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Performance of MODIS Semi-Analytic Ocean Color
Algorithms Chlorophyll a, Absorption
Coefficients, and Absorbed Radiation by
Phytoplanktonby Kendall L. Carder

• Absorption coefficients for phytoplankton, af(?),
  colored dissolved organic matter (CDOM) or
  gelbstoff, ag(?), and total, at(?)
• Chlorophyll a (Chlor_a_3) in the presence of CDOM
• Instantaneous photosynthetically active radiation
  for fluorescence, IPAR
• Absorbed radiation by phytoplankton for
  fluorescence, ARP
• Parameter adjustment for bio-optical domains
  comparing SST with NDTs
• Near-term focus is on the significantly improved
  performance of Chlor_a_3 at high latitudes

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Absorption spectra for water, CDOM, and
phytoplankton
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a? (443)/a? (675) versus a? (675) from Bering Sea
(MF0796) and Antarctic Polar Frontal Zone
(REV9802) are compared with high-light tropical
and subtropical data (dashed line)
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Surface map of (a) Temperature (oC) (b) Nitrate
concentration (µM l-1) (c) chla (mg m-3) and
(d) a? (443)/a? (675) for the California
upwelling region (Cal9704) in April 1997.
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Blending scheme to transition between fully
packaged and unpackaged pigment parameterization
for waters with SST between NDT-1 and NDT4
degrees C NDT map from D. Kamekowski
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Performance of new blending scheme for waters of
the Southern California Bight transitioning
between cold, nutrient-rich upwelled waters and
offshore, nutrient-poor waters.
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Comparison between Chlor_a_3 SA and OC4v4
algorithms for Chlorophyll a for the Southern
California Bight
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Chlor_a_3 applied to the Arctic region (a).
Quantile plot (b) shows no bias. CZCS algorithm,
dashed line in (d), shows bias due to package
effect
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Chlor_a_3 SA algorithm performance versus OC4v4
for Antarctic based on 971 field data points.
Note the large negative bias in the OC4v4
quantile plot
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Chlor_a_3 semi-analytical retrievals of
chlorophyll a for November 2000
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Chlor_a_2 empirical (OC4 surrogate) retrievals of
chlorophyll a for November 2000
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Semi-analytical retrieval of absorption by CDOM
or gelbstoff for November 2000Note the high
values in northern (river-rich) hemisphere
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Global histograms of chlorophyll a retrievals for
November 2000 using a) empirical Chlor_a_2 and b)
semi-analytic Chlor_a_3 algorithms. Mean values
are 0.215 and 0.325 mg m-3, respectively. Gregg
Conkright (2002)autumn mean 0.31 mg m-3 .
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Match-up Data Sets (preliminary) Provided by the
SIMBIOS for Non-Shallow Depths

• Chlor_a_3 Chlor_a_2
• Slope 0.97 0.79
• Intercept -.0045 -0.012
• r2 0.79 0.79
• Bias 0.009 -0.055
• RMS error 0.173 0.190
• Linear error ?49.1 ? 55.0

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Striping in the Chlor_a_3 products for the
western Gulf of Mexico due to stripes in the Lw
values (left) raw and (right) filtered
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Raw and filtered data from scene center. Lines 0,
20, 40, were each averaged horizontally and
then averaged together. Similar steps were taken
with Lines 1, 21, 41, etc. until a 20-line
pattern due to striping was acquired
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Noise pattern due to vertical striping from left
part of GOM scene (solid). A filter was made by
ratioing the mean to each line element and
applying to each appropriate line by multiplying.
Dashed line is the result. Over-compensation
occurred since striping was worse in the lower
left than in the upper right portions of original
scene.
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Change in horizontal variance was about 1 as a
result of applying the vertical filter. Note
solid and dashed lines represent unfiltered and
filtered data.
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Conclusions

• Chlor_a_3 algorithm performance has improved for
  high-latitude and upwelling scenes with little or
  no bias.
• SeaWiFS OC-4 algorithm performance in the
  Southern Ocean for field radiance is biased low
  by gt40 Chlor_a_2 global mean for November 2000
  was 0.215 mg/m3, while Chlor_a_3 value was 0.32
  mg/m3. Gregg Conkright (2002) mean global
  autumn value was 0.305 mg/m3.
• Global ocean primary production calculated with
  the MODIS Terra Chlor_a_3 algorithm are expected
  to show an increase over SeaWiFS-based values of
  about 29 for austral spring data.
• Preliminary non-shallow match-up field data for
  Terra Chlor_a_3 and Chlor_a_2 results show errors
  of 49 and 55, respectively, with 1 and 13.5
  linear biases, respectively, with most difference
  occurring for winter California Current data.
• A de-striping approach appears promising if
  smaller sub-scenes are used in the filter
  generation and application