Gupta, Pawan

Kleidman, Richard

Levy, Robert

Mattoo, Shana

Munchak, Leigh

Patadia, Falguni

Remer, Lorraine

NASA Technical Reports Server

MODIS Dark Target products 
Collection 6 and onward to Collection 7 
Robert Levy (NASA-GSFC) 
Shana Mattoo, Leigh Munchak, Richard Kleidman (SSAI @ NASA-GSFC) 
Falguni Patadia (GESTAR/Morgan State Univ. @ NASA-GSFC) 
Pawan Gupta (GESTAR/USRA @ NASA-GSFC) 
Lorraine Remer (JCET-UMBC) 
MODIS-STM-2014 Columbia MD:   April 29, 2014 1 
https://ntrs.nasa.gov/search.jsp?R=20140017069 2019-08-31T16:19:18+00:00Z
Aerosol retrieval from MODIS 
OCEAN  
 
GLINT 
LAND 
May 4, 2001; 13:25 UTC 
Level 2 “product” 
AOD 
1.0 
 
 
0.0 
May 4, 2001; 13:25 UTC 
Level 1 “reflectance” 
What MODIS observes Attributed to aerosol (AOD) 
There are many different “algorithms” to retrieve aerosol from MODIS 
1. Dark Target (“DT” ocean and land; Levy, Mattoo, Munchak, Remer, Tanré, Kaufman) 
2. Deep Blue (“DB” desert and beyond; Hsu, Bettenhousen, Sayer,.. ) 
3. MAIAC (coupled with land surface everywhere; Lyapustin, Wang, Korkin,…) 
4. Ocean color/atmospheric correction (McClain, Ahmad, …) 
5. Etc (neural net, model assimilation, statistical, … ) 
6. Your own algorithm (many groups around the world) 2 
Outline 
• Collection 6 (C6) in production 
– Differences from C5 (Level 2) 
– Some preliminary validation (for Aqua) 
– Terra versus Aqua and calibration 
– Level 3 protocol 
– Higher resolution 3 KM  product 
• Maintenance proposal accepted: Towards C7? 
– Corrections of urban surfaces 
– New Uncertainty products (per-pixel) 
– Consistency between Terra and Aqua, and 
continuation onto VIIRS 
• Dark target web page 
3 
MODIS Collection 6: Introduction 
Published in AMT 
 
• Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. 
A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The 
Collection 6 MODIS aerosol products over land 
and ocean, Atmos. Meas. Tech., 6, 2989-3034, 
doi:10.5194/amt-6-2989-2013, 2013.    
 
http://www.atmos-meas-tech.net/6/2989/2013/ 
4 
5 
Overall changes (C6 vs C5): Aqua, 2008 
Jan 
 
 
Apr 
 
 
 
 
Jul 
 
 
 
 
Oct 
0.0                          0.8 -0.1        0.0           0.1 
Aerosol over ocean 
6 
Dark target over ocean  
Overall changes to products (Aqua, Jul 2008) 
7 
• Overall decrease of 
AOD in mid-latitudes 
• Strong decrease in 
“roaring 40s” (even 
stronger in other 
months) 
• Overall increase in 
tropics 
 
 
• “New” coverage over 
inland lakes 
• Increase in coverage 
toward poles 
Why the changes? 
8 
C6-C5 ocean: Due to many incremental changes 
(Aqua, July 2008) 
•Also changed “Quality 
Assurance” Filtering 
•Changed aerosol 
definitions of land and sea 
•Etc 
9 
New reflectance, geo-location 
inputs, Wisconsin cloud mask Updated radiative transfer  
Improved cloud mask Account for wind speed impact on surface 
Re-define land and sea 
Comparison with AERONET and MAN 
10 
• Aqua for 8 months (Jan + July, 2003, 2008 and 2010; Apr + Oct 2008).  
• Overall, not much change over ocean (slope, intercept, correlation)  
• But 30% more valid points to compare with (1141 versus 830). 
• AERONET are gray and colored, MAN are black dots 
 
