th4.l10.1: smos smap synergisms for the retrieval of soil moisture

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

YHK July 2010

•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

YHK July 2010

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

YHK July 2010

Vegetation opacity map June 20-23 2010

YHK July 2010

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

YHK July 2010

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

YHK July 2010

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

YHK Julay 2010

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

YHK July 2010

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/