The Impact of VIIRS Polarization

Sensitivity On Ocean Color

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&P1 APR Consulting, 2 Noblis, Inc.

Paper # 4446, IGARSS 2010, Honolulu HI

2

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*Funded and managed through the tri-agency Integrated Program Office and provided to NPP

INTRODUCTION

3

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

Ocean Color requires accurate retrieval of the Water Leaving Radiance, Lw

Accurate characterization of polarization sensitivity and the correction for it in ACO algorithm are both essential for viable ocean color product

4

Solar Array Rotates to Track Sun

Solar Array Payload Platform facing earth

“Cold side”

NPOESS 1330 Orbit

View from Sun

Earth

Scan &

Swath

VIIRS as seen from Earth side

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

With NPP, VIIRS’ revisit time for any spot is ≈ 12 hours

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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%) ,

for each of the seven scan angle settings

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VISNIR focal plane has 7 moderate resolution bands M1 -

M7, each with 16 detectors that have different responses

Fourier series of each detector’s response yields its

polarization sensitivity (Ip, Φp) in terms of the Fourier

Coefficients for the 2-cycle variation (example below)

Polarization sensitivity is highest for Band M1 (shown at

right), decreases for higher numbered Bands, and

reaches much smaller values for Band M7 (0.5 %)

Polarization measured for each band, each detector at 7 angles of RTA scan

The magnitude of polarization sensitivity:

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)

Plot of 2-cycle Fourier Coeff.s: Cosine versus Sine (as

% of averaged response), for each of 16 detectors of

M1 band at 7 color coded scan angles (HAM* side A)6

0

0.2

0.4

0.6

0.8

1

1.2

-15 30 75 120 165 210 255 300 345

, Direction of Linear Polarization of Input RadianceRel

ati

ve

Res

pon

se

I p = 0.2

Fp

= 3

0 d

eg

1.0

00

* Two sided Half-Angle-Mirror in VIIRS rotates at half the speed of the scanning telescopeto de-rotate the optical axis and hold the scanned field-of-view on the fixed focal plane

M1 Polarization Response Across Scan Angles (HAM A)

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

-0.035 -0.025 -0.015 -0.005 0.005 0.015 0.025 0.035

E2

F2

-55 -45 -20 -8 +22 +45 +55

1.0%

2.0%

3.0%

(Requirement)

F2,

% o

f av

erag

e re

spo

nse

E2, % of average response

7

7

VIIRS F1 polarization sensitivity met specified sensor requirements6,7

* Maximum DoLP for any detector at any scan angle < 45 deg.

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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

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

No Uncertainty With Uncertainty

F2,

% o

f av

erag

e re

spo

nse

F2,

% o

f av

erag

e re

spo

nse

E2, % of average responseE2, % of average response

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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

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DoLP of TOA radiance in GSD – Example: Band M1, with/without sun-glint, EoS, SZA>70º

No Exclusions NPOESS Spec ExclusionsSUMMER

WINTERNo Exclusions NPOESS Spec Exclusions

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Performance Improvement in nLw is Large with Polarization

Correction, impact small for Polarization Uncertainty – M1 Example

Accuracy

Precision

Step 3: Polarization Corrected with Characterization Uncertainty

Precision

Accuracy

Step 1: With Polarization Sensitivity, But No Correction

Precision

Accuracy

Step 2: Polarization Corrected with No Uncertainty

Band M1 example of nLw % Accuracy, % Precision & sample population size as stratified with nLw magnitude• Polarization correction significantly

reduces errors in ocean color(cf. steps 1 & 2)

• Effect of characterization uncertainty is small compared with residual errors (cf. steps 2 & 3)

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Predicted nLw Performance with Polarization Correction including

Realistic Uncertainty in Sensor Characterization

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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

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

1. Polarization Impact:

w/ Polarization Effects,

w/o Polarization Corr. ,

w/o Char. Uncertainty

2. Perfect Correction:

w/ Polarization Effects,

w/ Polarization Corr.,

w/o Char. Uncertainty

3. Pred. Performance:

w/ Polarization Effects,

w/ Polarization Corr.,

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

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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