Better way to see MODIS improvement 
11 
• MODIS error (MODIS–AERONET) versus AERONET; zero “error” is dashed line 
• Boxes represent middle 67% of each dataset, whiskers are middle 95% of MODIS-AERONET 
• Solid lines are “expected error” (EE) envelope; note asymmetry (new definition for C6). 
• Note that in C6, that  the MODIS error is within EE for nearly all bins of AOD 
• C5 EE =  ±(0.03 + 5%). C6 EE = (-0.02 – 10%), (+0.04 + 10%) ) 
• Less overall “bias” in C6.  
See updates (10 years of Aqua) on L. Munchak’s poster! 
Impact on Ångström Exponent 
12 
• AE calculated from 0.55 vs 0.86 m 
• Comparison is for Aqua 
• Overall increase of global AE (+0.18).   
-0.2   0.0       0.5         1.0         1.5   1.8 -0.6      -0.3        0.0       0.3       0.6  
Reasonable validation of AE within ±0.4 
13 See L. Munchak’s poster! 
MODIS “range” is less than AERONET 
Aerosol over land 
14 
Dark target over land 
Overall changes to products (Aqua, Jul 2008) 
15 
• Overall decrease of 
AOD in semi-arid 
• Overall increase over 
vegetation 
• Strong increase over 
Eastern Asia 
 
 
 
• Slight change in 
coverage here and 
there 
Why the changes? 
16 
C6-C5 land: Due to many incremental changes 
(Aqua, July 2008) 
•Also changed “Quality 
Assurance” Filtering 
•Changed aerosol 
definitions of land and sea 
•Etc 
17 
New reflectance and geo-
location inputs Updated radiative transfer  
Improved cloud mask 
to detect smoke 
Fixed surface reflectance 
dependence on TOA NDVI 
Re-define land and sea 
This was a major bug! 
Preliminary comparison with AERONET  
(8 months of Aqua data)  
18 
• MODIS error (MODIS–AERONET) versus AERONET; zero “error” is dashed line 
• Boxes are middle 67% of dataset, whiskers are middle 95% of MODIS-AERONET 
• Solid lines are “expected error” (EE) envelope; no asymmetry 
• C6 MODIS error is within EE for nearly all bins of AOD (even at low values)  
• C5 EE = ±(0.05 + 15%)). Keep definition for C6.  
See updates (10 years of Aqua) on L. Munchak’s poster! 
Terra versus Aqua 
19 
If we had used Collection 5 
• Over land, Terra decreases (-0.04/decade), Aqua constant 
• Terra / Aqua divergence is the same everywhere on the globe! 
• In NH, observations are 1.5 hours apart, while SH are 4.5 hours 
• So, probably not due to diurnal cycle of aerosol 20 
desert test sites
Why? MODIS reflectance over desert sites: C5 
EOS
(1) Collect clear-sky MODIS data over desert sites 
(2) Develop site-specific BRDF from first 3 years of mission 
(3) Over time, compare “observed” reflectance with BRDF 
modeled reflectance, for different view angles 
 
 
 
 
 
 
 
 
 
 
(1) Characterize and de-trend MODIS observations 
(2) Create a new L1B dataset for C6.  
 
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21 
C5 reflectance C6 reflectance 
Impact to “observed” 
reflectance 
22 
reflectance 
Difference reflectance 
• “Global” Aqua changes in 
visible bands by -0.001 or 
less 
• “Global” Terra changes in 
visible bands by +0.002 or 
more 
 
 
• Overall Aqua changes are 
relatively stable, but 
Terra’s changes vary over 
time. 
Impact of New Terra calibration 
• Big changes to blue and red bands 
• Biggest impacts over land 
– Global increase by 0.02 (for this particular month). 10% of global mean!  
• Smaller impacts over ocean 
– Global increase by 0.004 (for this particular month)  23 
Impact of new calibration on trend 
of Terra-Aqua AOD 
• 8 months processed with same dark-target aerosol algorithms 
• Terra now more “in sync” with Aqua time series 
•New calibration  Terra/Aqua divergence removed for C6! 
• (Terra-Aqua) offset remains 0.02 (land) and 0.015 (ocean) 24 
What else for C6 Level 2? 
• Diagnostic SDSs (wind speed, integer QAC, 
topographic elevation, etc) 
• “Cloud mask”, “distance to nearest cloud” 
• Changes to SDS names 
25 
Dark Target AOD  DeepDark AOD  
Deep Blue/Dark Target Merge: 
Merging deep blue & dark target produces best global coverage 
• Deep blue is land-only; need dark target for oceans 
• Deep blue introduces coverage over Australian outback, Sahara desert  
and Arabian peninsula 
• Still no coverage over snow (see: most of Northern Hemisphere). 
  LOOKS REASONABLE, BUT NOT VALIDATED YET!!!!   26 
Beyond MxD04_L2 
 
