Werdell, Jeremy

NASA Technical Reports Server

PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
Ocean color atmospheric correction for
operational remote sensing of Chesapeake Bay
Jeremy Werdell
NASA Goddard Space Flight Center
Science Systems & Applications, Inc.
23 February 2010
Acknowledgements:
Rick Stumpf & the Gang at the
Ocean Biology Processing Group
https://ntrs.nasa.gov/search.jsp?R=20110015291 2019-08-30T17:09:23+00:00Z
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
challenges for remote sensing of estuaries
temporal & spatial variability
satellite sensor resolution
satellite repeat frequency
validity of ancillary data (SST, wind)
resolution requirements & binning options
straylight contamination (adjacency eﬀects)
non-maritime aerosols (dust, pollution)
region-speciﬁc models required?
absorbing aerosols
suspended sediments & CDOM
complicates estimation of Rrs(NIR)
complicates BRDF (f/Q) corrections
saturation of observed radiances
anthropogenic emissions (NO2 absorption)
Chesapeake Bay Program
AERONET
COVE
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
challenges for remote sensing of estuaries
temporal & spatial variability
satellite sensor resolution
satellite repeat frequency
validity of ancillary data (SST, wind)
resolution requirements & binning options
straylight contamination (adjacency eﬀects)
non-maritime aerosols (dust, pollution)
region-speciﬁc models required?
absorbing aerosols
suspended sediments & CDOM
complicates estimation of Rrs(NIR)
complicates BRDF (f/Q) corrections
saturation of observed radiances
anthropogenic emissions (NO2 absorption)
Chesapeake Bay Program
AERONET
COVE
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
the experiment
collaboration with NOAA, EPA Chesapeake Bay Program, University of
Maryland, & colleagues since 2006 Chesapeake Bay Remote Sensing Symposium
NOAA CoastWatch East Coast Node using results for operational processing
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
the experiment
collaboration with NOAA, EPA Chesapeake Bay Program, University of
Maryland, & colleagues since 2006 Chesapeake Bay Remote Sensing Symposium
NOAA CoastWatch East Coast Node using results for operational processing
run multiple long-term time-series of MODIS-Aqua
      Lower Chesapeake Bay, June 2002 - December 2008
      processing conﬁguration follows Reprocessing 2010
      QC metrics:  exclude cloudy days & high sensor zenith angles
      ﬁnal analyses use ~ 13 days per month
      generate frequency distributions and monthly ?time-series
      use in situ measurements as reference
consider potential for application in an operational environment     ?
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
need ρa(λ) to get ρw(λ) and vice-versa
ρt(λ)  =  ρw(λ)  +  ρg(λ)  +  ρf(λ)  +  ρr(λ)  +  ρa(λ)
atmospheric correction & the “black pixel” assumption
TOA          water          glint           foam           air       aerosols
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
need ρa(λ) to get ρw(λ) and vice-versa
the “black pixel” assumption (pre-2000):
ρa(NIR)  =  ρt(NIR)  -  ρg(NIR)  -  ρf(NIR)  -  ρr(NIR)  -  ρw(NIR)
ρt(λ)  =  ρw(λ)  +  ρg(λ)  +  ρf(λ)  +  ρr(λ)  +  ρa(λ)
atmospheric correction & the “black pixel” assumption
TOA          water          glint           foam           air       aerosols
0
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
need ρa(λ) to get ρw(λ) and vice-versa
the “black pixel” assumption (pre-2000):
ρa(NIR)  =  ρt(NIR)  -  ρg(NIR)  -  ρf(NIR)  -  ρr(NIR)  -  ρw(NIR)
calculate aerosol ratios, ε :
ε(748,869)
ε(λ,869)
ρt(λ)  =  ρw(λ)  +  ρg(λ)  +  ρf(λ)  +  ρr(λ)  +  ρa(λ)
atmospheric correction & the “black pixel” assumption
ρa(869)
ρa(748)
ρa(869)
ρa(λ)
TOA          water          glint           foam           air       aerosols
≈
≈
0
ε(748,869)
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
are Rrs(NIR) really black?
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
are Rrs(NIR) really black?
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
what happens when we don’t account for Rrs(NIR) > 0?
use the “black pixel” assumption (e.g., SeaWiFS 1997-2000)
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
many approaches exist, here are a few examples:
assign aerosols (ε) and/or water contributions (Rrs(NIR))
     e.g., Hu et al. 2000, Ruddick et al. 2000
use shortwave infrared bands
     e.g., Wang & Shi 2007
correct/model the non-negligible Rrs(NIR)
     Siegel et al. 2000 used in SeaWiFS Reprocessing 3 (2000)
     Stumpf et al. 2003 used in SeaWiFS Reprocessing 4 (2002)
     Lavender et al. 2005 MERIS
     Bailey et al. 2010 used in SeaWiFS Reprocessing 6 (2009)
use a coupled ocean-atmosphere optimization
     e.g., Chomko & Gordon 2001, Stamnes et al. 2003, Kuchinke et al. 2009
what to do when Rrs(NIR) > 0?
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
ﬁxed aerosol & water contributions (MUMM)
assign ε & ρw(NIR) (via ﬁxed values, a climatology, nearby pixels)
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
advantages:
accurate conﬁguration leads to accurate aerosol & Rrs(NIR) retrievals
several conﬁguration options: ﬁxed values, climatologies, nearby pixels
method available for all past, present, & future ocean color satellites
disadvantages:
no conﬁguration is valid at all times for all water masses
requires local knowledge of changing aerosol & water properties
implementation can be complicated for operational processing
advantages & disadvantages
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
use of NIR + SWIR bands
use SWIR bands in “turbid” water, otherwise use NIR bands
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
use of SWIR bands only
compare NIR & SWIR retrievals when considering only “turbid pixels”
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
advantages & disadvantages
advantages:
“black pixel” assumption largely satisﬁed in SWIR region of spectrum
straightforward implementation for operational processing
disadvantages:
only available for instruments with SWIR bands
SWIR bands on MODIS have inadequate signal-to-noise (SNR) ratios
diﬃcult to vicariously calibrate the SWIR bands on MODIS
must deﬁne conditions for switching from NIR to SWIR
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
correction of non-negligible Rrs(NIR)
estimate Rrs(NIR) using a bio-optical model
operational SeaWiFS & MODIS processing ~ 2000-present
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
advantages & disadvantages
advantages:
method available for all past, present, & future ocean color missions
straightforward implementation for operational processing
disadvantages:
bio-optical model not valid at all times for all water masses
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
summary of the three approaches
defaults as implemented in SeaDAS
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
MODIS-Aqua vs. SeaWiFS
default processing ~ OC3 for MODIS-Aqua & OC4 for SeaWiFS
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
SNR transect for MODIS-Aqua NIR & SWIR bands
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
MODIS-Aqua Level-2 Chl “match-ups” for NIR & SWIR processing
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
MODIS-Aqua ρa(443)
PJW, NASA/SSAI, 23 Feb 2010, Portland, OR
distribution of the turbidity index using in NIR-SWIR switching


Ocean Color Atmospheric Correction for Operational Remote Sensing of Chesapeake Bay

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