles and larger
coarse fraction. Model 3, which has high absorption, was developed to model
the polluted environment of Mexico City. The European Model 4 has a higher
absorption, but otherwise is the same as the east coast USA model, Model 1. Model 5,
representing industrial-world China, was developed based on the Beijing
AERONET model with an adjustment for absorption. The India model, Model 8 is
similar to 5 but with higher absorption coming from agricultural biomass
burning (seasonal), cooking and transportation (e.g., Singh et al., 2017).
The biomass-burning cerrado model (Model 7) of subequatorial Africa was
developed based on a AERONET Mongu site. Finally, the desert dust model (Model 6) was
based on Dubovik et al. (2002) climatological model for the Solar Village
site.
The transparent yellow shape in Fig. 4 maps the world region in which the dust
test is conducted and AOD is retrieved with the background or the dust model
depending on the dust test outcome. In the MAIAC C6 version, we still use the
regional background model for aerosol retrieval and atmospheric correction
even if smoke was detected. The next version will use a joint AOD–SSA
(single-scattering albedo) retrieval algorithm for areas with detected
smoke. This algorithm has already been developed and is in the
testing/tuning phase.
Lack of seasonal dependence of aerosol models and LUTs is one of the limitations of MAIAC C6. It does not account for regional aerosol seasonality, for
instance periods of biomass burning and variations in humidity. As a result,
current AOD product may show seasonal biases, for instance over India during
post-monsoon biomass burning (Mhawish et al., 2018). This issue will be
fixed in the next version of MAIAC.
Aerosol algorithm
The aerosol algorithm depends on the brightness of surface, which is
characterized using the uncertainty parameter δτa. Over dark
surfaces (0≤δτa<0.05), the AOD retrieval
routine first evaluates LER in B3 (0.47 µm):
ρ0.47=b37ρ2.13,
and then computes AOD by matching the LUT-based theoretical reflectance to
the measurement,
R0.47T(τa)=R0.47M.
The LER ρ2.13 is obtained by atmospheric correction from the
measurement R2.13M with current AOD used in the aerosol retrieval
loop. However, when smoke/dust is detected, or LER ρ2.13 is
significantly different from the BRDF model value,
ρ2.13<0.5RTLS2.13 or ρ2.13>2RTLS2.13(ρ2.13>1.5RTLS2.13 for bright surfaces when
k2.13L>0.25), which usually indicates undetected clouds
or cloud shadows, we use the BRDF model as LER, ρ2.13=RTLS2.13.
The dark target algorithm (Levy et al., 2013) is prone to overestimating AOD
as surface brightness increases. A typical example of high bias in the VIIRS
aerosol algorithm is given by Fig. 7d from Superczinsky et al. (2017).
While MAIAC implementation is different from the VIIRS (Jackson et al.,
2013), it faces the same general issue. Over brighter surfaces, as
sensitivity of measurements to AOD decreases, the effect of the
surface-related errors increases. The surface-related errors include those from
gridding, from the lagged SRC characterization with the time series method, etc. Over bright land, the SRC error, essentially related to
the change in the average sun angle during the 2-month lag period, becomes
more important over mountainous regions with terrain slope variations.
Statistically, most surface-related errors, including those from gridding,
should be symmetric about zero. However, because we do not accept negative
AOD, the net effect is a positive bias.
One more error source is characterization of the angular dependence of SRC.
As surface brightness increases, the difference in the BRDF shape between
darker 0.47 and much brighter 2.13 µm channels (angular dependence of
SRC) increases. To reduce this effect, we added minimization of the
blue/green band ratio where the surface brightness and BRDF shapes are much
closer. The resulting AOD retrieval is based on minimization of the
following function:
F(τa)=w1(1-R0.47T(τa)/R0.47M)2+w2(1-[ρ0.47(τa)(12)/ρ0.55(τa)]/b34)2,
where the weights of B3/B7 0.47–2.13 µm (w1) and of B3/B4
0.47–0.55 µm (w2=1-w1) are functions of surface brightness
expressed via uncertainty δτa (Eq. 6) as follows:
w1=1 if 0≤δτa<0.05 (dark surface),
w1=0 if δτa<0 or δτa>0.5 (bright surface) and a linear function in between,
w1=(δτa-0.05)/0.45. The reflectance ρλ(τa) in Eq. (12) is LER (result of atmospheric correction) with AOD
τa. The minimization algorithm (Eq. 12) incrementally increases AOD
from the LUT until F(τa) reaches minimum, computes coefficients of
quadratic polynomial based on three points encompassing the minimum and
analytically computes AOD in the minimum of quadratic function.
Our study of independent AOD retrievals using 0.47–0.55 µm ratio (second
term of Eq. 12) shows that it (1) has reasonable accuracy over dark
surfaces albeit somewhat lower than the standard algorithm (Eqs. 10–11),
(2) is more stable over bright surfaces with zero or much lower positive AOD
bias when atmospheric AOD is low and (3) underestimates AOD at high aerosol
loading over all surfaces by as much as 20 %–50 %. Given these properties,
it is clear that the second term of Eq. (12), having lower sensitivity to AOD,
mostly serves to stabilize the solution over brighter surfaces under clean (low
AOD) atmospheric conditions by minimizing high AOD bias from the first term.
When smoke is detected, meaning that AOD is usually sufficiently high and
effect of surface errors is reduced, we give more weight (if
w1<0.8 then w1=0.8) to the standard retrieval (bands
B3–B7) with much higher sensitivity to AOD.
Finally, when dust is detected, the aerosol retrieval adds an additional
term for the MODIS red band B1 and uses equal weights for all three terms:
F(τa)=1-R0.47T(τa)/R0.47M2+1-ρ0.47(τa)(13)/ρ0.55(τa)/b342+1-R0.64T(τa)/R0.64M2.
