Microwave Radiometer (MWR) Counts to Tb (Brightness ... · Microwave Radiometer (MWR) counts to Tb...
Transcript of Microwave Radiometer (MWR) Counts to Tb (Brightness ... · Microwave Radiometer (MWR) counts to Tb...
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Microwave Radiometer (MWR) Counts to
Tb (Brightness Temperature) Algorithm
Development (Version 6.0) and On-Orbit
Validation
Zoubair Ghazi
CFRSL –Central Florida Remote Sensing Lab
Dissertation Defense October 24, 2014
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• To develop an improved counts to brightness temp (Tb)
algorithm for the CONAE Microwave Radiometer on
the Aquarius/SAC-D satellite
• Validation of Tb measurements using inter-satellite
radiometric comparisons (X-CAL)
• Produce an Algorithm Theoretical Basis Document
(ATBD) and deliver prototype MatLab code to
CONAE
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• Post-launch CFRSL & CONAE evaluated MWR counts-to-Tb algorithm V5.0 – Used 6 mo of MWR on-orbit collocation with WindSat
• Ocean Tb’s exhibited small and acceptable Tb biases
• Land Tb’s exhibited anomalous behavior – Land/water Tb transitions were “Smeared”
– Step function changes of noise diode deflections
• Based upon on-orbit evaluation, it was concluded that: – V5.0 was unacceptable for producing MWR science data
– An improved counts-to-Tb algorithm must be developed to address the anomalous Tb effects
• Further, CONAE developed a revised Counts-to-Tb algo V5.0S that included a smear correction
• This was the starting point for my dissertation research
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1. Evaluated the MWR counts-to-Tb algorithm V5.0S
– On-orbit X-CAL with WindSat indicated that
• Smear effects at land/water boundaries were removed
• However, anomalous effect of noise diode deflections remained
– Determined that MWR system gain varied with scene Tb
2. Developed a forward model for MWR system Counts-to-Tb
– Empirically derived coefficients to match on-orbit observations, including deep space calibrations
– Characterized model coefficients versus scene Tb
3. Developed a gain non-linearity correction
4. Implemented a new inverse model Counts-to-Tb algorithm V6.0
5. Validated algorithm using X-CAL with WindSat
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Aquarius (AQ) is a mission of “Original
Exploration” First NASA mission to measure Sea
Surface Salinity (SSS) from space
SAC-D was launched on June 10th , 2011 from
Vandenberg Air Force Base, California.
• Aquarius instrument - NASA
• MWR - CONAE
(Argentinian Space Agency)
• MWR provides auxiliary environ
measurements: water vapor,
ocean surface wind speed, and
oceanic rain rate
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• 3 channel push-broom Dicke
radiometer:
– 36.5 GHz H- & V-Pol
(forward-look)
– 23.8 GHz H-Pol
(aft-look)
• Earth Incidence angle
– 52o for odd beams
– 58o for even beams
• Matches the AQ swath width
of 380 km
MWR supports AQ science by measuring simultaneous &
collocated ocean brightness temperatures (Tb)
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MWR Single Channel Block Diagram S
W M
atr
ix
Tin
To Counts
TN
On/off
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Three Dicke radiometer states:
)1 ( )( setoffrecvrecvina VGTTC
)2 ( )( setoffrecvrecvNinN VGTTTC
)3 ( )( setoffrecvrecvoo VGTTC
Subtracting (1) from (2) yields the radiometer gain,
which varies in time
N
aNrecv
T
CCG
oN
an
oain TT
CC
CCT
*
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200 250 300 350 400 450 500 5506000
7000
8000
9000
10000
11000
12000
13000
Tin
To
Tin + Tn
Radiometer Input to the antenna port of Dicke switch, Tin , Kelvin
Rad
_co
un
ts, c
ou
nts
Slope is radiometer gain
Dickie Sw
in ant
position
Dickie Sw in
reference load
position
Dickie Sw in ant
position + noise
diode ON
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• Our objective was to determine the MWR transfer
function based upon on-orbit measurements
• However, under typical on-orbit condition, the
