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    2_3398_IGARS2011.pptx 2_3398_IGARS2011.pptx Presentation Transcript

    • Evaluation of the SMAP Combined
      Radar-Radiometer
      Soil Moisture Algorithm
      IGARSS 2011
      Paper #3398
      N. N. Das1
      D. Entekhabi2
      S. K. Chan1
      R. S. Dunbar1
      S. Kim1
      E. G. Njoku1
      J. C. Shi3
      1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA
      2Massachusetts Institute of Technology, Cambridge, MA 02139, USA
      3University of California, Santa Barbara, CA 93106, USA
    • Overview of the SMAP Mission
      SMAP Measurements Approach
      Radar
      Frequency: 1.26 GHz
      Polarizations: VV, HH, HV
      Resolution: 3 km
      Relative Accuracy: 1.0 dB (HH ,VV), 1.5 dB (HV)
      Radiometer
      Frequency: 1.41 GHz
      Polarizations: H, V, 3rd & 4th Stokes
      Resolution: 40 km
      Relative Accuracy: 1.3 K
      Shared Antenna
      Constant Incidence Angle: 40º
      Wide Swath: 1000 km
      Orbit
      Sun-Synchronous, 6 am/pm Orbit, 680 km
    • National Aeronautics and Space Administration
      Jet Propulsion Laboratory
      California Institute of Technology
      Pasadena, California
      L-band Active/Passive Assessment
      • Soil Moisture Retrieval Algorithms Build on Heritage of Microwave Modeling and Field Experiments
      MacHydro’90, Monsoon’91, Washita92, Washita94, SGP97, SGP99, SMEX02, SMEX03, SMEX04, SMEX05, CLASIC, SMAPVEX08, CanEx10
      • Radiometer - High Accuracy (Less Influenced by Roughness and Vegetation) but
      Coarser Resolution (40 km)
      • Radar- High Spatial Resolution (1-3 km) but More Sensitive to Surface Roughness and Vegetation
      Combined Radar-Radiometer Product Provides
      Blend of Measurements for Intermediate Resolution
      and Intermediate Accuracy
    • SMAP Level 1 Science Requirements
      (1) North of 45°N Latitude
      (2) % volumetric water content, 1-sigma
      (3) % classification accuracy (binary: Freeze or Thaw)
    • Definitions and Data Products Flow
      nc = 1
      L1C_TB TB
      TBdisaggregation
      (Das et al., Preliminary ATBD)
      (TGARS, submitted)
      C
      nm = 16
      L2_SM_AP
      Merge Algorithms
      Mnm
      nf = 144
      L1_S0_HiRes
      σ
      Fnf
      C= Coarse (~36 km Radiometer)
      Mnm= Medium (~9 km Merged Product)
      Fnf= Fine (~3 km Radar)
    • L2_SM_AP Radar-Radiometer TB Disaggregation Algorithm
      DOY, 2002
      TBh~4 km
      σvv ~800 m
      176
      SMEX02
      Temporal Changes in TBand σppare Related. Relationship Parameter β is Estimated Statistically at Radiometer C-Scale Using Successive Overpasses:
      178
      182
      183
      186
      Same evaluated at scale Mj:
      187
      188
      Subtract Two Equations to Write:
      189
      dB
      K
      R2(Low: 0.65, High: 0.93) values between TBhand σvv
    • L2_SM_AP
      Radar-Radiometer Algorithm
      Heterogeneity in Vegetation and Roughness Conditions Estimated by Sensitivities Γin Radar HV Cross-Pol:
      Based on PALS Observations From: SGP99, SMEX02, CLASIC and SMAPVEX08
      TB-Disaggregation Algorithm is:
      TB( Mj) is Used to Retrieve Soil Moisture at 9 km
    • Active-Passive Algorithm Performance
      Active-Passive Algorithm
      RMSE: 0.033 [cm3 cm-3]
      Minimum Performance Algorithm
      RMSE: 0.055 [cm3 cm-3]
      Combined Airborne Data From: SGP99, SMEX02, CLASIC and SMAPVEX08
    • The Role of Cross-Pol in Capturing Heterogeneity
      Minimum Performance Algorithm
      RMSE: 0.055 [cm3 cm-3]
      Active-Passive Algorithm Without Cross-Pol
      RMSE: 0.043[cm3 cm-3]
      Active-Passive Algorithm
      RMSE: 0.033 [cm3 cm-3]
    • Assessment of L2_SM_AP Algorithm Using SMAP Algorithm Testbed
      Study region selected from the CONUS domain.
    • Sample of L3_SM_AP
      Output from SMAP Algorithm Testbed
      Global Composite Map of Soil Moisture for April 02
      V/V
    • Summary
      • PALS data verifies that the assumption (linear TB-log[σ] relationship) holds well as the basis for the L2_SM_A/P algorithm
      • With current baseline approach, the algorithm meets the SMAP Level-1 requirements
      • Algorithm relies on radar co-pols and cross-pols
      • L2_SM_AP processor developed in SMAP Testbed
    • Work in Progress
      • Optimize length of temporal window (balance between phenology and statistical robustness)
      • Develop and mature algorithm prior parameters database for Bayesian estimation
      • Develop and mature L2_SM_A/P error budget table
    • Acknowledgements
      Andreas Colliander
      Jet Propulsion Laboratory
      Joel Johnson
      Ohio State University
      NASA SMAP Project