The theoretical B1 TOA reflectance in the last term is computed using the
accurate GF solution (Eq. 2) with the B1 BRDF model. As one can see from Table 1, properties of the dynamic dust model are such that the concentration of
the coarse mode rapidly grows with AOD, increasing the anisotropy of the phase
function and reducing backscattering. This reduction counteracts and slows
down the respective increase of TOA reflectance. In effect, the dynamic Model 6
requires a significantly higher AOD to match the measured reflectance at
0.47 µm. We found experimentally that algorithm (Eq. 12) significantly
overestimates dust AOD by up to a factor of 2. However, adding the red-band
term (Eq. 13) reduces AOD and significantly improves its accuracy. The
mentioned spectral imbalance of the dust Model 6 may be caused by our use
of the spheroidal model (Dubovik et al., 2006) to approximate dust particles. A
similar spectral-angular mismatch from the use of spheroids to describe
optics of the dust scattering was observed in the analysis of MISR data
(Ralph Kahn, personal communication, 2018).
Lastly, at high altitudes (H>4.2 km, e.g., Tibetan plateau), AOD
is not retrieved unless smoke/dust was detected. Our study shows that in
conditions of very low AOD, nonflat terrain and a generally bright surface,
MAIAC aerosol retrievals at high altitudes are unreliable. Instead of
retrievals, we assume a fixed climatology AODmin=0.02 for the
atmospheric correction.
Bright surface bias correction
Regardless of the specific AOD retrieval algorithm, solutions over bright
surfaces can be unstable and can easily develop a positive bias. It should
be mentioned that the term “bright surface” in MAIAC is understood in
terms of low sensitivity (∂R/∂τa∼0)
or high uncertainty (δτa) of aerosol retrievals. The same
surface can be bright in the backscattering directions, in particular
close to the hotspot because of an increase in the surface reflectance, and
dark for the forward-scattering geometries where the surface is
considerably darker due to shadowing, in combination with higher aerosol
phase function and the single-scattering radiance. MAIAC retrievals show
that AOD is systematically overestimated over some bright surfaces in the
backscattering view directions, correlating with the surface features, which
is apparent in the time series of gridded AOD. These artifacts are generic
and one can easily find them in the MODIS DT, DB and in the VIIRS aerosol
products. As MAIAC deals with the time series analysis of gridded data
directly, we developed a special statistical correction procedure. It is
designed to detect and minimize spatially persistent biases and is only
applied in clear low-AOD conditions to prevent canceling the real aerosol
signal. The idea is to look at the large area, evaluate an average AOD using
the darkest pixels for which the solution can be trusted and correct biased AOD
over bright pixels with a known history of bias using the area-average value.
The bright surface correction procedure is applied to mesoscale areas
(150×150 km2), denoted by Ṙ below, and works as follows:
Compute average AOD for pixels in four bins of uncertainty: δτa≤0.05f, 0.05<δτa≤0.12f, 0.12<δτa≤0.22f and 0.22<δτa≤0.4f.
The AOD retrieval is trustworthy in the first bin and usually trustworthy in the second
bin. The first two bins cover densely vegetated surfaces and dark soils, but
extend to considerably (visually) brighter surfaces at low sun/view zenith
angles and/or high atmospheric turbidity.
The AOD bias generally manifests itself as an increase in the average AOD
with the bin number (uncertainty). In such a case, we define the area-average
value τav based on the first bin or the first two bins depending
on the statistics (the number of pixels in these bins) and set the high AOD
threshold as thresh =τav+0.1.
Mask the pixel in the high bins 2–4 if its AOD (τij) exceeds the
threshold. When a pixel is masked, its cumulative bias counter
(q.indexHighBias) is increased by 1, and the bias index for the current observation
(q.indexCurrentBias) is set to 1.
For pixel (i,j) with current and persistent high bias
(q.indexCurrentBias = 1 and q.indexHighBias > 2), replace AOD with the value τijw+τav(1-w), where the weight increases along with the deviation of pixel's
AOD from the average, w=(τij/τav)-2≤1.
The above procedure is not applied when absorbing aerosols (smoke/dust) are
detected or when τav>0.3, indicating the possibility of
generally higher aerosol levels.
Finally, it should be mentioned that the bias detection can be triggered
randomly for almost any pixel, leading to the accumulation of noise in the
cumulative counter. For this reason, and to avoid canceling the real
aerosol signal over regular pixels, we compute the average bias detection
noise over area Ṙ and subtract it from q.indexHighBias monthly, effectively zeroing it for
the regular pixels.
It should be mentioned that the described algorithm is largely an empirical
summary of numerous trials and errors using AERONET validation and
minimization of systematic spatial and temporal artifacts as our main criteria.
Spatial AOD filtering and smoothing
Cloud tests (Sect. 4) were designed to capture reliable bright, cold, high,
or spatially/spectrally contrasting clouds. Because the natural transition from
clear to cloudy is on a continuum, we use two additional AOD-based filters to
detect thin or subpixel clouds at 1 km resolution, all based on an
assumption that aerosols have some degree of spatial homogeneity. The
filters below were not used if absorbing aerosols (smoke/dust) were detected.