radiometer system gain will vary cyclically (once/orbit)
due to the receiver physical temperature changes
• Therefore, a procedure was developed to synthesize
rad_counts @ constant system gain from MWR
measurements
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• Time variable gain was removed and all counts were
normalized using the following equation:
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i
inormGain
GainCoCo
i
Toi
<Trec>
Gaini
<Gain>
is the instantaneous reference load physical temperature, Kelvin
is the orbit average receiver noise temperature
is the instantaneous system gain
is the orbit average gain
iii GainTrecToCo *)(
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Orbital Time, Min Orbital Time, Min
Co
Before Normalization After Normalization
Refe
ren
ce L
oad
Tem
pera
ture
, K
/100
Refe
ren
ce L
oad
Co
un
ts, co
un
ts
GainTC onormo *_GainTC oo *
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After Count (gain)
Normalization
Nois
e D
iode D
eflection,
counts
Nois
e D
iode D
eflection,
counts
Scene B
rightn
ess T
b, K
Effects of
variable gain
Before Count (gain)
Normalization
Linear dependence
on scene Tb
Radiometer Input to the antenna port of Dicke switch, Tin (Kelvin)
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123
2
3
1
therefore,
) () () (
)3( )(
)2 ( )(
)1 ( )(
recvrecvrecv
recvantrecvrefrecvNant
setoffrecvrecvrefref
setoffrecvrecvNantN
setoffrecvrecvanta
GGG
TTTTTTT
VGTTC
VGTTTC
VGTTC
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)()(
) ()(
) ()(
33_
22_
11_
inreforecv
inreforecv
inreforecv
ThTgGG
ThTgGG
ThTgGG
Go is the mean “long term gain” g(Tref) is the orbital gain change due to phy temp (Tref) h(Tin) is the gain compression due to variable scene brightness temp (and injected noise diode)
These parameters are estimated during a single orbit where a deep-space calibration is performed
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Rad
_co
un
ts
Radiometer Input to the antenna port of Dicke switch, Tin (Kelvin)
Quadratic regression
Tin-1 = Tant
Tin-3 = Tant + TN
Tin-2 = To
Ref Load
Full-dynamic range
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Gai
n C
om
pre
ssio
n, h
(Tin
)
Radiometer Input Brightness, Tin (Kelvin)
Tin-1 = Tant
Tin-3 = Tant + TN
Linear regression
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• Seven Deep Space Calibration (DSC) orbits that
included, space, ocean, and land observations were used
to cover wide range of scene Tb’s
• After counts (gain) normalization, the radiometer
transfer function was established
– Rad_counts = f(Tin)
• Quadratic regression for 37V channel yielded the
following
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3270 ) (58.16 )(105.7 _ 24
inin TTxcountsRad
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Rad
_co
un
ts, c
ou
nts
Radiometer Input to the antenna port of Dicke switch, Tin (Kelvin)
Tin-1 = Tant
Tin-3 = Tant + TN
Tin-2 = To
Ref Load
Tin Full-dynamic range
Quadratic
Regression
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• Averaging 2nd order regression coeff’s from 7 DSC orbits, the instantaneous counts linearization equation is:
– For 37 V
– For 37 H
– For 23 H
Where x = ant, N, and ref
Tin is the input Tb to the Dicke switch, which is estimated using non-linear counts
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2
in_ T*004)-(-7.4677e xlinearx CC
2
in_ T*004)-(-6.9064e xlinearx CC
2
in_ T*004)-(-2.1708e xlinearx CC
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3270)(6.16)(105.7_ 24
inin TTxcountRad
3270)(6.16)(102.8_ 26
inin TTxcountRad
(V5.0S)
(V6.0)
Rad
_co
un
ts (
gain
no
rmal
ized
)
Radiometer Input to the antenna port of Dicke switch, Tin (Kelvin)
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Brightness Temperature, K Brightness Temperature, K
No
ise
Dio
de
def
lect
ion
, co
un
ts/k
Ocean
Land
Space
Ocean
Land
Linear Regression
Scen
e B
righ
tnes
s Tb
, K
V5.0S (Without counts linearization)
V6.0 (With counts linearization)
No
ise
Dio
de
def
lect
ion
, co
un
ts/k
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Samples Samples
