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

1. Evaluation and Improvement of the AQUA/AMSR-E Soil Moisture Algorithm I. E. Mladenova, T. J. Jackson, R. Bindlish, M. CoshUSDA-ARS, Hydrology and Remote Sensing Lab, Beltsville, MD E. Njoku, S. Chan NASA, Jet Propulsion Lab, Pasadena, CA AMSR-E Science Team Meeting 28-29 June, 2011, Asheville, NC

2. Introduction Introduction ObjectivesTeamAlgorithmsEvaluation Summary • 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, … * Jackson et al. 2010 Jackson et al. (2010) Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE TGRS, 48(12), 4256-4272.

3. Objectives/Goals Introduction ObjectivesTeamAlgorithmsEvaluation Summary • 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.

4. Team & Collaborators Introduction ObjectivesTeamAlgorithmsEvaluation Summary • Team: • E. G. Njoku1* • T. J. Jackson2* • S. Chan1 • R. Bindlish2 • M. Cosh2 • I. E. Mladenova21NASA, 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

5. Available Algorithms Introduction ObjectivesTeamAlgorithms overview previous workEvaluation Summary • 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, IstitutodiFisicaApplicata(Paloscia) • NRLWINDSAT, Naval Research Laboratory (Li) • PrU, Princeton University (Gao & Wood)

6. Available Algorithms: Summary Introduction ObjectivesTeamAlgorithms overview previous workEvaluation 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.

7. Available Algorithms: Overview and examples Introduction ObjectivesTeamAlgorithms overview previous workEvaluation Summary UMo NASA VU-LPRM USDA-SCA JAXA IFA Aqua AMSR-E Descending 2007/06/28 Image courtesy of the JAXA and IFAC maps: JAXA

8. Available Algorithms: Overview and examples Introduction ObjectivesTeamAlgorithms overview previous workEvaluation Summary 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.

9. Available Algorithms: Overview and examples Introduction ObjectivesTeamAlgorithms overview previous workEvaluation Summary 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.

10. Available Algorithms: Overview and examples Introduction ObjectivesTeamAlgorithms overview previous workEvaluation Summary VU-LPRM NASA USDA-SCA AMSR-E time series were re-scaled using in situ data. x: AMSR-E retrieval –: station dataImage courtesy: Draper et al. 2009 JAXA Draper et al. (2009) An evaluation of AMSR-E derived soil moisture over Australia, RSE 113(4), 703-710.

11. Evaluation… Introduction ObjectivesTeamAlgorithmsEvaluation data sets stats Summary • 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

12. In situ Validation Data Sets Introduction ObjectivesTeamAlgorithmsEvaluation data sets stats Summary • 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.

13. In situ Validation Data Sets Introduction ObjectivesTeamAlgorithmsEvaluation data sets stats Summary • International Soil Moisture Network • Criteria to consider when selecting a soil moisture network Optimum USDA watersheds… Most… Low High Density Monthly Hourly Frequency Point Local Regional Global Scale Image modified after Jackson 2005, IGWCO Soil Moisture Working Group (ISMWG)

14. Additional Validation Data Sets Introduction ObjectivesTeamAlgorithmsEvaluation data sets stats Summary Continental/Global scale evaluation • Additional (independent data sets) • Other passive-derived soil moisture productse.g. SMOS • Radar-based soil moisture productse.g. ERS/ASCAT • Modeled outpute.g. Noah, ECMWF, … • Antecedent Precipitation Index, API

15. Evaluation statistics Introduction ObjectivesTeamAlgorithmsEvaluation data sets stats Summary • Error, RMSE/ubRMSE • Sample time series correlation, r Entekhabi et al. 2010 • 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 … … … Scipal et al. 2008

16. Summary Introduction ObjectivesTeamAlgorithmsEvaluation 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,…)