The first filter uses a histogram-based technique following the DT
algorithm (Levy et al., 2007), which applies it to the TOA reflectance in
20×20 500 m pixels' boxes, filtering the lower 20 % and upper 50 %
of data as potentially affected by either shadows or clouds. The average
reflectance of the remaining pixels is used for the DT AOD retrieval. In
MAIAC, we apply a similar technique to 25×25 km2 blocks by using
retrieved AOD and by filtering high values only. The upper threshold is a
function of the cloud fraction (CF) in the block, H=0.65-0.6CF/0.9,
decreasing from 65 % in cloud-free conditions to 5 % when CF=0.9. The
AOD threshold is defined as thresh = AODH+δ, where δ=0.2
when covariance is high (q.iFlag_HighCov = 1), and δ=0.1 otherwise. For pixels with AOD > thresh, the cloud mask value is
set to possibly cloud, CM_PCLOUD.
This filter is not applied when absorbing aerosols (smoke/dust) were
detected, as well as in clear (AODmax<0.35) or homogeneous
(AODmax-AODmin<0.2) conditions. Overall, this filter
rather significantly improves the quality of the final AOD product.
In earlier versions, AOD for the filtered pixels was set to the
FILL_VALUE. The current C6 version reports retrieved AOD for
these pixels for possible applications as a research quality. The main
impetus came from the air quality research groups: in particular, I.
Kloog and A. Just showed that the histogram filter often cancels AOD
retrievals over urban regions with high aerosol spatial variability from
human activity, e.g., the southwestern part of Mexico City. AERONET validation
for Mexico City shows an improvement from added high AOD pixels which were
previously mostly filtered as CM_PCLOUD. A similar
improvement was observed at the Ispra (Italy) AERONET site, located on western
edge of the Po Valley, Italy, where topography and proximity of aerosol
emission sources create conditions for high spatial aerosol variability.
Most of the CM_PCLOUD pixels are located in the transition zone
from clear to confidently cloudy. The high 1 km resolution AOD in this
twilight region (Koren et al., 2007) may be useful for studying the
aerosol–cloud interactions. However, we should emphasize that for most
applications, the user should only use the highest quality AOD with QA cloud
mask value CM_CLEAR.
The second spatial homogeneity filter based on the analysis of
3×3 pixels was proposed by Emili et al. (2011). We use it in the following
form:
find pixel with maximum AOD over 3×3 pixels (AODmax),
compute average AODav in 3×3 area without this pixel,
filter the maximum value (CM_PCLOUD) if AODmax>AODav+0.2.
After detection of residual clouds with filters 1–2, a 3×3 running
averaging window is applied to 1 km AOD, except when smoke/dust were
detected. The averaging serves to ameliorate residual errors of gridding,
which create noise in the surface SRC, BRDF and AOD. As this noise is local
and spatially coherent (due to the systematic nature of MODIS orbits and
footprint size/location with the scan angle), it is effectively reduced by
the 3×3 smoothing filter.
Cloud shadow detection
With cloud detection completed, the next step (7) detects cloud shadows using
geometric analysis (Simpson et al., 2000) and a BRDF reduction test. Based on
the view geometry, we generate the line of shadow for each cloudy pixel,
which is a function of the cloud top height (Hc). Parameter
Hc is evaluated based on the Tb11 contrast between the
cloud-free background (q.Tbs) and a cloudy pixel, assuming an average
adiabatic lapse rate of 6.5∘ K/km (Hc=(Tb11-q.Tbs)/6.5). If the background brightness temperature cannot
be reliably evaluated, we assume a maximal cloud height of 12 km
(Stubenrauch et al., 2010). The shadow is detected along the line of
shadow if surface reflectance in the bright channels B2 (0.87 µm)
or B5 (1.24 µm) falls below the BRDF-predicted value by a certain
threshold. Based on visual evaluation, the developed approach captures up to
80 %–90 % of cloud shadows.
Atmospheric correction
Following cloud detection and aerosol retrieval, the atmospheric correction
derives surface spectral BRF and BRDF models, and computes instantaneous
albedo. The spectral BRF is derived for cloud-free and clear to moderately
hazy (AOD0.47<1.5) pixels by scaling a pixel's BRDF in order to
match the TOA reflectance (Lyapustin et al., 2012a). It uses Eq. (2) modified
as follows:
R(μ0,μ,ϕ)=RA(μ0,μ,ϕ)+cRSurf(μ0,μ,ϕ),
where RSurf is a surface-reflected term computed using the current RTLS
parameters and retrieved aerosol data, and c is the spectrally dependent scaling
factor. Then, the BRF is given by the following:
rλ(μ0,μ,ϕ)=cλρλ(μ0,μ,ϕ),
where ρλ is computed from the RTLS model for a given
geometry. Because RSurf is a nonlinear function of the surface
reflectance, solving Eqs. (14) and (15) takes two iterations. On the second iteration,
the surface term RSurf is computed with scaled BRDF from the first
iteration, cλ(1)ρλ(μ0,μ,ϕ), and
the final scaling coefficient in Eq. (15) is a product cλ=cλ(1)cλ(2). Because the nonlinearity is small, the second
iteration has a very small effect on the final result Eq. (15).
In addition to 1 km spectral BRF in MODIS bands 1–7 is also computed at
500 m resolution nested in a 1 km grid. While MAIAC performs atmospheric
correction of MODIS 250 m bands (B1–B2) as well, these results are not
currently included in the output files.
The RTLS model parameters are retrieved for bands B1–B8 only. Therefore, BRF
in MODIS ocean bands B9–B12 is computed with the same approach (Eqs. 14–15) using
BRDF from the nearest bands; for instance, B4 BRDF is used for B11–B12. The
red band B1 BRDF (instead of B3) is used for AC of B9–B10 in the blue
part of the spectrum. Over dense vegetation, the red-band reflectance is
nearly as low as that in the blue but it is significantly less affected by
the aerosol retrieval errors. So, while the shapes of BRDF are very similar,
the BRDF is somewhat more stable in the red. At the same time, the difference in
the magnitude of reflectance does not matter for the scaling approach
(Eqs. 14–15) as long as the general BRDF shape is right and correctly
models the surface reflection and upward propagation for the direct solar
beam and diffuse (sky) irradiance.
