Evaluation and Improvement of the AQUA/AMSR-E Soil Moisture Algorithm AMSR-E Science Team Meeting...

Evaluation and Improvement of the AQUA/AMSR-E Soil Moisture

Algorithm

AMSR-E Science Team Meeting 28-29 June, 2011, Asheville, NC

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, Pasadena, CA

Introduction

Overall Almost a decade of soil moisture data products Used for a wide range of applications Extensively validated

Some validation issues* The ground area contributing (satellite footprint) is ambiguous. Day to day shifting of the satellite track results in different azimuth

angles The elliptical shape of the footprint means that a somewhat different area

contributes for each overpass. Nonlinearities in the radiative transfer processes as a result of land

cover, terrain, and soil types variability within the satellite footprint. Issues associated with ground data include: different sampling depths,

network density, accuracy of the sampling techniques, etc. Several well established retrieval algorithms

Strengths and weaknesses in the currently available retrieval techniques

Bias, narrow dynamic range, …

IntroductionObjectivesTeamAlgorithmsEvaluationSummary

* Jackson et al. 2010

Jackson et al. (2010) Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE TGRS, 48(12), 4256-4272.

Objectives/Goals

Evaluate the performance of the AMSR-E standard/baseline algorithm using ground based measurements, and assess its performance against alternative algorithms and soil moisture products.

Research will provide continuity for the existing Aqua/AMSR-E product, basis for transition of the algorithm to near-future missions, and will contribute to establishing a community algorithm applicable to multiple instruments and platforms.

Refine and test validation procedures & metrics. Develop a better understanding of the merits of the existing

algorithms. Algorithm(s) improvement.


Team & Collaborators

Team: E. G. Njoku1*

T. J. Jackson2*

S. Chan1

R. Bindlish2

M. Cosh2

I. E. Mladenova2

1NASA, Jet Propulsion Laboratory, Pasadena, CA2USDA-ARS, Hydrology and Remote Sensing Lab, Beltsville, MD*PI

Collaborators D. Bosch, G. C. Heathman, M. S. Moran, J. H. Prueger, M. Seyfried, P. J. Starks

USDA-ARS


Available Algorithms

Passive microwave algorithms suitable for soil moisture inversion from X-band brightness temperature observations

NASA, National Aeronautics Space Administration (Njoku & Chan) USDA-SCA, U.S. Department of Agriculture - Single Channel Algorithm

(Jackson) JAXA, Japan Aerospace Exploration Agency (Koike) VU-LPRM, Land Parameter Retrieval Model (Owe & de Jeu) UMo, University of Montana (Jones & Kimball) IFA, Istituto di Fisica Applicata (Paloscia) NRLWINDSAT, Naval Research Laboratory (Li) PrU , Princeton University (Gao & Wood)

IntroductionObjectivesTeamAlgorithms overview previous workEvaluationSummary

Available Algorithms: Summary

All algorithms are based on the τ-ω model. Each accounts for the effects of surface temperature and

vegetation; however, the way how this is done varies between the different algorithms.

Retrieved parameters: Soil moisture Additional (depending on algorithm): vegetation optical depth, surface

temperature, water fraction… Major differences:

Screening for RFI, frozen soils, dense vegetation, open water bodies. Assumptions and parameterization. Ancillary datasets, etc.


Available Algorithms: Overview and examples

Image courtesy of the JAXA and IFAC maps: JAXA

Aqua AMSR-EDescending2007/06/28

JAXA IFA

USDA-SCA VU-LPRM

NASA UMoIntroductionObjectivesTeamAlgorithms overview previous workEvaluationSummary

Image courtesy: Jackson et al. 2010

Jackson et al. (2010) Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE TGRS, 48(12), 4256-4272.



VU-LPRM

NASA

USDA-SCA

JAXA x: AMSR-E retrieval–: station dataImage courtesy: Draper et al. 2009

Draper et al. (2009) An evaluation of AMSR-E derived soil moisture over Australia, RSE 113(4), 703-710.

AMSR-E time series were re-scaled using in situ data.



Evaluation…

Assessment includes two aspects: evaluate the performance of the individual retrievals, and asses the accuracy of the resulting soil moisture products.

Previous AMSR-E evaluation studies

Selecting proper data sets statistics

IntroductionObjectivesTeamAlgorithmsEvaluation data sets statsSummary

In situ Validation Data Sets

International Soil Moisture Network Criteria to consider when selecting a soil moisture network

Image courtesy: Dorigo et al. 2011

Dorigo et al. (2011) The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, HESS, 15, 1675-1698.


In situ Validation Data Sets

International Soil Moisture Network Criteria to consider when selecting a soil moisture network

Den

sity

Freq

uenc

y

ScalePoint Local Regional Global

Mon

thly

H

ourly

Low

Hig

hOptimum

Image modified after Jackson 2005, IGWCO Soil Moisture Working Group (ISMWG)

Most…

USDA watersheds…


Additional Validation Data Sets

Continental/Global scale evaluation

Additional (independent data sets) Other passive-derived soil moisture products

e.g. SMOS Radar-based soil moisture products

e.g. ERS/ASCAT Modeled output

e.g. Noah, ECMWF, … Antecedent Precipitation Index, API


Evaluation statistics

Error, RMSE/ubRMSE

Sample time series correlation, r

])[( 2TEERMSE

}])][(])[{[( 2TTEE EEEubRMSE

222 bubRMSERMSE

TE

TTEE EEEr

])][])([[(

Error analysis using tree-way collocation statistics, triple collocationestimates RMSE (e2) “while simultaneously solving for systematic differences in each colligated data set”, is based on linear regression models, andrequires independent data sets

Entekhabi et al. 2010

Scipal et al. 2008

TTTT

SSSS

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rbarbarba

))((

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))((

****2*

****2*

****2*

TSTET

TSSES

TESEE

e

e

e

… … …


Summary

In depth evaluation of the NASA AMSR-E soil moisture product as well as available alternative retrieval methods that focuses on physical and algorithm sources of differences.

Algorithm improvement Link between the current AMSR-E and upcoming

missions (GCOM-W,…)


Evaluation and Improvement of the AQUA/AMSR-E Soil Moisture Algorithm AMSR-E Science Team Meeting...

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