Gain
, co
un
ts/k
Gain
, co
un
ts/k
V5.0S V6.0
Note:
“gain jumps”
Sce
ne
Bri
gh
tnes
s T
b,
K
Gain variation
due to changing
recvr phy temp
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• Characterization of injected noise diode
temperature (TN) over physical temperature
• Retrieve antenna switch matrix loss coefficients
– Empirical method (regression model) was applied
– Assumption: All transmission and reflection coeff’s
are constant and are NOT expected to change during
MWR's mission life time
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waveguide
Load
Distributed Loss (L)
Warm / Cold Load
Distributed Temperature (Tguide)
Th /Tc
Receiver Counts
Calibration Ref
Plane
T’h /T’c
Input to The
Receiver
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SAYAK BISWAS
V2.0 V6.0
Nois
e D
iode D
eflection, counts
Samples Samples
Nois
e D
iode D
eflection, counts
Hot-load
Cold-load
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Tap=[Tin-(b2*To+b3*T1+b4*T2+b5*T3+b6*T4)]/b1
– where
• Tap is the scene brightness temp at horn aperture
• Tin is the input brightness temperature to antenna port
of Dicke switch
• To , T1 , T2 , T3 ,& T4 are MWR physical temps
• b1, b2, b3, b4, b5 and b6 are antenna switch matrix
loss coefficients derived using the regression model
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MWR coffin
• The thermal vacuum (TV) test for MWR was
performed in September 2009. (09/06 – 09/09)
• Performance of the SW matrix losses coefficients
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Tap=[Tin-(b2*To+b3*T1+b4*T2+b5*T3+b6*T4)]/b1
Tin calc from rad_counts
Tap & Tap_regress
SWM Model, RMS=3.94 Regression Model ,RMS= 0.75
Te
mp
era
ture
s (
K)
Te
mp
era
ture
s (
K)
Time, min Time, min
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To = 309K
To = 282K
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• APC and residual bias correction were applied
by inter-satellite XCAL – MWR = target & WindSat = reference
• MWR and WindSat have different incident
angles, therefore, Tbs were adjusted using
theoretical radiative transfer model values for
both satellites (MWRsim and WSsim)
WSadj= WSobs + (MWRsim – WSsim )
• Double Difference Technique
DD = MWRobs – WSadj
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MWR Tb, K
Ocean
Linear
Regression
Space
Land
WS Adjusted Tb, K
Before Correction After Correction
WS Adjusted Tb, K
MW
R T
b, K
MW
R T
b, K
Ocean
Linear
Regression
Space
Slope= 0.92329 Offset = 0.40928 Slope= 0.99952 Offset = 0.091356
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1100 1200 1300 1400 1500 1600 1700-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
B2
B4
B6
B8
1100 1200 1300 1400 1500 1600 1700-5
0
5
10
15
B2
B4
B6
B8
Samples Samples
Bri
ghtn
ess
Tem
pe
ratu
re. T
b, K
Bri
ghtn
ess
Tem
per
atu
re. T
b, K
Version 5.0S Version 6.0
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Samples
Samples
Bri
ghtn
ess
Tem
pe
ratu
re. T
b, K
Version 5.0S Version 6.0
1100 1200 1300 1400 1500 1600 1700-5
0
5
10
15
B1
B3
B5
B7
1100 1200 1300 1400 1500 1600 1700
-40
-20
0
20
40
60
B1
B3
B5
B7
Bri
ghtn
ess
Tem
per
atu
re. T
b, K
Samples
Bri
ghtn
ess
Tem
per
atu
re. T
b, K
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Double Difference Radiometric Biases
MWR/WindSat
(Five Days Average)
Jan 01, 2012 – Dec 31, 2012
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10 20 30 40 50 60
10
20
30
40
50
60
70-4
-3
-2
-1
0
1
2
3
4
South Pole
North Pole
South Pole
Jan 01,2012 - Dec 31,2012
DD
, K
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• MWR Counts-to-Tb algorithm V6.0 has been developed and distributed to the AQ Cal/Val Team
– MWR transfer function non-linearity in V5.0S has been characterized and corrected in V6.0
– Antenna switch matrix loss coefficients were derived using re-analysis of MWR pre-launch TV calib test
• Validation of V6.0 performed using 2 years of on-orbit measurements
– On-orbit X-CAL, between MWR and WindSat, have produced the antenna pattern correction (APC) and removed small Tb biases