A Lambertian assumption is used during the algorithm initialization period,
which may last from just 4 days (observations) in cloud-free low-AOD
conditions to over a month depending on cloudiness and snow cover. During this
period of time, MAIAC performance is suboptimal with a higher rate of
undetected clouds and reflectance biases from the Lambertian assumption. In the
ongoing C6 MODAPS processing of MODIS Terra and Aqua, MAIAC was initialized
globally using the second half of 2002, and then the processing started from
the beginning of 2000. In parallel, a separate forward-processing stream
using MODIS data from the second half of 2016 to initialize is expected to
start soon.
The latest BRF combined with previous BRFs stored in the queue are used for
the BRDF inversion, providing three parameters of the RTLS model. This represents
a change from the original algorithm (Lyapustin et al., 2012a), which derived
RTLS coefficients by matching the measured TOA reflectance. Using the
original TOA measurements potentially allows a better accuracy of
BRDF retrievals to be achieved, but at the expense of storing or recomputing a number of
RT functions for each past observation from the queue. Our analysis showed
that, with the current high accuracy of MAIAC cloud detection and aerosol
retrieval, the result of much simpler and faster BRF-based inversion is
practically indistinguishable from the TOA-based inversion.
With a C6+ calibration (Lyapustin et al., 2014b), which added MODIS Terra
polarization correction (Kwiatkowska et al., 2008) and response vs. scan (RVS)
trending using quasi-stable desert calibration sites (Sun et al., 2014), and
removed residual trends and cross-calibrated Terra to Aqua, we are using
MODIS Terra and Aqua as one data set. This doubles the MODIS revisit frequency, a
critical requirement for the time series analysis, which significantly helps
MAIAC in all stages of processing, in particular for the BRDF retrieval and
SRC characterization.
MAIAC has a surface change detection algorithm (Lyapustin et al., 2012a)
based on an analysis of geometrically normalized BRF in bands B1, B2, B7. For
instance, normalization to SZA = 45∘ and VZA = 0∘
(F0V=-0.0458621, F0G=-1.1068192 in Eq. 1) at any wavelength
uses the following formula (see Eq. 6 from Lyapustin et al., 2012a):
BRFn=BRF⋅(kL-0.0458621⋅kV-1.1068192⋅kG)(16)/(kL+FV⋅kV+FG⋅kG),
where FV and FG are volumetric and geometric kernels for the MODIS
view geometry provided for the user's convenience in the MAIAC output (see
Sect. 10.2.2). Geometric (or BRDF) normalization significantly reduces BRF variations (by a factor
of 3–6) caused by the changing view geometry of MODIS
with the orbit. MAIAC change detection looks for anticorrelated changes in
the red and NIR bands during the accumulation period, η=ΔBRFn/BRFn,av, where BRFn,av is an average value. Based on
this analysis, the surface state is characterized as stable or having no change
(η<0.05) and two categories of greenup or senescence,
namely regular change (0.05<η<0.15) and big change
(η>0.15, Sect. 10.2.2).
Based on extensive empirical analysis, MAIAC undertakes RTLS inversion when
the surface is relatively stable, η<0.15). When change is
significant (η≥0.15), the BRDF is scaled with the latest
observation to adjust the total reflectance assuming the shape of BRDF does
not change, as in (Schaaf et al., 2002). After inversion, the new BRDF goes
through several tests to verify the correctness of its shape and its
consistency with the previous solution stored in the queue (for details, see
Lyapustin et al., 2012a). In order to preserve consistency and reduce
random noise, we are using an update with relaxation,
K=wKnew+(1-w)Kprev.
Above, the superscript indicates the new and previous solutions, the
weight w=0.5 when surface does not change and w=0.7 for the regular
change. While such an update practice generally improves the quality of the BRDF
model during stable periods, it delays the BRDF model response to the
surface change in addition to the delay related to the length of the queue.
On the contrary, spectral BRFn represents an instantaneous surface
snapshot from the latest observation. For this reason, studies of vegetation
phenology, seasonality, etc. should use BRFn rather than BRDF
model-based reflectance values.
Many applications, including higher-level algorithms for vegetation
characterization, e.g., LAI/FPAR (Chen et al., 2017) and global model
assimilation, require knowledge of uncertainty. We provide the BRF uncertainty (Sigma_BRFn) in MODIS
red (B1) and NIR (B2) bands at 1 km defined as a standard deviation of the
BRFn over the accumulation period of the queue (4–16 days) under the assumption
that the surface is stable or changes linearly in time. This is one of the
most conservative and realistic estimates of uncertainty which includes
contribution from gridding, undetected clouds, errors of atmospheric
correction including those from the aerosol retrieval, and of surface change
when reflectance change is nonlinear over the length of the queue.
Sigma_BRFn in the red band can serve as a proxy of uncertainty at shorter wavelengths,
where the surface is generally darker, and the NIR value can be a proxy for
the longer wavelengths with high surface reflectance.
With the detection of snow, MAIAC freezes the land spectral BRDF in the
Q memory and switches to the snow processing mode, retrieving subpixel snow
fraction and snow grain size. The total surface reflectance (albedo) in this
case is computed as a linear mixture of land BRDF and snow reflectance given
by a semi-analytical model (Lyapustin et al., 2010).
