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Evaluation of Alternatives for Improving the AMSR Soil Moisture Product
AMSR-E TIM Meeting4-5 Sep, 2012, Oxnard, CA.
I. E. Mladenova, T. J. Jackson, R. Bindlish, M. CoshUSDA-ARS, Hydrology and Remote Sensing Lab, Beltsville, MD
E. Njoku, S. ChanNASA, Jet Propulsion Lab, Cal Tech, Pasadena, CA
Improve the operational NASA AMSR-E approach and/or product if possible.” …
…“ Dampened temporal response, Narrow dynamic range & Bias.”…
AMSR-E, descending overpass; Time period: 2003-2010; * 5/95 percentile
>0.45<0.05
Motivation
MotivationOptions Summary
Current passive microwave retrieval algorithms produce very different soil moisture estimates!
Options
[1] Do nothing…[2] Modify the algorithm (based on theory)[3] Modify the final product (stat rescaling)[4] Switch to an alternative retrieval approach
… temporal response and dynamic range…
MotivationOptions Summary
Evaluate the performance of the NASA AMSR-E standard/baseline algorithm using ground-based measurements, and assess its performance against alternative algorithms and soil moisture products.
Keep the dataset in the archive as is…Is this the best we can do?
Option [1]: Do nothing
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
SM
AMSR-E
Is it accurate?
ALG
Alt SMn ds
Stat metrics
Error Estimates
SM1
AMSR-E
ALG1 ALGNASA ALGn
SMNASA SMn
Why are they different?{Given that they are all based on the same principles}
Improve the NASA algorithm
SM – Soil Moisture Alt SMn ds – alternative soil moisture datasets
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Option [2]:Examine the underlying theory, Overview
TB
SMImage courtesy: https://www.meted.ucar.edu/
Atmospheric contribution
Canopy attenuationRoughness effect
Canopy + Soil Physical Temperature
RTE
DM
RTE – Radiative Transfer EquationsDM – Dielectric Mixing modelingTB – brightness temperatureSM – Soil Moisture
geophysical parameters
derived simultaneously
(with the SM retrieval)
--- (NASA)Fixed (SCA)RTE (LPRM, UMT)
--- (NASA)Regression (SCA, LPRM)RTE (UMT)
MPDI, fixed prms* (NASA)Ancillary, fixed prms (SCA)RTE, fixed prms (LPRM)RTE, --- (UMT)
geophysical parameters determined
a priori
Option [2]:Examine the underlying theory, Overview
Generating a ‘combined’
or ‘universal’ retrieval
approach… may not be
feasible.
NASA SCA LPRM UMT HA-ANN
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
RTE-based Empirical solution: NASA
Option [2]: Examine the underlying theory, Algorithm
Minimal roughness effect.SM computed as a departure from minimal conditions.
g
ba
MPDI
MPDI
SM
SM
dry
dry
,,,
Soil moisture Baseline* soil moisture Microwave polarization ratio Baseline* polarization ratio Empirically calibrated coefficients Combined vegetation-roughness parameter *bare, dry soil
HV
HV
gbdry
dry
TBTB
TBTBMPDI
MPDIMPDIbgbaSM
MPDIaag
][
102
10
2exp)(
)ln(
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Option [2]: Examine the underlying theory, Algorithm
][102
2exp)( gbdryMPDIMPDIbgbaSM
Baseline moisture conditions
Temporal response
Range
)ln(10dryMPDIaag
dryMPDI 2003 2003-2010
0 )( dryMPDIMPDI
Modifications:
2MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
[A]
[B]
[C]=[A]+[B]
[A`]
[B`]
[C`]=[A`]+[B`]
)ln(10dryMPDIaag , where
NASA
NASA
NASA SMAP VWC
Rawls et al
NASAUPDATED
Cano
pyRe
sidu
al S
MM
in M
oist
ure
Base
line
moi
stur
e co
nditi
ons
gba 02
SMresidual
0.05 m3/m3
Canopyf(MPDIdry)
Option [2]: Examine the underlying theory, Algorithm
][102
2exp)( gbdryMPDIMPDIbgbaSM
Temporal response
2
2][,*
]2[**SCA
][MINMINNASA
cos where,e1
e)( )...(SM
e)ζ(ζ )...ζ(ζSM 2
GTs
TBe
efef
ff
GhhX
gb
An example of the vegetation-roughness correction term used by SCA and NASA, where each plot represents a monthly composite generated using data from May 2007
Temporal ResponseMotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Option [2]: Examine the underlying theory, Algorithm
Temporal ResponseMotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
TLS + MnMx -> temporal response and dynamic range
6 stat-based approaches Cumulative Distr. Function (CDF) Variance (VARS) Regression
Ordinary Least Squares (OLS) Total Least Squares (TLS)
Min/Max (Mn/Mx) Triple Collocation Analysis (TCA)
Option [3]: Statistical Rescaling, Final Product
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Approach R RMSEORG 0.55 0.047CDF 0.56 0.059
VARS 0.55 0.059OLS 0.55 0.058TLS 0.78 0.041
Mn/Mx 0.55 0.054TCA 0.55 0.063
TLS+Mn/Mx* 0.78 0.034
Ref: Noah(ASCAT) AMSR-E: Asc
LW
TLS+Mn/Mx => more accurate product with reasonable dynamic range.
