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Synergy of L-band and optical data for soil moisture monitoring

Synergy of L-band and optical data for soil moisture monitoring. O. Merlin, J. Walker and R. Panciera. 3 rd NAFE workshop 17-18 sept. 2007. Objective. Use synergy optical/passive microwave for improving 1. Accuracy (passive microwave scale) OR 2. Spatial resolution (downscaling)

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Synergy of L-band and optical data for soil moisture monitoring

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  1. Synergy of L-band and optical data for soil moisture monitoring O. Merlin, J. Walker and R. Panciera 3rd NAFE workshop 17-18 sept. 2007

  2. Objective Use synergy optical/passive microwave for improving 1. Accuracy (passive microwave scale) OR 2. Spatial resolution (downscaling) of L-band derived soil moisture retrievals

  3. Data • Regional area of NAFE’06 • 1km resolution PLMR data: TB • 1km resolution MODIS (Terra/Aqua) data: Tsurf, NDVI

  4. Illustration Carlson et al., 1995 Impact of SM and vegetation on TB and Tsurf

  5. Illustration 7K 4K 25K 75K Impact of SM on TB and Tsurf

  6. Illustration 5K 2K 15K 65K Impact of vegetation on TB and Tsurf

  7. Sensitivity of Tsurf to SM: downscaling Tsurf LAI SM TB Retrieval algo SM TB/SM Downscaling algo Illustration Synergy L-band/optical Impact of vegetation on TB: multi-spectral retrieval

  8. VWC = 0.5 LAI MODIS LAI Retrieval algo SM PLMR TB MODIS Tsurf Teff = f(Tsurf,T2) 1. SM retrieval • RT model: • TAU-OMEGA formalism Mo et al., 1982 • soil roughness (H,Q) Wang and Choudhury, 1981 • Teff = f(Tsurf,T2) Wigneron et al., 2001 • TAU = bVWC Jackson and Schmugge, 1991 Inverse model: Minimize (TBobs - TBsim)2

  9. TBH Angle Tsurf LAI Retrieval algo SM 1. SM retrieval Application to NAFE’06 regional area (Yanco) Assumptions: veg para, roughness uniform Standing water = Bare soil with SM 100% v/v

  10. 1. SM retrieval Comparison with ground measurements at the PLMR scale Model parameters: Sand = 30% Clay = 30% b = 0.15 OMEGA = 0.05 T2 = 20degC H = 0.1 RMSE = 3.2% v/v Bias ~ 10 % v/v 70% of pixels 30% of pixels

  11. 304 Preliminary SM product SM (% v/v) 40km 0 8 18 25 33 >40 306 307 308 309 311 313 317 318 322 320

  12. MODIS NDVI SM SM Downscaling algo MODIS Tsurf 2. SM downscaling Test a downscaling technique of ~40km SMOS like data from MODIS data

  13. 2. SM downscaling Approach: SEF (soil evaporative fraction) as a proxy of surface soil moisture Tsurf Tmax Tsoil MODIS SEF derived from triangle method Ta NDVI NDVImin NDVImax

  14. 2. SM downscaling A downscaling relationship

  15. SEF EF (% v/v) Generated SM (% v/v) 2. SM downscaling One difficulty: the non-linearity of SEF to SM Modified downscaling relationship

  16. 2. SM downscaling SEF model Komatsu, 2003 Modified downscaling relationship

  17. 2. SM downscaling Correlation between MODIS SEF and PLMR SM SM sensitivity of Tsurf ~ SM sensivity of TB /10

  18. 2. SM downscaling Limitations and applicability: Dry-end conditions (Tmax) Uncertainty in SEF is high: need to aggregate to lower resolution Could account for heterogeneity of soil

  19. Conclusions • Illustrated two applications of the synergy between optical and passive microwave data • Preliminary SM product with accuracy ~4% v/v for 70% of the validation area (fitted with roughness H) • An example of downscaling technique of SMOS type data from 1km MODIS type data • Some questions: • stripes on PLMR TB images • bias in retrieved SM over 30% validation pixels (not explained by any parameter) • …

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