Imaging Spectrometer Stray Spectral Response: In-Flight ... · 18/10/2017  · Imaging Spectrometer...

Jet Propulsion LaboratoryCalifornia Institute of Technology

David R. Thompson, david.r.thompson@jpl.nasa.gov!Joseph W. Boardman*!Michael Eastwood!Robert O. Green!Justin Haag!Pantazis Mouroulis!Byron Van Gorp!!

Jet Propulsion Laboratory, California Institute of Technology!*Analytical Imaging and Geophysics, Inc. Boulder, CO!!Copyright 2017 California Institute of Technology. All Rights Reserved. US Government Support Acknowledged.!!!

Imaging Spectrometer Stray Spectral Response: In-Flight Characterization and Correction!

11/1/17! david.r.thompson@jpl.nasa.gov! 1

Motivation!•  Non-Gaussian tails of spectral response functions

can be difficult to characterize in the laboratory!•  Calibration can shift during deployment!•  Small SSRF contributions can damage downstream

atmospheric correction!•  In-flight techniques are useful for validating and

updating laboratory measurements. !

11/1/17!david.r.thompson@jpl.nasa.gov! 2

-20 -15 -10 -5 0 5 10 15 20Channel

10-4

10-3

10-2

10-1

Res

pons

e

Nominal SRFStray SRFActual SRF

Method!

11/1/17!david.r.thompson@jpl.nasa.gov! 3

•  Sequential estimation of Nominal and Stray SRF parameters. !

•  Exploit predictable changes in the shape of the A band across varying surface elevation.!

•  Diverse scene content provides numerical leverage to characterize spectral response tails!

Estimation of nominal SRF[Thompson et al., Atmos. Meas. Tech 2015]!

Optimize a wavelength shift to match high-contrast atmospheric absorption features!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 4

Estimation of nominal SRF[Thompson et al., Atmos. Meas. Tech 2015]!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 5

-0.0003!

-0.0002!

-0.0001!

0!

0.0001!

0.0002!

0.0003!

0! 100! 200! 300! 400! 500! 600! 700!

Wav

elen

gth

shift

(mic

ron)!

Sample!

Stray SRF Measurement model Adapted from [Zhong et al., 2006]!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 6

StrayRadiance

NominalRadiance

MeasurementNoise

MeasuredRadiance

Stray SRF Measurement model Adapted from [Zhong et al., 2006!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 7

StrayRadiance

NominalRadiance

MeasurementNoise

MeasuredRadiance

GHLA HLA 𝜖LMRadianceatsensorNominalSRFStraySRF

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 8

StrayRadiance

NominalRadiance

MeasurementNoise

MeasuredRadiance

RadianceatsensorNominalSRFStraySRF

Stray SRF Measurement model Adapted from [Zhong et al., 2006]!

A Linear SRF Correction Matrix!

Calculate a Moore-Penrose Pseudoinverse:!!!!This estimates the nominal SRF:!!!!!!A similar correction fixes cross-track stray light !

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 9

CorrectedRadiance

Correc=onmatrix

DistortedMeasurement

Retrieve Stray SRF from a “Calibration Scene”!Death Valley Transect, 2014 (visible RGB)!

11/1/17!david.r.thompson@jpl.nasa.gov! 10

Predict A band radiances using a Digital Elevation Model!

Nonlinear least squares optimization finds SSRF parameters!

Estimation accuracy for Gaussian SSRF (simulated)!

11/1/17!david.r.thompson@jpl.nasa.gov! 11

0.02 0.04 0.06 0.08 0.1�

4

5

6

7

Gau

ssia

n �

SNR 400SNR 200

(Straylightfrac=on)

Estimation accuracy for Lorentz SSRF (simulated)!

11/1/17!david.r.thompson@jpl.nasa.gov! 12

0.02 0.04 0.06 0.08 0.1�

4.71

5.8875

7.065

8.2425

Lore

ntz

half

wid

th

SNR 400SNR 200

(Straylightfrac=on)

Fit error for candidate SSRF shapes!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 13

bestfit

-30 -20 -10 0 10 20 30Channel

10-6

10-4

10-2

100

Response

gaussianlorentzpareto

Improvement in O2 A band fit!