MCD19 data products and quality assurance
MAIAC provides a suite of MODIS atmospheric and surface products in three
HDF4 files: daily MCD19A1 (spectral BRF, or surface reflectance), daily MCD19A2
(atmospheric properties) and 8-day MCD19A3 (spectral BRDF/Albedo). As this paper
describes the first official public release of MAIAC MODIS data, we consider
it useful to provide a brief technical description of MAIAC products and its
quality assurance flags (QA) which is given in the User's Guide in more
detail.
Tiled file structure and naming convention
All products are reported on 1 km sinusoidal grid. The sinusoidal projection
is not optimal due to distortions at high latitudes and off the grid center,
but it is a tradeoff made by the MODIS Land team for the global data
processing. The gridded data are divided into 1200×1200 km2
standard MODIS tiles shown in Fig. 5. The current data set presents data
per orbit. Each daily file name follows the standard MODIS name convention,
for instance:
Illustration of MODIS tiles for the sinusoidal grid.
MAIAC performs processing over green and light blue (land-containing) tiles.
MCD19A1.DayOfObservation.TileNumber.Collection. TimeOfCreation.hdf.
DayOfObservation has the format YYYYDDD, where YYYY is year, DDD is
Julian day. TileNumber has the standard format, e.g., h11v05 for the east
coast USA.
Each daily file usually contains multiple orbit overpasses (1–2 at equator
and up to 30 in the polar regions for combined Terra and Aqua) which
represents the third (time) dimension of MAIAC daily files. The orbit number
and the overpass time of each orbit are saved in global attributes
“Orbit_amount” and “Orbit_time_stamp” sequentially. The Orbit_time_stamp has the format YYYYDDDHHMM[TA], where YYYY is year, DDD is Julian
day, HH is hour, MM is minute, and T and A stand for Terra and Aqua. At high
latitudes, only 16 orbits with the largest coverage are reported per day in
order to limit the file size.
MAIAC products: general description
MAIAC conducts processing over global land tiles and land-containing ocean
tiles (green and light blue colors in Fig. 5).
Over inland, coastal and open-ocean waters, MAIAC reports AOD, fine-mode
fraction and spectral reflectance of underlight (water-leaving radiance).
MAIAC processing over water will be described in a separate publication.
Atmospheric properties file (MCD19A2)
For each orbit, the MAIAC daily MCD19A2 (atmospheric properties) file includes the
following parameters listed in Table 2a. Over land, MAIAC reports the
following parameters at 1 km resolution: AOD at 0.47 and 0.55 µm and AOD
uncertainty evaluated using Eq. (9) for cloud-free and
possibly cloudy pixels; column water vapor (cm) for all
pixels; injection height of smoke plume (in meters above ground); and background
aerosol model used in the retrievals (see Fig. 4). The aerosol type (result
of smoke/dust test) is reported in QA bits 13–14 (aerosol model) of Table 2b.
(a) Reported parameters in the atmospheric properties file
(MCD19A2). (b) AOD QA definition for MCD19A2 (16 bit unsigned integer). N/a is not applicable.
(a) SDS nameScaleDescriptionOptical_Depth_0470.001Blue-band aerosol optical depthOptical_Depth_0550.001Green-band aerosol optical depthAOD_Uncertainty0.0001AOD uncertaintyFineModeFraction0.0001Fine mode fraction over waterColumn_WV0.001Column water vapor (cm)Injection_Heightn/aSmoke injection height (meters above ground)AOD_QAn/aAOD QAAOD_MODEL0.001Regional background model usedSun–view geometry suite at 5 km cosSZA0.0001Cosine of solar zenith angle (5 km)cosVZA0.0001Cosine of view zenith angle (5 km)RelAZ0.01Relative azimuth angle (5 km)Scattering_Angle0.01Scattering angle (5 km)Glint_Angle0.01Glint angle (5 km)(b) BitsDefinition0–2Cloud mask000 – Undefined 001 – Clear 010 – Possibly cloudy (detected by AOD filter) 011 – Cloudy (detected by cloud mask algorithm) 101 – Cloud shadow 110 – Hot spot of fire 111 – Water Sediments3–4Land–water snow/ice mask00 – Land 01 – Water 10 – Snow 11 – Ice5–7Adjacency mask000 – Clear 001 – Adjacent to clouds 010 – Surrounded by more than 8 cloudy pixels 011 – Adjacent to a single cloudy pixel 100 – Adjacent to snow 101 – Snow was previously detected in this pixel8–11QA for AOD over land and water0000 – Best quality 0001 – Water sediments are detected (water) 0011 – There is one neighboring cloud 0100 – There is >1 neighboring clouds 0101 – No retrieval (cloudy or other) 0110 – No retrievals near detected or previously detected snow 0111 – Climatology AOD (0.02): altitude above 4.2 km (Land)/3.5 km (water) 1000 – No retrieval due to sun glint over water 1001 – Retrieved AOD is very low (<0.05) due to glint (water) 1010 – AOD within ±2 km from the coastline (may be unreliable) 1011 – Land, research quality: AOD retrieved but CM is possibly cloudy12Glint mask0 – No glint 1 – Glint (glint angle <40∘)13–14Aerosol model00 – Background model (regional) 01 – Smoke detected 10 – Dust model (dust detected)15Reserved
Over water, we report AOD outside of the glint area. Current processing has
a glint angle cutoff of ≤40∘ as in the DT-over-ocean
algorithm (Levy et al., 2013). When MAIAC detects dust, AOD is also reported
for smaller glint angles when measured TOA reflectance at 1.24 µm (B5)
significantly exceeds reflectance from the ocean surface predicted by the
Cox–Munk model (Cox and Munk, 1954) for a given wind speed. Over the open
ocean and large inland lakes (e.g., Great Lakes of North America), we also
report the fine-mode fraction (FMF). FMF is not retrieved over small inland
water bodies.