27 
Changes to Level 3 (MxD08_M3) 
In C5, averaging daily data did not look like monthly 
data (left, from Giovanni web application)  
C5 monthly was “pixel weighted”. A day with 100 
retrieved pixels was worth 10 times more than one 
with 10. It was clear-sky biased.  
 C6 monthly is “equal day” weighted.  If at least five 
pixels in a day, than that day counts.  
  Increases monthly mean AOD over land, and 
ocean. Less clear sky biased? 
C5 C6 C6-C5 
28 
C5 
MxD04_3K (a new 3 km aerosol product)  
• Driven by air quality community,  
• Maybe also some applications to aerosol/clouds.  
• Currently Dark target only 
29 
3 km 10 km Aqua: Day 209 2010 
Munchak, L., R.C. Levy, S. Mattoo, L.A. Remer, B.N. Holben, J.S. Schafer, C.A. Hostetler, and R.A. Ferrare (2013). 
MODIS 3km Aerosol Product: applications over land in an urban/suburban region Atmos. Meas. Tech, 6, 1747-1759, 
doi:10.5194/amt-6-1747-2013 
 
Remer, L., S. Mattoo, R.C. Levy, and L. Munchak (2013). MODIS 3km Aerosol Product: Algorithm and Global 
Perspective Atmos. Meas. Tech, 6, 1829-184, doi:10.5194/amt-6-1829-2013 
 
