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WHY DIFFERENT PASSIVE MICROWAVE ALGORITHMS GIVE DIFFERENT SOIL MOISTURE RETRIEVALS

WHY DIFFERENT PASSIVE MICROWAVE ALGORITHMS GIVE DIFFERENT SOIL MOISTURE RETRIEVALS. Xiwu Zhan, Jicheng Liu NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD Thomas Holmes, Wade Crow, Tom Jackson USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD Steven Chan

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WHY DIFFERENT PASSIVE MICROWAVE ALGORITHMS GIVE DIFFERENT SOIL MOISTURE RETRIEVALS

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  1. WHY DIFFERENT PASSIVE MICROWAVE ALGORITHMS GIVE DIFFERENT SOIL MOISTURE RETRIEVALS Xiwu Zhan, Jicheng Liu NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD Thomas Holmes, Wade Crow, Tom Jackson USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD Steven Chan NASA-JPL, Pasadena, CA IGARSS 2011, Vancouver, Canada, 24-27 July, 2011

  2. OUTLINE • Current PM SM Data Products • Single-Channel vs Multi-Channel Algorithms • Uncertainty Sensitivity Analysis • Summary

  3. Current Satellite Soil Moisture Data Products: • GSFC SMMR (Owe et al, 2001) • USDA TMI (Bindlish et al, 2003) • Princeton TMI (Gao et al, 2006) • NASA AMSR-E (Njoku et al, 2003) • USDA AMSR-E (Jackson et al, 2007) • VUA AMSR-E (Owe et al, 2008) • USDA WindSat (Jackson et al, 2008) • NRL WindSat (Li et al, 2008)

  4. Multi-channel Inversion (MCI) Algorithm : (Njoku & Li, 1999) TB,icmp = Ts {er,i exp (-i/cos) + (1 – ) [1 – exp (-i/cos)] [1 + (1-er,i)exp (-i/cos)]} i = b *VWC er,i= f(es, h) es = f(ε) -- Fresnel Equation ε= f(SM) -- Mixing model (Dobson et al) TB,iobs= TB06h, TB06v , TB10h , TB10v , TB18h , TB18v

  5. Land Parameter Retrieval Model (LPRM) : (Owe, de Jeu & Holms, 2008) TBhcmp = Ts {eh,r exp (-/cos) + (1 – ) [1 – exp (-/cos)] [1 + (1- eh,r)exp (-/cos)]} = f(MPDI) ,MPDI = (TBv-TBh)/(TBv+TBh) eh =f(es, h, Q) es = f(ε) -- Fresnel Equation ε= f(SM) -- Mixing model (Wang & Schmugge) Ts= f(TB37v) or TsLSM TBhobs= TB06h,TB10h or TB18h

  6. Single Channel Retrieval Algorithm (SCA) : (Jackson, 1993) TB10h = Ts [1 –(1-er) exp (-2 /cos)] = b * VWC, VWC = f(NDVI) eh =f(ev, h, Q) es = f(ε) -- Fresnel Equation ε= f(SM) -- Mixing model Ts= f(TB37v) or TsLSM

  7. Retrieval Results: SM: Aug 4, 2010 MCI LPRM SCR

  8. Retrieval Results: SM: Aug 5, 2010 MCI LPRM SCA

  9. Retrieval Results: SM: Aug 6, 2010 MCI LPRM SCA

  10. Retrieval Results: NDVI/VWC/tau: Aug 4, 2010 MCI LPRM SCA

  11. Retrieval Results: NDVI/VWC/tau: Aug 5, 2010 MCI LPRM SCA

  12. Retrieval Results: NDVI/VWC/tau: Aug 6, 2010 MCI LPRM SCA

  13. Uncertainty Sensitivity Analysis Procedure: MCI and LPRM: 1. LPRM converges while MCI sometimes not; 2. Remove tau=f(MPDI) from LPRM and use Ts = f(Tb37v) for MCI; 3. Perturb Tb37v, Tbh & Tbv for LPRM and MCI to test how they are sensitive to their errors.

  14. Land Parameter Retrieval Model (LPRM) : (Owe, de Jeu & Holms, 2008) TBhcmp = Ts {eh,r exp (-/cos) + (1 – ) [1 – exp (-/cos)] [1 + (1- eh,r)exp (-/cos)]} = f(MPDI) ,MPDI = (TBv-TBh)/(TBv+TBh) eh =f(es, h, Q) es = f(ε) -- Fresnel Equation ε= f(SM) -- Mixing model (Wang & Schmugge) Ts= f(TB37v) TBhobs= TB10h

  15. Multi-channel Inversion with LPRM (MCI) : TBhcmp = Ts {eh,r exp (-/cos) + (1 – ) [1 – exp (-/cos)] [1 + (1- eh,r)exp (-/cos)]} eh =f(es, h, Q) es = f(ε) -- Fresnel Equation ε= f(SM) -- Mixing model (Wang & Schmugge) Ts= f(TB37v) TBiobs= TB10h and TB10v

  16. Impact of Tau = f(MPDI) on SM Retrievals: LPRM with tau = f(MPDI) MCI without tau = f(MPDI)

  17. Impact of 2K Ts error on LPRM/MCI Retrievals: Ts + 2K Ts – 2K

  18. No Ts errors No Ts errors

  19. Impact of 2K Tb error on LPRM/MCI Retrievals: Tbh + 2K Tbv - 2K Tbh - 2K Tbv + 2K

  20. No Tb errors No Tb errors

  21. Uncertainty Sensitivity Analysis Procedure: SCA: 1. Use GLDAS SM inverse tau with SCA eqns; 2. Use the inversed tau to retrieve SM as reference; 3. Perturb Tb37v, Tbh for SCA retrievals to test how they are sensitive to these errors.

  22. Inversed SM and Tau using SCAequns: tau SM

  23. Impact of Tau error on SCA Retrievals: Tau + 0.01 No Tau error

  24. Impact of Tau error on SCA Retrievals: Tau + 0.05 No Tau error

  25. Impact of Tau error on SCA Retrievals: Tau + 0.1 No Tau error

  26. SUMMARY • The difference of current satellite soil moisture products may confuse users. • Single-Channel Algorithm relies heavily on accuracy of tau estimates. • LPRM algorithm uses a tau-MPDI relationship and TB37v for Ts estimate to reduce iteration variable numbers in solution procedure. Its sensitivity to TB calibration, Ts estimate and other parameter errors needs to be assessed. • Multi-channel Inversion algorithm is similar to LPRM algorithm when using the same Ts estimates. Thus, the tau-MPDI relationship may not be the key for the LPRM success.

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