In addition to the blue-band AOD (0.47 µm), MAIAC also reports AOD at
0.55 µm, which is computed based on spectral properties of the aerosol
model used in retrievals. It is provided to support the regional and global
chemical transport and climate simulation models, AOD validation and AOD
product intercomparison, all standardized to 0.55 µm. Validation shows
that the quality of AOD at 0.55 µm is generally close though slightly worse
than the original retrieval at 0.47 µm.
Along with the retrieval results, we also provide the sun–view geometry
at 5 km resolution, which includes cosines of solar and view zenith angle,
relative azimuth, and scattering and glint angles, which may be required for
analysis or applications.
The QA structure for MCD19A2 file is presented in Table 2b.
Surface reflectance file (MCD19A1)
For each orbit, the MAIAC daily MCD19A1 (surface reflectance) file includes
parameters shown in Table 3a.
Over cloud-free land and clear-to-moderately turbid (AOD0.47<1.5)
conditions, for solar zenith angles below 80∘, file MCD19A1
reports the surface BRF at 1 km in bands 1–12, and at 500 m in bands 1–7 and BRF
uncertainty (Sigma_BRFn) in MODIS red (B1) and NIR (B2) bands at 1 km. When snow is detected,
we report snow grain size (diameter in millimeters), subpixel snow fraction and
RMSE (Snow_Fit) between MODIS measurements in bands B1, B5, B7 and the linear mixture
model of spectral snow reflectance and land spectral BRDF at 1 km. Following
the sun–view geometry suite at 5 km, MCD19A1 also reports values of
volumetric (Fv) and geometric-optics (Fg) kernels of the RTLS model
for the geometry of observation. The kernels are provided for the ease of
users' geometric (or BRDF) normalization of spectral BRFs using Eq. (16).
One can easily modify normalization to a preferable sun angle according to
latitude or season, by replacing coefficients in the numerator of Eq. (16)
with values from Table 1 in the User's Guide calculated for different solar
zenith angles and nadir view.
(a) Reported parameters in the surface reflectance file
(MCD19A1). (b) Surface reflectance QA definition for MCD19A2 (16 bit
unsigned integer).
(a) SDS nameScaleDescriptionSur_refl[1–12]0.0001Surface reflectance, bands 1–12Sigma_BRFn[1–2]0.0001BRFn uncertainty, bands 1–2Snow_Fraction0.0001Snow fractionSnow_Grain_Size0.001Snow grain diameter (mm)Snow_Fit0.0001Land–snow mixture model RMSE in bands 1, 5, 7Status_QAn/aQA bitsSur_refl_500 m[1–7]0.0001Surface reflectance at 500 m, bands 1–7Sun–view geometry suite (5 km) Fvn/aRTLS volumetric kernel (5 km)Fgn/aRTLS geometric kernel (5 km)(b) BitsDefinition0–2Cloud mask3–4Land water snow/ice mask5–7Adjacency mask8AOD level0 – AOD is low (<=0.6) 1 – AOD is high (>0.6) or undefined9Algorithm initialization status0 – Algorithm is initialized 1 – Algorithm is not initialized10BRF retrieved over snow assuming AOD = 0.050 – no 1 – yes11Altitude > 4.2 km (land)/3.5 km (water), BRF is retrieved using climatology AOD = 0.020 – No 1 – Yes12–14Surface change mask000 – No change 001 – Regular change: greenup 010 – Big change: greenup 011 – Regular change: senescence 100 – Big change: senescence
Over water, MCD19A1 reports diffuse reflectance of underlight (of
water-leaving radiance) in bands 1–12.
Table 3b shows the QA definition for the surface reflectance file. Bits 0–2, 3–4
and 5–7 are the same as in Table 2b. The QA bits 8 and 9 carry additional
information about the quality of atmospheric correction. For instance, better
quality is achieved at low AOD and when the surface BRDF is known (algorithm
is initialized) as opposed to high AOD and/or “not initialized” status
when a Lambertian assumption is used in the atmospheric correction.
Surface BRDF file (MCD19A3)
The 8-day MCD19A3 (BRDF/Albedo) file reports parameters of RTLS BRDF model
(kL, kv, kG) for MODIS bands B1–B8, number of days since the
last RTLS model update (Update_Day) and instantaneous
surface albedo for the overpass time in bands 1–8 at 1 km resolution. These
parameters are listed in Table 4.
Eight-day BRDF model parameters (MCD19A3).
SDS nameScaleDescriptionKiso0.0001RTLS isotropic weight, bands 1–8Kvol0.0001RTLS volumetric weight, bands 1–8Kgeo0.0001RTLS geometric weight, bands 1–8Sur_albedo0.0001Surface albedo, bands 1–8Update_Dayn/aNumber of days since the last updateQuality assurance
In daily output files, the QA reports cloud mask, adjacency mask, surface type
(the result of MAIAC dynamic land–water–snow classification) and a surface
change mask. In general, MAIAC aerosol–surface retrievals are only performed
for cloud-free pixels (QA.Cloud_Mask = Clear) except AOD
which is also reported for the value possibly cloudy. As discussed in Sect. 7, this AOD may
be used with caution in specific well-understood cases, e.g., at high spatial
variability of aerosol or aerosol analysis near clouds. Because most pixels
with QA.Cloud_Mask = possibly cloudy contain residual cloud contamination,
these pixels are not recommended for general use.
Regional linear regression model parameters for the
expected error (RMSE) and bias. NA and SA stand for North
and South America.
Global browse images showing MAIAC AOD (scale 0–2), column water
vapor (scale 0–5 cm), RGB BRF, snow fraction (scale 0–1) and RGB of the isotropic parameter (kL) of
the RTLS model for days 60 (top row) and 230 (bottom row) of 2005.
