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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) - PowerPoint PPT Presentation
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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
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
Data
• Regional area of NAFE’06
• 1km resolution PLMR data: TB
• 1km resolution MODIS (Terra/Aqua) data: Tsurf, NDVI
Illustration
Impact of SM and vegetation on TB and Tsurf
Carlson et al., 1995
Illustration
Impact of SM on TB and Tsurf
75K 25K
4K7K
Illustration
Impact of vegetation on TB and Tsurf
65K 15K
2K
5K
TB SMRetrieval algo
LAI
Illustration
Impact of vegetation on TB: multi-spectral retrieval
Sensitivity of Tsurf to SM:downscaling
TB/SM
Tsurf
Downscaling algo
SM
Synergy L-band/optical
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
SMRetrieval algo
Tsurf
MODIS
TB
PLMR
LAI
MODIS
Teff = f(Tsurf,T2)
VWC = 0.5 LAI
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.0000 1.0000 2.0000 3.0000 4.0000
1. SM retrievalApplication to NAFE’06 regional area (Yanco)
Assumptions:veg para, roughness uniformStanding water = Bare soil with SM 100% v/v Retrieval algo
TBH Angle
SM
Tsurf LAI
1. SM retrievalComparison with ground measurements at the PLMR scale
Model parameters:Sand = 30%Clay = 30%b = 0.15OMEGA = 0.05T2 = 20degCH = 0.1
RMSE = 3.2% v/v Bias ~ 10 % v/v
70% of pixels 30% of pixels
322320318317313
311309308307306
304
40km0 8 18 >4025 33
Preliminary SM product
SM (% v/v)
2. SM downscaling
Downscaling algo
MODIS
SM
MODIS
SM
NDVI
Tsurf
Test a downscaling technique of ~40km SMOS like data from MODIS data
2. SM downscaling
minsoil,maxsoil,
soilmax,soilSEFTT
TT
Approach: SEF (soil evaporative fraction) as a proxy of surface soil moisture
MODIS SEF derived from triangle method
Ta
Tmax
NDVI
Tsurf
NDVImin NDVImax
Tsoil
2. SM downscaling
MODISMODISSMOS SEFSEFfWW
A downscaling relationship
2. SM downscaling
MODISMODISSMOS SEFSEFWfWW
Modified downscaling relationship
One difficulty: the non-linearity of SEF to SM
Generated SM (% v/v)
EF
(%
v/v
) SEF
2. SM downscaling
Modified downscaling relationship
SEF model Komatsu, 2003
arWc
WSEF
1exp1
0model
MODISMODISmodelSMOS SEFSEFWfWW
2. SM downscalingCorrelation between MODIS SEF and PLMR SM
SM sensitivity of Tsurf ~ SM sensivity of TB /10
2. SM downscalingLimitations and applicability:
Dry-end conditions (Tmax)
Uncertainty in SEF is high: need to aggregate to lower resolution
Could account for heterogeneity of soil
ConclusionsIllustrated 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)-…