SMOS - SMAP synergisms for retrieval of soil

moisture

Y.H. Kerr, F Cabot, P. Richaume, A. AlBitar, E. Jacquette, A. Mialon, C Gruhier, S Juglea, D. Leroux,

A. Mahmoodi, J.P. Wigneron

IGARSS’10 Honolulu, HAWAII, July 26-30-2010

Layout

• Quick overview of SMOS and SMAP

• Comparison of specifications

• Spatial resolution issue

• Dis-aggregation

• Freeze thaw

• Conclusions

YHK July 2010

SMOS vs SMAP– Interferometer Scanning fixed angle– Always same point almost same point– Passive only active passive

• Spatio temporal resolution– 30-55 km, a/b<1.5 36 (9, 3) km– 3 day 3 day

• Sensitivity– 2. 4 K 0.1 K

• Angles– Up to 120 (0- 60°) 1 angleYHK July 2010

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•Each integration time, (2.4 s) a full scene is acquired (dual or full pol)•Average resolution 43 km, global coverage•A given point of the surface is thus seen with several angles•Maximum time (equator) between two acquisitions 3 days

Principle of operationsSMOS FOV; 755 km, 3x6, 33°, 0.875,

P. Waldteufel, 2003

SMOS SMAP

Typical SMOS browse productequivalent to SMAP data

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Algorithmic approaches

• Basic fundamentals are the same ( see other presentations) but….

• SMOS has several angles– meaning easier to infer the different

contributors– Vegegation opacity and others (rain, droughts….)– Surface roughness– Equivalent temperature

• SMAP has a better sensitivity

• Active system used for disagregation• Different physics involved

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Data acquired over one point

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Case of Forest

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But…• Both will need ancillary data

– Land use– Soil type and texture– Initial conditions– Meteorological conditions (snow, freeze,….– Water bodies– …

• Issue with varying footprint size?– No as

• Addressed in SMOS SM algorithm• Case for almost all sensors (AMSR, ASCAT,…SMAP)

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Thank You!

Soil moisture retrievals June 20 -23 2010

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Vegetation opacity map June 20-23 2010

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Spatial resolution issue• SMAP has a sophisticated algorithm using

active data (see presentations)– Probably very efficient but has to be validated

in in orbit data

goal 3 or 9 km

• SMOS is currently focused on 43 km target (though data provided at 15 km!)

• Higher resolution is currently level 4– Several approaches currently tested

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SMOS’ approach (1/2)

• Two prongs– Hydrology based (Pellenq et al , Boulet et al)

• Rationale– Topography, soil texture and depth, vegetation cover drives

the soil moisture evolution– Rainfall patterns drives the soil moisture initial distribution

• Approach– Use high resolution rainfall fields (from satellites) (but with

caution!)– Use a SVAT to redistribute the SMOS averaged

measurements

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Saturation exces Runoff

evaporation

infiltration Infiltration exces Runoff

PotentialEvaporation

Rain ptime

Inter-storm

StormInfiltration + Runoff

Évaporation +

percolation

SVATSimple

z

dE=edt

0

zf(t+dt)

K0dt

A=0dd

Wg

(Boulet and al. 2000)

Develop a 3 D Modelling including:

At the catchment scale

- vertical fluxes

- lateral transfers due

to topography

At local scale

- local soil water content fields

derived from topography,

surface proprieties

and mean

humidity information i=f(mean, topography,surface)

i

SVATSIMPLE TOPMODEL

SVAT HYDROLOGICAL MODEL

mean

Coupling and desegregation Scheme

Wg mean, W mean

t

SVATSIMPLE

LE, Rn,H,Gpercolation

Infiltration

Subsurface flow

Saturation excess Runoff

TOPMODEL

Wg mean, W meant + dt

{ Wg i }, { W i }t + dt Soil proprieties

+

DTM

OBSERVEDSimulated (topo) Simulated (topo and

soil depth)

DoY 275: “ wet Conditions”

DoY 291:dry Conditions

Surface soil moisture fieldsNerrigundah basin (Williams river, MDB, Australia)

SMOS’ approach (2/2)

• Two prongs (Merlin et al)– Signal based

• Rationale– The soil moisture distribution is visible through the

temperature field / evaporation rate

• Approach– Use high resolution Vis / NIR and Thermal infra red data to

redistribute teh SOIL moisture integrated values from SMOS

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Dis-aggregation• With use of higher resolution data (O. Merlin 2005, 2006, 2007)

Measured SM (SGP ’97)

Dis aggregated SM (O Merlin 2005)

SMOS pixel 40x40 km

AVHRR Pixels TIR

1 km

Dis-aggregation

Pixel to pixel comparison

Freeze - thaw• Important Science issue

• May have a very large impact on retrieval– Wet soil becomes dry– Free water on top– Dry and wet snow issue– Infra pixel comtributions

• Medium resolution radar is the best approach

• SMOS is limited in that field

• While SMAP should be very adequate

Freezing Event

SM drops

Bare Soil

Main synergisms

• Long term continuity– Overlap– Same core sites– ECV

• Freeze thaw

• Vegetation optical thickness

• Auxiliary data

• RFI….YHK July 2010

NEXT Steps• Business as usual

– Improve SMOS algorithm and keep on Cal Val activities– Use SMOS for simulating SMAP data and test algorithms– Feed back on RFI and other issues

• Have – hopefully- an overlap SMOS Aquarius SMAP – To intercalibrate (long time series) – To select optimal design for next generation– To improve design

• SMOS and SMAP have very close objectives and specifications– Could be the start of a long time series of global SM fields– Need for for common and long term ground sitesYHK July 2010

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Summary• SMOS delivers first global maps of soil moisture and

vegegation opacity and SMAP should do so in 4 years time• Different approaches but similar goals

– Many view angles versus better sensitivity and use of active?– Firsts tests can be carried out using SMOS data?

• Spatial resolution enhancement – To be validated with real data when available.. Should be similar– Overall goal and specifications equivalent

• Definite advantage to SMAP for Freeze thaw issue.• SMOS can deliver vegetation opacity• Very similar goals with very different systems:• Need to be intercompared to identify which technology for

the next generation• Need to use common Cal Val sites (underway!)

• Visit our Blog http://www.cesbio.ups-tlse.fr/SMOS_blog/