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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.
IntroductionObjectivesTeamAlgorithmsEvaluationSummary
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
IntroductionObjectivesTeamAlgorithmsEvaluationSummary
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.
IntroductionObjectivesTeamAlgorithms overview previous workEvaluationSummary
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.
Available Algorithms: Overview and examples
IntroductionObjectivesTeamAlgorithms 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.
Available Algorithms: Overview and examples
IntroductionObjectivesTeamAlgorithms overview previous workEvaluationSummary
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.
Available Algorithms: Overview and examples
IntroductionObjectivesTeamAlgorithms overview previous workEvaluationSummary
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.
IntroductionObjectivesTeamAlgorithmsEvaluation data sets statsSummary
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…
IntroductionObjectivesTeamAlgorithmsEvaluation data sets statsSummary
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
IntroductionObjectivesTeamAlgorithmsEvaluation data sets statsSummary
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*
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****2*
TSTET
TSSES
TESEE
e
e
e
… … …
IntroductionObjectivesTeamAlgorithmsEvaluation data sets statsSummary
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,…)
IntroductionObjectivesTeamAlgorithmsEvaluationSummary