Adjacency mask gives information about detected clouds or snow in the
±2-pixel vicinity. For most applications, we recommend to only use
data with QA.AdjacencyMask = Clear (000). The value 011 (Adjacent to a
single cloudy pixel) can also be used as it often represents a false cloud
detection. The other categories of Q.AdjacencyMask are not recommended when
using either AOD or BRF products because neighboring clouds or snow increase
possibility of residual cloud/snow contamination of a given pixel, resulting
in overestimation of AOD and respective errors of atmospheric correction.
To select the best-quality AOD, one should use QA.QA_AOD = Best_Quality which combines the best values of cloud and
adjacency masks: QA.CloudMask = Clear and QA.AdjacencyMask = Clear.
For the best-quality BRF, one should apply the following QA filter: QA.AODLevel = low (0), QA.AdjacencyMask = Clear, and
QA.AlgorithmInitializeStatus = initialized (0). We should admit that the current QA structure is not optimal and may be
improved in the future.
An example of MAIAC products
To illustrate MAIAC product suite, Fig. 6 shows the global daily composite
browse images at 20 km resolution for selected products including AOD (0–2),
column water vapor (0–5 cm), RGB BRF, snow fraction (0–1) and RGB of the
isotropic parameter (kL) of the RTLS model, which gives an indication of
spectral BRDF and serves as a proxy of the general surface brightness and
spectral albedo. The numbers in parenthesis give the scale range. The browse
images were generated by the MODAPS Land processing team (Roy et al., 2002)
as part of the product quality evaluation.
Results of global MAIAC AOD validation against AERONET
from the MODIS Terra and Aqua 2000–2016 record.
The browse images are shown for days 60 and 230 of 2005: day 60 shows a
considerable snow cover in the Northern Hemisphere in RGB BRF with the
corresponding high snow fraction; dust storms in the northern Sahara and in
Taklamakan Desert, and high AOD levels in the Indo-Gangetic plane, northern
China and southeastern Asia. In contrast, on day 230 cloud-free observations
show detected snow only over Greenland and polar north, as well as southern
Andes. AOD shows strong forest fires in Alaska and large-scale biomass
burning in southern Amazon with smoke transported southeast across South
America. It also reveals dust storms in Western Sahara and the Thar Desert, and
high aerosol levels in southern Africa. The RGB of RTLS kL parameter is
naturally gap-filled and shows contrasting seasonal dynamics of vegetation
between the northern and southern hemispheres. The column water vapor shows
seasonal, latitudinal and vertical variations, the latter of which is associated with
retrievals above the clouds.
Accuracy assessment of MAIAC AOD
Figure 7 presents results of the global MAIAC AOD validation against AERONET
(Holben et al., 1998) showing correlation coefficient, average bias and
RMSE for individual AERONET sites along with the global scatterplot during
2000–2016. The detailed validation analysis of MAIAC data set, and its
comparison with the standard products from MODIS or other sensors deserves a
separate consideration, so this analysis merely serves to illustrate the
overall quality of MAIAC aerosol retrievals. Figure 7 shows (a) predominantly
high correlation with AERONET except for the world regions where typically
both AOD and its range of variation are low (e.g., southwestern USA or
south of South American continent); (b) globally low bias and RMSE except major
biomass burning, industrial or mineral dust source regions such as Sahara,
Sahel and subtropical Africa, Indo-Gangetic Plane, southern Asia and China.
The higher RMSE in these source regions is typical of all aerosol retrieval
products and is expected due to high variability of aerosol types and
properties, often in combination with the bright land surface increasing
uncertainties of satellite retrievals. The bias shows clustering of results and
gives a clear indication of the required tuning of MAIAC regional
aerosol models, e.g., in southern Asia and China. Some of these biases come from
the seasonal variation in aerosol properties (e.g., Mhawish et al., 2018),
which will be implemented in the next version of MAIAC.
Bias (a) and RMSE (b) of MAIAC AOD for different
world regions as a function of retrieved AOD. NA and SA stand for North
and South America.
Illustration of MAIAC AOD contrast on the boundary of
aerosol models (see Fig. 4) caused mainly by the difference in aerosol
absorption between the models: (a) transition 8–2 (day 82, 2010),
(b) transition 7–6 (day 113, 2010) and (c) transition 7–2 (day 237, 2010).
The global scatterplot of Fig. 7 shows that 66 % of retrievals (grey area)
agree with AERONET within ±0.05±0.1 AOD, which improves over the
standard accuracy assessment of 15 % from the DT algorithm over land
(e.g., Levy et al., 2013). While the global assessment may serve as a useful
indicator of accuracy, the true performance of any algorithm is inherently
regional and local, as shown by R, RMSE and bias statistics for each AERONET site. To
generalize these assessments into regional prognostic error models, we
computed RMSE and bias binned to retrieved AOD for different world regions. These
results are summarized in Fig. 8, where the line shows the mean and the shaded
area represents ± 1 standard deviation. Our analysis and results of
independent studies (e.g., Superczynski et al., 2017; Mhawish et al., 2018)
show that MAIAC AOD has little dependency on view geometry. Although MAIAC
accuracy somewhat decreases over bright surfaces, here the regional analysis
was done for all AERONET sites together. Figure 8 shows that the linear
model for both mean and standard deviation can serve as a reasonable proxy
for both RMSE and bias; for instance
RMSE=a+b×AOD±(α+β×AOD)..
The regional linear regression model parameters are given in Table 5. A more
detailed MAIAC AOD error analysis, as in Sayer et al. (2013), will be given
separately.
