1. The Impact of VIIRS Polarization
Sensitivity On Ocean Color
Paper # 4446, IGARSS 2010, Honolulu HI
7/26/2010
Vijay Kulkarny, Bruce Hauss, Sid Jackson, Justin Ip, Patty Pratt,
Clark Snodgrass, Roy Tsugawa, Bernie Bendow1, Gary Mineart2
NPOESS/SEITO/A&DP/VIIRS M&P
1 APR Consulting, 2 Noblis, Inc.

2. INTRODUCTION
VIIRS* on NPP and Ocean Color EDR
 VIIRS will produce 21 of 25 EDRs for NPOESS Preparatory Project (NPP)
 The Ocean Color/Chlorophyll (OC/C) EDR uses VIIRS reflective VISNIR bands
 VIIRS polarization sensitivity needs correction for Ocean Color & Chlorophyll EDR
Present Objective
 Evaluate Ocean Color performance of VIIRS Flight Unit 1 (F1) with its polarization
characteristics, with and without correction for the polarization sensitivity
o These evaluations use the F1 spectral response characteristics measured during TVAC tests
o Prior results shown elsewhere assumed the nominal VIIRS spectral response characteristics
Evaluation Approach
 Polarization sensitivity of VIIRS F1 was extensively characterized and modeled
 Simulated open ocean scene observations with VIIRS sensor model, followed by
Ocean Color retrieval with the Atmospheric Correction over Ocean (ACO) Algorithm
 Compared retrieved Ocean Color with input ocean scene “truth”
1. With VIIRS polarization affecting the Ocean Color – Polarization Impact
2. With VIIRS polarization effect corrected in processing – Perfect Correction
3. With correction including characterization uncertainty – Expected Performance
2*Funded and managed through the tri-agency Integrated Program Office and provided to NPP

3. Ocean Color requires accurate retrieval of
the Water Leaving Radiance, Lw
 Water leaving radiance, Lw, is a small component of Top-Of-Atmosphere
(TOA) radiance1 VIIRS measures (as low as 10% in M1 for some waters)
 TOA radiance is dominated by polarized Rayleigh scattering of sunlight
• Rayleigh scattering is highly polarized; depending on scattering geometry, up
to ≈90% in M7; up to ≈70% in M1 outside sun-glint, end-of-scan, SZA >70º
• ACO algorithm retrieves Lw by removing other components in TOA radiance2,3,4
(Rayleigh, Aerosol and multiple scattering, sun glint and reflection off the white caps on
the ocean surface)
• ACO also accounts for gaseous absorption and diffuse transmittance
• OC/C Algorithm5 estimates chlorophyll and optical properties in water
 If VIIRS polarization sensitivity is characterized, its effect on TOA radiance
data can be corrected in the ACO algorithm
 Uncertainty in the characterization of VIIRS polarization sensitivity
translates into errors in retrieved Lw, and in Ocean Color
Accurate characterization of polarization sensitivity and the correction for it in
ACO algorithm are both essential for viable ocean color product
3

4. Sun, VIIRS, Orbit, Ground Swath and Scan
View Geometry
VIIRS F1 will fly on NPP spacecraft at 824 km altitude in a
sun-synchronous orbit similar to NPOESS C1 or JPSS
NPOESS 1330 Orbit VIIRS as seen from Earth side
Solar Array Payload Platform
facing earth
“Cold side”
Earth
Solar Array Scan &
Rotates to Swath
Track Sun
View from Sun
With NPP, VIIRS’ revisit time for any spot is ≈ 12 hours
4

5. VIIRS polarization sensitivity is
thoroughly characterized
 The Polarization Working Group (PWG) with IPO, NASA,
NRL, Raytheon and NG technical experts oversaw the
associated planning, testing and data analysis for VIIRS F1
 Extensive, careful, repeatable and accurate measurements
of VIIRS F1 polarization sensitivity in VISNIR bands were
performed in a Raytheon laboratory ambient test (ETP679)6
 A large circular source of steady, uniform and diffuse white
light was viewed by VIIRS through a linear polarizer at
seven scan angles, distributed across VIIRS Earth View
scan, to record the associated responses of VISNIR bands
 At each scan angle setting, the polarizer was rotated about
the optical axis (24 steps in 360 deg.) to rotate the direction
of polarization and measure the responses of all bands
 The response of each detector in each band, M1 – M7,
averaged over several scan cycles of the rotating telescope
resulted in constant (averaged) response superposed with
two cycles of small sinusoidal variation (amplitude < 3%) ,
5 for each of the seven scan angle settings