J. M. Livingston, J. Redemann, et al, (2013). Comparison of MODIS 3-km and 10-km resolution aerosol optical depth 
retrievals over land with airborne Sunphotometer measurements during ARCTAS summer 2008, Atmos. Chem. 
Phys. Disc,  
MxDATML2 product 
• Combines the “best of” 
MxD04_L2 (10 km) 
aerosol, MxD06_L2 (5 
km) cloud products, 
and other atmosphere 
prods 
• For joint analyses of 
aerosols and clouds (at 
granule level 
30 
From MxD06 (clouds) 5 km:  
• Latitude 
• Longitude  
• Cloud_Optical_Thickness 
• Cloud_Optical_Thickness_Uncertainty 
• Cloud_Optical_Thickness_PCL 
• Cloud_Optical_Thickness_16 
• Cloud_Optical_Thickness_16_PCL 
• Cloud_Optical_Thickness_37 
• Cloud_Optical_Thickness_37_PCL 
• Cloud_Optical_Thickness_Uncertainty_16 
• Cloud_Optical_Thickness_Uncertainty_37 
• Cloud_Effective_Radius 
• Cloud_Effective_Radius_Uncertainty 
• Cloud_Effective_Radius_PCL 
• Cloud_Effective_Radius_16 
• Cloud_Effective_Radius_16_PCL 
• Cloud_Effective_Radius_37 
• Cloud_Effective_Radius_37_PCL 
• Cloud_Effective_Radius_Uncertainty_16 
• Cloud_Effective_Radius_Uncertainty_37 
• Cloud_Water_Path 
• Cloud_Water_Path_Uncertainty 
• Cloud_Water_Path_PCL 
• Cloud_Water_Path_16 
• Cloud_Water_Path_16_PCL 
• Cloud_Water_Path_37 
• Cloud_Water_Path_37_PCL 
• Cloud_Water_Path_Uncertainty_16 
• Cloud_Water_Path_Uncertainty_37 
• Cloud_Optical_Thickness_1621 
• Cloud_Optical_Thickness_Uncertainty_1621 
• Cloud_Effective_Radius_1621 
• Cloud_Effective_Radius_Uncertainty_1621 
• Cloud_Water_Path_1621 
• Cloud_Water_Path_Uncertainty_1621 
• Cloud_Phase_Optical_Properties 
• Cloud_Quality_Assurance 
• Cirrus_Reflectance 
• Cloud_Top_Pressure 
• Cloud_Top_Temperature 
• Cloud_Top_Height 
• Cloud_Height_Method 
• Cloud_Top_Pressure_1km 
• Cloud_Top_Temperature_1km 
• Cloud_Top_Height_1km 
• Surface_Temperature_1km 
• OS_Top_Flag_1km 
• Infrared_obs_minus_calc 
• Cloud_Mask_SPI 
• Cloud_Multi_Layer_Flag 
• Cloud_Fraction 
• Cloud_Phase_Infrared 
• Cloud_Phase_Infrared_1km 
From MxD04 (aerosol) 10 km:  
• Latitude_10km 
• Longitude_10km 
• Solar_Zenith_10km 
• Viewing_Zenith_10km 
• Relative_Azimuth_10km 
• Aerosol_Optical_Depth 
• Aerosol_Angstrom_Exponent_Ocean 
• Aerosol_Land_Sea_Flag 
• Aerosol_Cloud_Pixel_Distance_Land_Ocean 
• Aerosol_Cloud_Fraction_Ocean 
• Aerosol_Cloud_Fraction_Land 
• Aerosol_Land_Ocean_Quality_Flag 
• AOD_550_Dark_Target_Deep_Blue_Combined 
• AOD_550_Dark_Target_Deep_Blue_Combined_QA_Flag 
• AOD_550_Dark_Target_Deep_Blue_Combined_Algorithm_Flag 
• Deep_Blue_Aerosol_Optical_Depth_550_Land 
• Deep_Blue_Angstrom_Exponent_Land 
• Deep_Blue_Single_Scattering_Albedo_412_Land 
• Deep_Blue_Aerosol_Optical_Depth_550_Land_Best_Estimate 
• Deep_Blue_Aerosol_Optical_Depth_550_Land_QA_Flag 
• Deep_Blue_Aerosol_Optical_Depth_550_Land_Uncertainty 
• Aerosol_Quality_Assurance_Land 
• Aerosol_Quality_Assurance_Ocean 
From MxD05 (precip water) 10 km:  
• Precipitable_Water_Infrared_ClearSky 
Precipitable_Water_Near_Infrared_ClearSky  
From MxD35 (Cloud Mask) 5 km:  
• Cloud_Mask 
From MxD07 (Profiles) 5 km:  
• Total_Ozone 
• Lifted_Index 
• K_Index 
• Total_Totals_Index 
Platnick, King, Hubanks,..  
Towards collection 7 
• Accounting for bias over urban areas 
• Determining per-retrieval uncertainty 
• Residual calibration/polarization errors 
 
 
 
31 
Accounting for Urban bias 
• Can we reduce artificial urban hotspots without impacting 
surrounding rural areas?   
32 
More urban --> 
higher bias 
 
Over MD/DC 
during 
DISCOVER-AQ 
Looking at 
possible 
corrections 
 
Applied to E-USA 
over 2010 
United States: 2002-2010 Aqua 
C6 Retrieval  Retrieval using Urban fix  
At least over the U.S, we can correct the positive urban bias.    
See Pawan Gupta’s poster!  
Characterizing uncertainty in Aerosol Optical Depth Retrieval 
There are two broad error sources :
 
1. Measurement / Input Uncertainties
 
• Calibration Uncertainty [1 – 2%] 
• Standard Deviation of reflectance in 10 x 10 km retrieval box [1 – 2%] 
• Uncertainty in the Ancillary data used for atmospheric correction [~3.5%] 
• Cloud contamination [  Bias of +0.04 (Terra) and +0.01 (Aqua), Hyer et al.,2011] 
• Snow contamination 
 
