1 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, 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 USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD Steven Chan NASA-JPL, Pasadena, CA NASA-JPL, Pasadena, CA IGARSS 2011, Vancouver, Canada, July, 2011

2 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, OUTLINE  Current PM SM Data Products  Single-Channel vs Multi-Channel Algorithms  Uncertainty Sensitivity Analysis  Summary

3 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, GSFC SMMR (Owe et al, 2001) GSFC SMMR (Owe et al, 2001) USDA TMI (Bindlish et al, 2003) USDA TMI (Bindlish et al, 2003) Princeton TMI (Gao et al, 2006) Princeton TMI (Gao et al, 2006) NASA AMSR-E (Njoku et al, 2003) NASA AMSR-E (Njoku et al, 2003) USDA AMSR-E (Jackson et al, 2007) USDA AMSR-E (Jackson et al, 2007) VUA AMSR-E (Owe et al, 2008) VUA AMSR-E (Owe et al, 2008) USDA WindSat (Jackson et al, 2008) USDA WindSat (Jackson et al, 2008) NRL WindSat (Li et al, 2008) NRL WindSat (Li et al, 2008) Current Satellite Soil Moisture Data Products:

4 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, T B,i cmp = T s {e r,i exp (-  i /cos  ) + (1 –  ) [1 – exp (-  i /cos  )] [1 + (1-e r,i )exp (-  i /cos  )]}  i = b *VWC e r,i = f(e s, h) e s = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model (Dobson et al) T B,i obs = T B06h, T B06v, T B10h, T B10v, T B18h, T B18v Multi-channel Inversion (MCI) Algorithm : (Njoku & Li, 1999)

5 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Land Parameter Retrieval Model (LPRM) : (Owe, de Jeu & Holms, 2008) T Bh cmp = T s {e h,r exp (-  /cos  ) + (1 –  ) [1 – exp (-  /cos  )] [1 + (1- e h,r )exp (-  /cos  )]}  = f(MPDI),MPDI = (T Bv -T Bh )/(T Bv +T Bh ) e h = f(e s, h, Q) e s = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model (Wang & Schmugge) T s = f(T B37v ) or T s LSM T Bh obs = T B06h, T B10h or T B18h

6 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Single Channel Retrieval Algorithm (SCA) : (Jackson, 1993) T B10h = T s [1 –(1-e r ) exp (-2  /cos  )]  = b * VWC, VWC = f(NDVI) e h = f(e v, h, Q) e s = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model T s = f(T B37v ) or T s LSM

7 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCR SM: Aug 4, 2010

8 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCA SM: Aug 5, 2010

9 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCA SM: Aug 6, 2010

10 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCA NDVI/VWC/tau: Aug 4, 2010

11 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCA NDVI/VWC/tau: Aug 5, 2010

12 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Retrieval Results: MCI LPRM SCA NDVI/VWC/tau: Aug 6, 2010

13 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, 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. Uncertainty Sensitivity Analysis Procedure:

14 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Land Parameter Retrieval Model (LPRM) : (Owe, de Jeu & Holms, 2008) T Bh cmp = T s {e h,r exp (-  /cos  ) + (1 –  ) [1 – exp (-  /cos  )] [1 + (1- e h,r )exp (-  /cos  )]}  = f(MPDI),MPDI = (T Bv -T Bh )/(T Bv +T Bh ) e h = f(e s, h, Q) e s = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model (Wang & Schmugge) T s = f(T B37v ) T Bh obs = T B10h

15 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Multi-channel Inversion with LPRM (MCI) : T Bh cmp = T s {e h,r exp (-  /cos  ) + (1 –  ) [1 – exp (-  /cos  )] [1 + (1- e h,r )exp (-  /cos  )]} e h = f(e s, h, Q) e s = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model (Wang & Schmugge) T s = f(T B37v ) T Bi obs = T B10h and T B10v

16 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of Tau = f(MPDI) on SM Retrievals: LPRM with tau = f(MPDI) MCI without tau = f(MPDI)

17 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of 2K Ts error on LPRM/MCI Retrievals: Ts + 2K Ts – 2K

18 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, No Ts errors

19 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of 2K Tb error on LPRM/MCI Retrievals: Tbh + 2K Tbv - 2K Tbh - 2K Tbv + 2K

20 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, No Tb errors

21 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, 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. Uncertainty Sensitivity Analysis Procedure:

22 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Inversed SM and Tau using SCA equns: tau SM

23 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of Tau error on SCA Retrievals: Tau No Tau error

24 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of Tau error on SCA Retrievals: Tau No Tau error

25 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, Impact of Tau error on SCA Retrievals: Tau No Tau error

26 X. Zhan, NOAA-NESDIS-STAR, IGARSS 2011, Vancouver, Canada, July, 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 T B37v for T s estimate to reduce iteration variable numbers in solution procedure. Its sensitivity to T B calibration, T s estimate and other parameter errors needs to be assessed.  Multi-channel Inversion algorithm is similar to LPRM algorithm when using the same T s estimates. Thus, the tau-MPDI relationship may not be the key for the LPRM success.