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