Known issues and limitations
Below is a list of currently known issues and limitations of algorithm
MAIAC:
The maximum value of LUT AOD0.47 is 4.0, which limits characterization
of strong aerosol emissions.
MAIAC LUTs are built assuming pseudospherical correction in single
scattering, which has a reduced accuracy for high sun/view zenith angles. A
reduced MAIAC performance is expected at solar zenith angles >70∘.
MAIAC may be missing bright salt pans in several world deserts. In such
cases, it generates a persistently high AOD resulting in missing surface
retrievals.
Geographic AOD boundaries may sometimes be observed on borders of the
regional aerosol models when they have a significant difference in
absorption. While this is not an issue over most of the globe, three
transition zones may stand out during the biomass-burning seasons (see
Fig. 4): the northwest boundary between India (model 8) and central Asia
(model 2), and two transitions from central Africa to Sahel–Sahara (models 7–6) and to southern Africa (models 7–2). Figure 9 shows one the
worst-case examples for each transition zone when at high AOD the contrast across
the model boundary can be as high as 40–50 % of the mean value, while it
is not noticeable for most of the year when AOD is moderate to low.
Because of inherent uncertainties of gridding on the coastline, the area of
±1–3 pixels from the coastline may contain frequent artifacts in
cloud mask (usually overdetection), AOD (higher values) and surface BRF.
Users should exercise caution near the coastline as indicated by
QA.QA_AOD (value 1010).
AC over detected snow: as MAIAC does not retrieve AOD over snow, it assumes
a low climatology AOD is 0.05 globally and 0.02 at high elevations
(H>4.2 km). Over north-central China, which is often heavily
polluted and where low-AOD assumption can lead to a significant bias, we use AOD
averaged over a mesoscale area of 150 km using reliable AOD retrievals over
snow-free pixels. Such an approach does improve the quality of AC compared to
low-AOD assumption as judged by the reduced boundaries and diminished color
artifacts, but it does not account for the aerosol variability inside a 150 km area, which may be significant.
The ice mask is currently unreliable.
A particular type of cloud (moderately thin and
homogeneous cumulus) is consistently missed over water generating high AOD.
MAIAC uses a specialized “bay” mask for aerosol retrievals over coastal
waters with high sediments. The current bay mask misses several these
areas where AOD retrievals often show a high bias.
Since 2014, when MODIS Terra/Aqua calibration was consistently updated
(Lyapustin et al., 2014b), the continued calibration degradation of MODIS
Terra in MAIAC AOD increasingly appears via striping and a positive bias on the left-hand side of the MODIS scan, mostly over bright surfaces. The
MODIS calibration was recently updated. It will be implemented in MODIS
Collection 6.1 Land Discipline reprocessing (which includes MAIAC),
scheduled for the second half of 2018. We expect a significant reduction of
mentioned errors in MAIAC C6.1 AOD.
Conclusions
This paper presented the C6 MAIAC algorithm used in the ongoing MODIS
Collection 6 processing. MAIAC cloud detection, aerosol retrieval and
atmospheric correction over land were described in detail. Being the first
publication of the official new MODIS product MCD19, this paper also
provided the technical specification of MCD19 output files along with the brief
quality assurance discussion and recommendations for use. Other MAIAC
components related to detection and processing of snow, retrievals over
water and smoke plume height retrieval will be described elsewhere.
The paper also presented a brief analysis of near-global MAIAC AOD
validation against AERONET measurements along with error analysis. These
results serve to complement the growing body of the air quality and land
community studies of MAIAC data quality and its comparison to the standard
MODIS products.
The following tools offer options for searching the LP DAAC
(Land Processes Distributed Active Archive Center) data holdings and provide
access to the data:
Bulk download: LP DAAC Data Pool
(https://lpdaac.usgs.gov/data_access/data_pool, last access: 10 October 2018) and DAAC2Disk
(https://lpdaac.usgs.gov/data_access/daac2disk, last access: 10 October 2018)
Search and
browse: USGS EarthExplorer (https://earthexplorer.usgs.gov/, last access: 10 October 2018) and NASA
Earthdata Search (https://search.earthdata.nasa.gov/search, last access: 10 October 2018)
MODIS
Land Global Browse Images: 5 km versions of selected product to enable
synoptic quality assessment
(http://landweb.nascom.nasa.gov/cgi-bin/browse/browse.cgi, last access: 10 October 2018)
AL developed MAIAC code and prepared
the manuscript. YW developed an operational version of MAIAC and conducted
global evaluation and testing. SK supported the computation of MAIAC
lookup tables. DH conducted global validation of the MAIAC aerosol
product.
The authors declare that they have no conflict of
interest.
Acknowledgements
The research of Alexei Lyapustin, Yujie Wang and Sergey Korkin was funded by NASA
Science for Terra, Aqua and SNPP (17-TASNPP17-0116; solicitation
NNH17ZDA001N-TASNPP). Alexei Lyapustin was additionally supported by the NASA
GeoCAPE program. The work of Dong Huang was funded by the NASA DSCOVR program.
We appreciate the large amount of work from the MODAPS team on MAIAC integration, in particular
the support of Ed Masuoka and Sadashiva Devadiga, and the support of LP DAAC. The lasting
support of the NASA Center for Climate Simulations in continental-scale
testing and multiple internal releases of MAIAC data has been invaluable. We
are grateful to the AERONET team for providing validation data. We appreciate
help of Andy Sayer, comments/edits by Jeff Reid and an anonymous reviewer who helped to improve the paper. Lastly, we would like to express gratitude to
multiple users and user groups in the land and air quality communities whose
continuous analysis of MAIAC MODIS data helped to bring MAIAC to its current
level. Edited by: Jun Wang
Reviewed by: Jeffrey Reid and one anonymous referee
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