1. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Evaluation of the SMAP Combined Radar-Radiometer Soil Moisture Algorithm 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA 2 Massachusetts Institute of Technology, Cambridge, MA 02139, USA 3 University of California, Santa Barbara, CA 93106, USA IGARSS 2011 Paper #3398 N. N. Das 1 D. Entekhabi 2 S. K. Chan 1 R. S. Dunbar 1 S. Kim 1 E. G. Njoku 1 J. C. Shi 3

2. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 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, 3 rd & 4 th 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 Overview of the SMAP Mission

3. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 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

4. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California RequirementSoil Moisture Freeze/ Thaw Resolution 10 km3 km Refresh Rate 3 days2 days (1) Accuracy 0.04 [cm 3 cm -3 ] (2) 80% (2) Duration 36 months (1) North of 45°N Latitude (2) % volumetric water content, 1-sigma (3) % classification accuracy (binary: Freeze or Thaw) SMAP Level 1 Science Requirements Product Short Name Description Data Resolution L2_SM_PRadiometer Soil Moisture36 km L2_SM_ARadar Soil Moisture3 km L2_SM_A/PActive-Passive Soil Moisture9 km L2_F/T_HiResDaily Global Composite Freeze/Thaw State1-3 km L3_SM_PDaily Global Composite Radiometer Soil Moisture36 km L3_SM_A/PDaily Global Composite Active-Passive Soil Moisture9 km L4_SMSurface & Root Zone Soil Moisture9 km L4_CCarbon Net Ecosystem Exchange1 km

5. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Definitions and Data Products Flow L1_S0_HiRes σ Merge Algorithms C M nm nc = 1 nf = 144 nm = 16 C = Coarse (~36 km Radiometer) M nm = Medium (~9 km Merged Product) F nf = Fine (~3 km Radar) L1C_TB T B L2_SM_AP T B disaggregation (Das et al., Preliminary ATBD) (TGARS, submitted) F nf

6. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California L2_SM_AP Radar-Radiometer T B Disaggregation Algorithm Same evaluated at scale M j : Temporal Changes in T B and σ pp are Related. Relationship Parameter β is Estimated Statistically at Radiometer C- Scale Using Successive Overpasses: Subtract Two Equations to Write: DOY, 2002T Bh ~4 kmσ vv ~800 m 176 178 182 183 186 187 188 189 R 2 (Low: 0.65, High: 0.93) values between T Bh and σ vv dBK SMEX02

7. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California L2_SM_AP Radar-Radiometer Algorithm Heterogeneity in Vegetation and Roughness Conditions Estimated by Sensitivities Γ in Radar HV Cross-Pol: T B ( M j ) is Used to Retrieve Soil Moisture at 9 km T B -Disaggregation Algorithm is: Based on PALS Observations From: SGP99, SMEX02, CLASIC and SMAPVEX08

8. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Combined Airborne Data From: SGP99, SMEX02, CLASIC and SMAPVEX08 Active-Passive Algorithm Performance Minimum Performance Algorithm RMSE: 0.055 [cm 3 cm -3 ] Active-Passive Algorithm RMSE: 0.033 [cm 3 cm -3 ]

9. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California The Role of Cross-Pol in Capturing Heterogeneity Active-Passive Algorithm RMSE: 0.033 [cm 3 cm -3 ] Active-Passive Algorithm Without Cross-Pol RMSE: 0.043 [cm 3 cm -3 ] Minimum Performance Algorithm RMSE: 0.055 [cm 3 cm -3 ]

10. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California ParameterAdded Uncertainty (1 std.) Brightness Temperature T B 1.5 [K] Vegetation Opacity (τ)10% Soil Temprature2 [K] Single-Scattering Albedo (ω)5% Roughness (h)10% Sand fraction (sf)10% Clay fraction (cf)10% Study region selected from the CONUS domain. Assessment of L2_SM_AP Algorithm Using SMAP Algorithm Testbed

11. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Sample of L3_SM_AP Output from SMAP Algorithm Testbed V/V Global Composite Map of Soil Moisture for April 02

12. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 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

13. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California  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 Work in Progress

14. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Acknowledgements Andreas Colliander Jet Propulsion Laboratory Joel Johnson Ohio State University NASA SMAP Project