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

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)-…