Bettenhausen, Corey

Carletta, Nick

Hsu, Nai-Yung Christina

Lee, Jaehwa

Sayer, Andrew Mark

Seftor, Colin

Warner, Jeremy

NASA Technical Reports Server

Recent progress in the NASA
‘e-Deep Blue’ algorithm for remote 
sensing of aerosol optical properties
Andrew M. Sayer, N. Christina Hsu (PI), Corey Bettenhausen, Nick Carletta,
Jaehwa Lee, Colin Seftor, Jeremy Warner
Past team members: Ritesh Gautam, Jingfeng Huang,
Myeong-Jae Jeong, Becky Limbacher, Clare Salustro
With acknowledgements to many others: MODIS Characterization Support Team, AERONET,
MODIS Dark Target group, Ocean Biology Processing Group
Climate & Radiation Laboratory, NASA Goddard Space Flight Center
andrew.sayer@nasa.gov
https://ntrs.nasa.gov/search.jsp?R=20140017839 2019-08-31T14:26:28+00:00Z
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
2A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
3A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
Deep Blue: original motivation
• MODIS ‘Dark Target’ AOD algorithm 
does not retrieve over bright surfaces
• These are important aerosol source 
regions
• Deep Blue initially filled in some 
gaps
– Now, it does more than that: 
extended/enhanced e-Deep Blue
4A Sayer- AEROCOM 2014
Deep Blue: key concepts
• Often, better surface/aerosol 
contrast in the violet/blue (~400-
490 nm) than longer wavelengths
– Prescribe surface reflectance
– Retrieve AOD independently at 
several wavelengths
• Advantages:
– Avoids regional/seasonal artefacts
arising from e.g. global surface 
models
– Applicable to many sensors
• Disadvantages:
– Departures from expected surface 
cover can lead to artefacts in 
instantaneous data
– Cannot directly back out e.g. 
aerosol effective radius or mass
Figure from Hsu et al., IEEE TGARS (2004)
5A Sayer- AEROCOM 2014
Sensors Deep Blue has been applied to
1997 2000 2002 2010 2011 Present ?
SeaWiFS
MODIS
(Terra)
MODIS
(Aqua)
VIIRS
(Suomi-NPP)
6A Sayer- AEROCOM 2014
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
7A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
SeaWiFS version 4
• Minor changes from Version 3 dataset
– Land: some regional surface/aerosol model changes
– Water: improved shallow/turbid pixel identification
• 550 nm AOD uncertainty:
– Land: ~0.05+20%
– Ocean: ~0.03+15%
A Sayer- AEROCOM 2014 8
Aspect Comments
Time
series
SeaStar satellite (1997-2010, a few gaps)
Coverage Daytime cloud-free snow-free land
Daytime cloud-free ice-free non-turbid 
water
Data
products
Main product is AOD at 550 nm
Land: also AOD at 412/490/670 nm, 
Ångström exponent, and SSA (for heavy 
dust)
Water: also AOD at 510/670/865 nm, 
Ångström exponent, fine mode fractional 
volume
Level 2 Nominal 13.5 x 13.5 km resolution
~1,500 km swath
Level 3 0.5° and 1°; daily and monthly resolution
Data
access
Distributed by GES DISC
Level 3 visualisation through Giovanni
SeaWiFS V4-V3 differences
A Sayer- AEROCOM 2014 9
SeaWiFS V4-V3 differences
• Improved shallow/turbid water detection removes artefacts, mainly in 
coastal areas
– Example shown for Bay of Bengal
– Note V3 did manage to identify and remove some turbid water!
• Features are semi-persistent regionally, but not always on a per-pixel basis
A Sayer- AEROCOM 2014 10
SeaWiFS V4-V3 differences
• Changes make physical sense and improve time series consistency 
with Data Assimilation (DA) grade MODIS product from NRL
• Comparison against AERONET modestly improved
A Sayer- AEROCOM 2014 11
South
America
Amazon
Sahel
English
Channel
(La Manche)
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
12A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
• Described by Hsu et al., JGR (2013); Sayer et al., JGR (2013)
– Summary: more retrievals, better retrievals
• Collection 6 refinements to Deep Blue:
1. Extended coverage to include vegetated surfaces as well as bright land
2. Improved surface reflectance models
3. Improved aerosol optical models
4. Improved cloud screening
5. Simplified quality assurance (QA) flags and QA-filtered AOD SDS included
6. Radiometric calibration improvements
7. AOD uncertainty estimates for every retrieval
MODIS C6: main developments
13A Sayer- AEROCOM 2014
MODIS C6: extended spatial coverage
14A Sayer- AEROCOM 2014
• Provided within level 2 
MOD/MYD04 files