2. Retrieval Assumptions
 
• Surface reflectance 
• Aerosol models 
See Falguni Patadia’s poster!  
Absolute AOD Error Relative to AOD 
AOD differences due
to standard deviation
of reflectance within
10 X 10 km box
Compared to EE
envelope
MODIS L1B RGB Image over Central  Africa 
11 JULY 2010 
Example :  Uncertainty in AOD retrieval from Reflectance Standard Deviation 
MODIS AOD (554 nm)  
0 1.0 
Absolute Error in AOD 
0 0.1 See Falguni’s poster!  
Residual calibration/polarization errors 
• Dealing with “striping” of 0.1 AOD in recent (post 2012) Terra data 
• Seems to be a mirror polarization sensitivity issue.  36 
Image over Japan 0.0        0.2         0.4       0.6       0.8       
Hear Alexei 
Lyapustin’s talk! 
Dark-target aerosol retrieval:  
Beyond MODIS 
37 
VIIRS versus MODIS 
Orbit: 825 km (vs 705 km), sun-synchronous, over same point every 16 days 
 Equator crossing: 13:30 on Suomi-NPP, since 2012 (versus on Aqua since 2002) 
Swath: 3050 km  (vs 2030 km) 
Spectral Range: 0.412-12.2m (22 bands versus 36 bands) 
Spatial Resolution:  375m (5 bands) 750m (17 bands): versus 250m/500m/1km 
Wavelength bands (nm) used for DT aerosol retrieval: 482 (466), 551 (553) 671 (645), 861 (855), 
2257 (2113)  differences in Rayleigh optical depth, surface optics, gas absorption.  
Aerosol Retrieval: Created and maintained by scientists partnered with NOAA (NASA), with a strategy 
of maximizing environmental data record - EDR (climate data record – CDR) 
ALSO: Different cloud masks, different aggregation techniques, different pixel selections.  
Suomi-NPP (13:30 Local Time, Ascending);   Aqua (13:30 Local Time, Ascending) 
38 
Different instrument, resolution, sampling, cloud masking, algorithms, etc.  
Will VIIRS “continue” the MODIS aerosol data record?  
ONE RETRIEVAL ALGORITHM:  Consistent Across Platforms 
39 
Different SZA 
thresholds 
Different snow 
thresholds? 
? 
Still different, but 
much more 
similar over both 
land and ocean 
We make VIIRS consistent with MODIS.   
(we learned from Terra vs Aqua)   
MODIS C6 product NOAA-VIIRS product 
MODIS-like on VIIRS 
MODIS-like algorithm on ANY sensor!  
For climate continuity 
• MODIS 
• VIIRS 
• MAS/E-MAS/AMS (Airborne spectrometers and 
historical experiment data) 
• International sensors  
• Future sensors (e.g. PACE / ACE) as a baseline for 
testing new ideas 
 
 
Many details, but can be done! 
Okay: summary time 
41 
Summary (C6) 
• There are many ways to retrieve aerosol properties from 
MODIS, and there is more than one set of 
algorithms/products 
• Dark-target algorithm/products updated for C6 
• Changes are “modest” but lead to significant changes in 
retrieved global aerosol 
• New products: DB/DT merge, MxD04_3K, etc 
• Documentation:  
– Algorithm papers have been published 
– ATBD in progress  
– Website under development 
• C6 processing (Level 2) for Aqua almost finished. Terra begin 
soon? Level 3 soon? 
• Validation (vs AERONET, MAN, etc) in progress 
• Calibration/polarization/trending issues still being studied 
42 
Summary (Towards C7) 
• Corrections for urban surface bias 
• Development of “pixel level” uncertainty 
products 
• Calibration/polarization/trending issues still 
being studied 
– Why is Terra offset from Aqua?  
• Development of generic dark-target algorithm 
to be used on VIIRS, airborne and other spectral 
remote sensing datasets. (a Super C7).  
43 
http://darktarget.gsfc.nasa.gov 
• Web site in development 
• Reference for all things “dark target” 
– The algorithms and assumptions 
– Examples 
– Validation  
– Primary publications 
– Educational material 
– FAQ 
– Links to data access 
– Considering a “forum” 
44 


MODIS Dark Target Products Collection 6 and Onward to Collection 7

Abstract

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