6. Polarization measured for each band,
each detector at 7 angles of RTA scan
 VISNIR focal plane has 7 moderate resolution bands M1 - Plot of 2-cycle Fourier Coeff.s: Cosine versus Sine (as
% of averaged response), for each of 16 detectors of
M7, each with 16 detectors that have different responses M1 band at 7 color codedAcross Scan Angles (HAM side A)6
M1 Polarization Response scan angles (HAM A)
*
 Fourier series of each detector’s response yields its 0.035
polarization sensitivity (Ip, Φp) in terms of the Fourier 0.03
0.025
Coefficients for the 2-cycle variation (example below) 0.02
F2, % of average response
1.2
0.015
1 1.0 0.01
Relative Response
F p = 30 deg
0.8 0.005
I p = 0.2
F2
0.6 0
-0.035 -0.025 -0.015 -0.005 0.005 0.015 0.025 0.035
0.4 -0.005
1.0%
0.2
-0.01
, Direction of Linear Polarization of Input Radiance -0.015
2.0%
-15
00
0
30 75 120 165 210 255 300 345
3.0%
(Requirement) 7
-0.02
 Polarization sensitivity is highest for Band M1 (shown at -0.025
right), decreases for higher numbered Bands, and -0.03
reaches much smaller values for Band M7 (0.5 %)
-0.035
E2, % of average response
E2
 The magnitude of polarization sensitivity: -55 -45 -20 -8 +22 +45 +55
Degree of Linear Polarization (DoLP) = Ip = [sinusoid amplitude / averaged response] (in %)
 Phase (angle) of polarization sensitivity = Φp = angle of maximum response (or sinusoid peak)
as measured from the track (flight) direction = ½·tan-1(sine coeff. E2 / cosine coeff. F2)
* Two sided Half-Angle-Mirror in VIIRS rotates at half the speed of the scanning telescope
6 to de-rotate the optical axis and hold the scanned field-of-view on the fixed focal plane

7. VIIRS F1 polarization sensitivity met
specified sensor requirements6,7
* Maximum DoLP for any detector at any scan angle < 45 deg.
7

8. VIIRS polarization errors modeled,
example for Band M1, HAM side A
All 16 detectors’ polarization sensitivity, 2nd order curve fit to measurements over 7
scan angle in (E2, F2) Fourier Coefficient space equivalent to (Ip, 2Fp) in polar space
No Uncertainty With Uncertainty
F2, % of average response
F2, % of average response
E2, % of average response E2, % of average response
Polarization errors are biases by band, scan angle, HAM side, and detector, with random
errors by detector; sensor realization with uncertainty contains 3346 random draws
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9. Simulated GSD scenes, sensor errors
and Ocean Color retrieval algorithms
 Sun-ocean-sensor geometry of NPP/NPOESS 1330 orbit with VIIRS scanning geometry
 Geophysical properties used to generate polarized TOA radiances, as sampled from GSD8,9 (Global
Synthetic Data) environmental scene datasets for each of 12 days over a year, including surface
pressure & wind speed
– MODIS 8-day AOT (MOD08E3 Aerosol Optical Thickness) quantized in steps of 0.03 from 0.03 to
0.3; sun glint and white caps reflecting off ocean surface not modeled at present
 Sensor model includes the polarization sensitivity (and uncertainty) and spectral errors based on F1
characterization and a conservative sensor noise model9, 10
− Polarization model used to predict measurements of scene “truth”, as well as for correcting the
effects of polarization in processing the measurements with ACO algorithm
− Rayleigh radiance correction is based on band average RSR (Relative Spectral Response)
measured in TVAC tests, and is applied with detector dependent gain correction11
− Vicarious Calibration and its radiometric effects are yet to be modeled, and sensor errors like
polarization uncertainty may affect the ocean color performance through that process as well12
 Compared retrieved water leaving radiance, Lw, in bands M1-M4 with ocean scene “truth”
1. With polarization affecting VIIRS measurements – Polarization Impact
2. With effects of polarization corrected in processing – Perfect Correction
3. With polarization correction with characterization uncertainty – Expected Performance
9

10. DoLP of TOA radiance in GSD – Example:
Band M1, with/without sun-glint, EoS, SZA>70º
No Exclusions SUMMER NPOESS Spec Exclusions
No Exclusions WINTER NPOESS Spec Exclusions
10