• Depend on AOD, QA flag, 
and geometric air mass 
factor (AMF)
• Prognostic (defined relative 
to retrieved AOD, not 
AERONET AOD)
• For Aqua, QA=3:
– Expected error +/-
(0.086+0.56*AOD)/AMF
– Median AMF=2.8, leads to 
typical expected error +/-
(0.03+0.2*AOD)
• Similar for QA=2, larger for 
QA=1
Retrieval-level uncertainty estimates
15A Sayer- AEROCOM 2014
Error histograms are nearly* zero-centered Gaussian
*not perfectly – scope for future refinement
16A Sayer- AEROCOM 2014
What about Terra?
• Reprocessing handled by MODAPS, should be soon (this year)
– In-house testing suggests Deep Blue Terra performance appears similar 
to, but slightly poorer than, Aqua
– Both show good temporal stability
17A Sayer- AEROCOM 2014
Importance of calibration stability
• Use C6 algorithm with C5 and C6 
level 1 data
– Use of C6 level 1 data results in better 
agreement with AERONET
– Both versions have 0.01-0.02 change in 
bias over the mission; not sure if related 
to calibration or a coincidence of 
sampling
• This is for Aqua – Terra calibration 
improvements are larger
18A Sayer- AEROCOM 2014
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
19A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
What is the ‘merged’ MODIS dataset?
• Combination of Deep Blue and Dark Target group land/ocean algorithms to 
provide a more spatially-complete dataset
• Seasonal mean of daily mean AOD from the ‘merged’ SDS, 2006-2008
20A Sayer- AEROCOM 2014
How is merging done?
• This is a first attempt
• 12 monthly 
climatologies of NDVI 
used to assign retrievals 
over land:
– NDVI < 0.2: Deep 
Blue
– NDVI > 0.3: Dark 
Target
– Otherwise: pick the 
algorithm with higher 
QA value, else average 
if both QA=3
• Ocean algorithm used 
over water
• Only contains retrievals 
passing QA checks
21A Sayer- AEROCOM 2014
What does merging look like?
22A Sayer- AEROCOM 2014
So… what should I use?
• Shown ‘Best algorithm’ vs. AERONET by different metrics
• Usage recommendations depend on your application and comfort level with the data
• No single algorithm is better than the others by all metrics, or for all regions/seasons
• Paper in review at JGR on this topic (and more general Deep Blue/Dark Targer
comparison)
A Sayer- AEROCOM 2014 23
So… what should I use?
• Shown ‘Best algorithm’ vs. AERONET by different metrics, where differences are large
• Usage recommendations depend on your application and comfort level with the data
• No single algorithm is better than the others by all metrics, or for all regions/seasons
• Paper in review at JGR on this topic (and more general Deep Blue/Dark Targer
comparison)
A Sayer- AEROCOM 2014 24
Overview
• Deep Blue basics
• SeaWiFS, 1997-2010
• MODIS Terra/Aqua, 2000/2002+
– ‘Merged’ Deep Blue/Dark Target land & ocean dataset
• VIIRS, 2011+
25A Sayer- AEROCOM 2014
Images from NASA Earth Observatory, http://earthobservatory.nasa.gov/Features/Aerosols/
VIIRS – coming soon
26A Sayer- AEROCOM 2014
• Visible Infrared Imaging Radiometer 
Suite (VIIRS), launched on Suomi-
NPP at end of 2011
– Similar to MODIS (for our purposes)
• e-Deep Blue over land, improved 
SeaWiFS algorithm over water
– Both algorithms enhanced over 
previous applications
• Match NOAA product spatial 
resolution of ~6x6 km VIIRS daily coverage – note overlap between orbits (no gaps)
Preliminary VIIRS validation
• Looked at a selection of land and ocean AERONET sites
– Performance already approaching that of MODIS/SeaWiFS
– Further refinements before release
27A Sayer- AEROCOM 2014
Summary
• Deep Blue datasets:
– SeaWiFS version 4 – available
• http://disc.gsfc.nasa.gov
– MODIS Collection 6
• Aqua: level 2, 3 available now
• Terra: level 2, 3 probably by around end of year
• http://modis-atmos.gsfc.nasa.gov/
• Expanded spatial coverage, AOD uncertainty estimates, includes 
dataset already filtered for QA
– VIIRS – funded, in development
• Please use the data, ask questions, tell us when you find 
something exciting (or troubling)
– We are happy to help you read the data, and use it 
appropriately
– It’s nice to hear from users -
28A Sayer- AEROCOM 2014


Recent Progress in the NASA 'E-Deep Blue' Algorithm for Remote Sensing of Aerosol Optical Properties

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