1. Evaluation of Alternatives for Improving the AMSR Soil Moisture Product AMSR-E TIM Meeting 4-5 Sep, 2012, Oxnard, CA. I. E. Mladenova, T. J. Jackson, R. Bindlish, M. Cosh USDA-ARS, Hydrology and Remote Sensing Lab, Beltsville, MD E. Njoku, S. Chan NASA, Jet Propulsion Lab, Cal Tech, Pasadena, CA

2. 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 Motivation Options Summary Current passive microwave retrieval algorithms produce very different soil moisture estimates!

3. 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… Motivation Options 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.

4. Keep the dataset in the archive as is… Is this the best we can do? Option [1]: Do nothing Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

5. SM AMSR-E Is it accurate? ALG Alt SMn ds Stat metrics Error Estimates SM 1 AMSR-E ALG 1 ALG NASA ALG n SM NASA SM n 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 Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary Option [2]: Examine the underlying theory, Overview

6. TB SM Image courtesy: https://www.meted.ucar.edu/ https://www.meted.ucar.edu/ Atmospheric contribution Canopy attenuation Roughness effect Canopy + Soil Physical Temperature RTE DM RTE – Radiative Transfer Equations DM – Dielectric Mixing modeling TB – brightness temperature SM – 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 Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

7.  RTE-based Empirical solution: NASA Option [2]: Examine the underlying theory, Algorithm Minimal roughness effect. SM computed as a departure from minimal conditions. Soil moisture Baseline * soil moisture Microwave polarization ratio Baseline * polarization ratio Empirically calibrated coefficients Combined vegetation-roughness parameter *bare, dry soil Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

8. Option [2]: Examine the underlying theory, Algorithm Baseline moisture conditions Temporal response Range  2003  2003-2010 Modifications: Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

9. [A] [B] [C]=[A]+[B] [A`] [B`] [C`]=[A`]+[B`], where NASA SMAP VWC Rawls et al NASA UPDATED Canopy Residual SM Min Moisture Baseline moisture conditions SM residual 0.05 m 3 /m 3 Canopy f (MPDI dry )

10. Option [2]: Examine the underlying theory, Algorithm Temporal response 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 Response Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

11. Option [2]: Examine the underlying theory, Algorithm Temporal Response Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

12. 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 Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary ApproachRRMSE ORG0.550.047 CDF0.560.059 VARS0.550.059 OLS0.550.058 TLS0.780.041 Mn/Mx0.550.054 TCA0.550.063 TLS+Mn/Mx*0.780.034 Ref: Noah(ASCAT) AMSR-E: Asc LW

13.  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 ε ref > ε sat ε ref ~ ε sat ε ref < ε sat 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. Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary

14. Option [4]: Alternative Algorithm [1]NASANational Aeronautics Space Administration (Njoku & Chan) [2]USDA-SCAU.S. Department of Agriculture-Single Channel Algorithm (Jackson) [3]VU-LPRMLand Parameter Retrieval Model (Owe & de Jeu) [4]UMTUniversity of Montana (Jones & Kimball) [5]IFAC-Hydroalgo(HA)-ANNIstituto di Fisica Applicata (Paloscia & Santi) Motivation Options [1] Nothing [2] Theoretical [3] Stat rescaling [4] Alt. algorithm Summary Why SCA? Validation exercises suggest that this algorithm performs as well as alternatives. Jackson et al. 2010 Watershed specific Average from all 4 watersheds

15. Validation exercises suggest that this performs as well as alternatives Option [4]: Alternative Algorithm R AVR NOAHASCAT ANASA0.3890.362 SCA0.4420.423 DNASA0.3620.339 SCA0.4370.420 Motivation Options [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

16. Summary  Do nothing Limited 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 product 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 SCA; more extensive global validation. Motivation Options Summary

17. 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. Motivation Options Summary

18.  Choose one of the four options as the direction for the AMSR-E product  Reprocess the entire AMSR-E data set (if Option 2 or 4 is selected)  Implement and test algorithm code  Quantitative assessments of AMSR2 SM product  Statistical comparisons of AMSR-E and AMSR2 SM products Motivation Options Summary Summary: What Needs to be Resolved for AMSR2