• V6.0 Algorithm Theoretical Basis Document and MatLab code delivered to CONAE for science data processing
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• Conferences 1. Ghazi, Z.; Biswas, S.; Jones, L.; Hejazin, Y.; Jacob, M.M., "On-orbit signal processing
procedure for determining Microwave Radiometer non-linearity," Southeastcon, 2013
Proceedings of IEEE , vol., no., pp.1,5, 4-7 April 2013 doi: 10.1109/SECON.2013.6567504
2. Ghazi, Zoubair; Santos-Garcia, Andrea; Jacob, Maria Marta; Jones, Linwood, "CONAE
Microwave Radiometer (MWR) counts to Tb algorithm and on-orbit validation," Microwave
Radiometry and Remote Sensing of the Environment (MicroRad), 2014 13th Specialist
Meeting on , vol., no., pp.207,210, 24-27 March 2014 doi: 10.1109/MicroRad.2014.6878941
3. Santos-Garcia, A; Biswas, S.; Jones, L.; Ghazi, Z.; "Aquarius/SAC-D Microwave Radiometer
brightness temperature validation," Oceans, 2012 , vol., no., pp.1,4, 14-19 Oct. 2012 doi:
10.1109/OCEANS.2012.6404830
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Back UP
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Note: “gain jumps” @
land/water boundaries Dynamic range = 60 counts
Rad
_co
un
ts
V5.0S V6.0
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1100 1200 1300 1400 1500 1600 1700-30
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To Gain
Scen
e B
righ
tnes
s Tb
, K
Time (min) Time (min)
To Gain
Before Gain Normalization After Gain Normalization
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1
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Secondary
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• New MWR Tb data set to be used for tuning and validation of the wind speed algorithms
• XCAL 5 day double difference (DD) biases calculated between WindSat & MWR
– DD = MWR-WS • Applied triangular moving average on the 5 day DD time
series to smooth the correction
• The new MWR Tb’s V7.0 = V6.0 – Tbbiases
– These V7.0 Tb’s will be normalized to match the WindSat Tb’s in the mean i.e., have zero DD Tb-bias
• The new “adjusted DD” given in the following charts was derived as:
• DDadj = DDV6.0 -Tb biases
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7/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
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3B1
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
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-2
-1.5
-1
-0.5
0
0.5
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1.5
2
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3B2
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
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-1.5
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-0.5
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July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
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July 2012 - Nov 2013
7/12 11/12 3/13 7/13 11/13-3
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-2
-1.5
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-0.5
0
0.5
1
1.5
2
2.5
3B5
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
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2.5
3B6
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
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-0.5
0
0.5
1
1.5
2
2.5
3B7
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B8
July 2012 - Nov 2013
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7/12 11/12 3/13 7/13 11/13-3
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-2
-1.5
-1
-0.5
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3B1
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B2
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B3
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B4
July 2012 - Nov 2013
7/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B5
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B6
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B7
July 2012 - Nov 20137/12 11/12 3/13 7/13 11/13-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3B8
July 2012 - Nov 2013