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