11/1/17!david.r.thompson@jpl.nasa.gov! 14

745 750 755 760 765 770 775Wavelength (nm)

3

4

5

6R

adia

nce

(uW

nm

-1 s

r-1 c

m-2

)

NominalMeasuredCorrected

745 750 755 760 765 770 775Wavelength (nm, with offset for clarity)

0

0.01

0.02

0.03

0.04

0.05

Rad

ianc

e sq

uare

d er

ror

MeasuredCorrected

Correction fixes a bias in pressure altitude estimates!

11/1/17!david.r.thompson@jpl.nasa.gov! 15

0 0.5 1 1.5 2 2.5 3Elevation, km ASL

0

0.5

1

1.5

2

2.5

3

Estim

ated

Pre

ssur

e Al

titud

e, k

m A

SL

1.372

0 0.5 1 1.5 2 2.5 3Elevation, km ASL

0

0.5

1

1.5

2

2.5

3

Estim

ated

Pre

ssur

e Al

titud

e, k

m A

SL0.721

Beforecorrec6on A8ercorrec6on

Ivanpahvalida6onsite

Reflectance validation!

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400Wavelength (nm)

0

0.1

0.2

0.3

0.4

Ref

lect

ance

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400Wavelength (nm)

0

0.1

0.2

0.3

0.4

Ref

lect

ance

950 1000 1050 1100 1150 1200Wavelength (nm)

0.28

0.3

0.32

0.34

Ref

lect

ance

950 1000 1050 1100 1150 1200Wavelength (nm)

0.25

0.3

0.35

0.4

Ref

lect

ance

test interval

Reference interval

q(x) = 0.0045

q(x) = 0.0032

11/1/17!david.r.thompson@jpl.nasa.gov! 16

Reflectancequalitymetric:

India Validation Results!

11/1/17!david.r.thompson@jpl.nasa.gov! 17

•  26of37flightdaysshowsignificantimprovements(p<0.001)•  Typicalimprovementis20-35%•  Noflightdayshowsasta=s=callysignificantaccuracyreduc=on

Frac=onalimprovementfor277scenes

Agreement with laboratory data!

david.r.thompson@jpl.nasa.gov! 18

-10 -8 -6 -4 -2 0 2 4 6 8 10Channel

10-4

10-2

100

Relat

ive re

spon

se

Laboratory measurementAtmospheric fitNominal SRF

11/1/17!

Spatial dimension!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 19

•  ExploitNear-Infrared(NIR)oceanreflectance•  Useahaze-freedaytoconstrainpathradianceandadjacencyeffects•  Useawind-freedaywithnadirobserva=onstolimitglint•  DarkwatershouldbehighlyabsorbantinNIR•  Dataset:2015Greenlandiceflow

500 1000 1500 2000 2500Wavelength (nm)

0

0.1

0.2

0.3

0.4

0.5

0.6

Ref

lect

ance

Sea!

Ice!

“Halo” reduction!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 20

Original RGB! 612 nm, equalization stretch!(0-3 uW nm-1 sr-1 cm-2)!

612 nm, after CRF correction!

Discussion!•  Can leverage scene invariant properties to fit PSFs!•  Some advantages to using separable functions!

–  Numerical stability, fairly easy to prevent ringing & overcorrection!–  Can model CRF or SRF or both, and fit them independently!

•  Positive results on held-out validations!–  Appears to fix our pressure altitude bias!–  Improves H2O residuals!–  Improves spatial halos!

•  Implemented in latest India release, and all AVIRIS-NG datasets starting from 2016!

1/16/2017!AVIRIS-NG PSFs / David.R.Thompson@jpl.nasa.gov! 21

Thanks!!NASA Earth Science!The AVIRIS-NG Team, including Sarah Lundeen, Brian Bue, Winston Olson-Duvall, Ian McCubbin, Mark Helmlinger, and others!

11/1/17!david.r.thompson@jpl.nasa.gov! 22