11. Performance Improvement in nLw is Large with Polarization
Correction, impact small for Polarization Uncertainty – M1 Example
Step 1: With Polarization Sensitivity,
But No Correction Band M1 example of nLw % Accuracy,
% Precision & sample population size
as stratified with nLw magnitude
• Polarization correction significantly
Precision
reduces errors in ocean color
Accuracy
(cf. steps 1 & 2)
• Effect of characterization uncertainty
is small compared with residual
errors (cf. steps 2 & 3)
Step 2: Polarization Corrected with Step 3: Polarization Corrected with
No Uncertainty Characterization Uncertainty
Precision Precision
Accuracy Accuracy
11

12. Predicted nLw Performance with Polarization Correction including
Realistic Uncertainty in Sensor Characterization
12

13. VIIRS Ocean Color Performance (nLw % error) Improves significantly
with Polarization Correction and other updates based on F1 Testing
 Upgrade of ACO (Atmospheric Correction over Ocean) algorithm with polarization correction
and detector-dependent RSR were needed based on results of VIIRS F1 test program
1. Polarization Impact: 2. Perfect Correction: 3. Pred. Performance:
w/ Polarization Effects, w/ Polarization Effects, w/ Polarization Effects,
w/o Polarization Corr. , w/ Polarization Corr., w/ Polarization Corr.,
w/o Char. Uncertainty w/o Char. Uncertainty w/ Char. Uncertainty
Band Accuracy Precision Accuracy Precision Accuracy Precision
M1 -17.73 18.09 4.33 9.96 -7.94 10.68
M2 -14.56 14.62 -6.45 8.45 -6.99 8.72
M3 -5.41 7.45 -2.51 6.24 -2.65 6.50
M4 -9.96 11.41 -8.06 9.72 -6.51 9.52
 The improvement with polarization correction is significant (e.g., over 10% in accuracy and
nearly 10% in precision for M1)
 The impact of polarization uncertainty is small (at worst, less than 1% in accuracy and just
over 1% in precision for M1)
− Simulation results based on available databases dominated by open ocean truth data
VIIRS F1 Ocean Color performance should be comparable overall with legacy systems
13

14. Bibliography
1. “Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review”; H. R.
Gordon and A.Y. Morel; Springer-Verlag, New York; p. 114 (1983)
2. “Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS”; H.
R. Gordon and M. Wang; Applied Optics 33, 443; 1994
3. “Atmospheric Correction Over Ocean”; Q. Liu, C. Carter, K. Carder (U. of S. Florida); Santa Barbara
Remote Sensing, Raytheon Co.; NPOESS, VIIRS ATBD, Y2389, rev B; Feb. 18, 2009
4. “Normalized Water-leaving Radiance Algorithm Theoretical Basis Document”; H. R. Gordon and K.J.
Voss; MODIS ATBD 17; Apr. 30, 1999; http://modis.gsfc.nasa.gov/data/atbd/ocean_atbd.php
5. “Case 2, Chlorophyll_a Algorithm and Case 2, Absorption Coefficient Algorithm”; K. Carder, S. Hawes
and R.F. Chen; MODIS ATBD 19 (1997); ver.7; Jan. 30, 2003; (link same as above, in # 4)
6. “Performance Verification Report – VIIRS FU1 Polarization (PVP Section 4.7.3)”; E. Novitsky, S.
Herbst, J. Young, and E. Fest; Raytheon Co.; VIIRS_02_18_86_ Rev_A_v07; Oct. 30, 2009
7. “Performance Specification Sensor Specification for the Visible/Infrared Imager Radiometer Suite
(VIIRS)”; R. Ontjes, POC; Raytheon Co.; PS154640-101, Rev D; June 19, 2008
8. “EVEREST: an end-to-end simulation for assessing the performance of weather data products
produced by environmental satellite systems”; M. Shoucri, B. Hauss; SPIE Proc. 7458; 2009
9. “VIIRS Chain Test Report – The VIIRS Ocean color Algorithm”; J. Ip; Northrop Grumman; D44205, Rev
A, sec. 8; Sept. 12, 2007
10.“Simulation of Earth Science Remote Sensors with NGST's EVEREST/VIRRISM”; S. Mills; A Collection
of Technical Papers - AIAA Space 2004 Conf. & Expo. 2, p 1105-1124, 2004
11.“Error Budget Development Status, CDRL A037”; B. Bendow, J. Diehl, Ed.s; Northrop Grumman; NP-
EMD.2010.510.0053, sec. 1 and 5; June 30, 2010
12.“Sensitivity of Ocean Color Remote Sensing from Space to Calibration Errors”; K. R. Turpie, et al.;
NASA-GSFC; NASA/TM-2009-214179; May 2009
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