Need of long-term global reference data set How to determine the Min/Max? More extensive evaluation:
More locations Robustness
εref > εsat εref ~ εsat εref < εsat
Option [3]: Statistical Rescaling, Final Product
TLS assumes error in both the satellite and the reference
dataset, thus the correction requires simultaneous
observations, which can be an issue in an operational setup.
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Option [4]: Alternative Algorithm[1] NASA National Aeronautics Space Administration (Njoku & Chan) [2] USDA-SCA U.S. Department of Agriculture-Single Channel Algorithm (Jackson) [3] VU-LPRM Land Parameter Retrieval Model (Owe & de Jeu)[4] UMT University of Montana (Jones & Kimball) [5] IFAC-Hydroalgo(HA)-ANN Istituto di Fisica Applicata (Paloscia & Santi)
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
Why SCA?
Validation exercises suggest that this algorithm performs as well as alternatives.
WG LR
0
0.0500000000000001
0.1
0.15
0.2
JAXANASASCALPRM
ARS Watershed
RM
SE
WG LW LR RC-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
JAXANASASCALPRM
ARS Watershed
R
Jackson et al. 2010
Watershed specificAverage from all 4 watersheds
Validation exercises suggest that this performs as well as alternatives
Option [4]: Alternative Algorithm
RAVR NOAH ASCAT A NASA 0.389 0.362 SCA 0.442 0.423 D NASA 0.362 0.339 SCA 0.437 0.420
MotivationOptions [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary
[Desc]
Heritage with AMSR-E Baseline for Aquarius and SMAP Physically based on RTE
Summary
Do nothingLimited usefulness of the product.
Theoretical modifications Some improvement can be achieved by recalibrating selected
NASA algorithm coefficients; the MDPI response does not always effectively capture the change in soil moisture conditions.
Statistical-based rescaling of the final productAddresses the shortcomings of the available retrievals; requires a
long-term reference dataset and simultaneous observations.
Replace the NASA AMSR-E algorithm with an alternative approach
SCA; more extensive global validation.
MotivationOptions Summary
Summary (Considering AMSR2)
Do nothing (Yes, but same limitations)Limited usefulness of the product.
Theoretical modifications (Yes, but same limitations) Some improvement can be achieved by recalibrating selected
NASA algorithm coefficients; the MDPI response does not always effectively capture the change in soil moisture conditions.
Statistical-based rescaling of the final product (No)Addresses the shortcomings of the available retrievals; requires a
long-term reference dataset and simultaneous observations.
Replace the NASA AMSR-E algorithm with an alternative approach (Yes)
SCA; more extensive global validation.
MotivationOptions Summary
Choose one of the four options as the direction for the AMSR-E productReprocess the entire AMSR-E data set (if Option 2 or 4 is selected)Implement and test algorithm codeQuantitative assessments of AMSR2 SM productStatistical comparisons of AMSR-E and AMSR2 SM products
MotivationOptions Summary
Summary:What Needs to be Resolved for AMSR2