TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE
description
Transcript of 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
YHK July 2010
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
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/