SMOS - SMAP synergisms for retrieval of soil moisture Y.H. Kerr, F Cabot, P. Richaume, A. AlBitar, E. Jacquette, A. Mialon...
Layout <ul><li>Quick overview of SMOS and SMAP </li></ul><ul><li>Comparison of specifications </li></ul><ul><li>Spatial re...
SMOS vs SMAP <ul><ul><li>Interferometer Scanning fixed angle </li></ul></ul><ul><ul><li>Always same point almost  same poi...
YHK July 2010 <ul><li>Each integration time, (2.4 s) a full scene is acquired (dual or full pol) </li></ul><ul><li>Average...
Typical SMOS browse product equivalent to SMAP data YHK July 2010
Algorithmic approaches <ul><li>Basic fundamentals are the same ( see other presentations) but…. </li></ul><ul><li>SMOS has...
Data acquired over one point YHK July 2010
Case of Forest YHK July 2010
But… <ul><li>Both will need ancillary data </li></ul><ul><ul><li>Land use </li></ul></ul><ul><ul><li>Soil type and texture...
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 <ul><li>SMAP has a sophisticated algorithm using active data (see presentations) </li></ul><ul><u...
SMOS’ approach (1/2) <ul><li>Two prongs </li></ul><ul><ul><li>Hydrology based (Pellenq et al , Boulet et al) </li></ul></u...
SVATSimple  z (Boulet and al. 2000) Saturation exces Runoff e vaporation infiltration Infiltration exces Runoff Potential ...
Develop a 3 D Modelling including: <ul><li>At the catchment scale </li></ul><ul><li>- vertical fluxes </li></ul><ul><li>- ...
Coupling and desegregation Scheme  Wg mean,  W mean t SVATSIMPLE LE, Rn,H,G percolation Infiltration Subsurface  flow Satu...
OBSERVED Simulated (topo) Simulated (topo and  soil depth) DoY 275: “ wet Conditions”  DoY 291:dry Conditions  Surface soi...
SMOS’ approach (2/2) <ul><li>Two prongs (Merlin et al) </li></ul><ul><ul><li>Signal based </li></ul></ul><ul><ul><ul><li>R...
Dis-aggregation <ul><li>With use of higher resolution data (O. Merlin 2005, 2006, 2007) </li></ul>Measured SM (SGP ’97)  D...
Freeze - thaw <ul><li>Important Science issue </li></ul><ul><li>May have a very large impact on retrieval </li></ul><ul><u...
Freezing Event SM drops Bare Soil
Main synergisms <ul><li>Long term continuity </li></ul><ul><ul><li>Overlap </li></ul></ul><ul><ul><li>Same core sites </li...
NEXT Steps <ul><li>Business as usual </li></ul><ul><ul><li>Improve SMOS algorithm and keep on Cal Val activities </li></ul...
Summary <ul><li>SMOS  delivers first global maps of soil moisture and vegegation opacity and SMAP should do so in 4 years ...
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TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE

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  • Ici on s&apos;interesse aux caracteristiques locales qui peuvent expliquer la variablilite spatiale des champs d&apos;humidite. Si on ne prend en compte que la topographie et qu&apos;on considere une profondeur de sol moyenne partout, on s&apos;apercoit qu&apos;on arrive a expliquer la zone humide de fond de vallon mais que l&apos;on n&apos;arrive pas a expliquer la zone plus humide a droite. En revanche si on prend en compte la profondeur de sol on explique assez bien la tendance generale.
  • TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE

    1. 1. 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
    2. 2. Layout <ul><li>Quick overview of SMOS and SMAP </li></ul><ul><li>Comparison of specifications </li></ul><ul><li>Spatial resolution issue </li></ul><ul><li>Dis-aggregation </li></ul><ul><li>Freeze thaw </li></ul><ul><li>Conclusions </li></ul>YHK July 2010
    3. 3. SMOS vs SMAP <ul><ul><li>Interferometer Scanning fixed angle </li></ul></ul><ul><ul><li>Always same point almost same point </li></ul></ul><ul><ul><li>Passive only active passive </li></ul></ul><ul><li>Spatio temporal resolution </li></ul><ul><ul><li>30-55 km, a/b<1.5 36 (9, 3) km </li></ul></ul><ul><ul><li>3 day 3 day </li></ul></ul><ul><li>Sensitivity </li></ul><ul><ul><li>2. 4 K 0.1 K </li></ul></ul><ul><li>Angles </li></ul><ul><ul><li>Up to 120 (0- 60°) 1 angle </li></ul></ul>YHK July 2010
    4. 4. YHK July 2010 <ul><li>Each integration time, (2.4 s) a full scene is acquired (dual or full pol) </li></ul><ul><li>Average resolution 43 km , global coverage </li></ul><ul><li>A given point of the surface is thus seen with several angles </li></ul><ul><li>Maximum time (equator) between two acquisitions 3 days </li></ul>Principle of operations SMOS FOV; 755 km, 3x6, 33°, 0.875  , P. Waldteufel, 2003 SMOS SMAP
    5. 5. Typical SMOS browse product equivalent to SMAP data YHK July 2010
    6. 6. Algorithmic approaches <ul><li>Basic fundamentals are the same ( see other presentations) but…. </li></ul><ul><li>SMOS has several angles </li></ul><ul><ul><li>meaning easier to infer the different contributors </li></ul></ul><ul><ul><ul><ul><li>Vegegation opacity and others (rain, droughts….) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Surface roughness </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Equivalent temperature </li></ul></ul></ul></ul><ul><li>SMAP has a better sensitivity </li></ul><ul><li>Active system used for disagregation </li></ul><ul><ul><ul><li>Different physics involved </li></ul></ul></ul>YHK July 2010
    7. 7. Data acquired over one point YHK July 2010
    8. 8. Case of Forest YHK July 2010
    9. 9. But… <ul><li>Both will need ancillary data </li></ul><ul><ul><li>Land use </li></ul></ul><ul><ul><li>Soil type and texture </li></ul></ul><ul><ul><li>Initial conditions </li></ul></ul><ul><ul><li>Meteorological conditions (snow, freeze,…. </li></ul></ul><ul><ul><li>Water bodies </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Issue with varying footprint size? </li></ul><ul><ul><li>No as </li></ul></ul><ul><ul><ul><li>Addressed in SMOS SM algorithm </li></ul></ul></ul><ul><ul><ul><li>Case for almost all sensors (AMSR, ASCAT,…SMAP) </li></ul></ul></ul>YHK July 2010
    10. 10. Thank You! Soil moisture retrievals June 20 -23 2010
    11. 11. YHK July 2010 Vegetation opacity map June 20-23 2010
    12. 12. YHK July 2010
    13. 13. Spatial resolution issue <ul><li>SMAP has a sophisticated algorithm using active data (see presentations) </li></ul><ul><ul><li>Probably very efficient but has to be validated in in orbit data </li></ul></ul><ul><li> goal 3 or 9 km </li></ul><ul><li>SMOS is currently focused on 43 km target (though data provided at 15 km!) </li></ul><ul><li>Higher resolution is currently level 4 </li></ul><ul><ul><li>Several approaches currently tested </li></ul></ul>YHK July 2010
    14. 14. SMOS’ approach (1/2) <ul><li>Two prongs </li></ul><ul><ul><li>Hydrology based (Pellenq et al , Boulet et al) </li></ul></ul><ul><ul><ul><li>Rationale </li></ul></ul></ul><ul><ul><ul><ul><li>Topography, soil texture and depth, vegetation cover drives the soil moisture evolution </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Rainfall patterns drives the soil moisture initial distribution </li></ul></ul></ul></ul><ul><ul><ul><li>Approach </li></ul></ul></ul><ul><ul><ul><ul><li>Use high resolution rainfall fields (from satellites) (but with caution!) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Use a SVAT to redistribute the SMOS averaged measurements </li></ul></ul></ul></ul>YHK July 2010
    15. 15. SVATSimple z (Boulet and al. 2000) Saturation exces Runoff e vaporation infiltration Infiltration exces Runoff Potential Evaporation Rain p t ime Inte r-storm Storm Infiltration + Runoff Évaporation + percolation dE=edt  0  z f (t+dt) K 0 dt A=  0 d d Wg
    16. 16. Develop a 3 D Modelling including: <ul><li>At the catchment scale </li></ul><ul><li>- vertical fluxes </li></ul><ul><li>- lateral transfers due </li></ul><ul><li>to topography </li></ul><ul><li>At local scale </li></ul><ul><li>- local soil water content fields </li></ul><ul><li>derived from topography, </li></ul><ul><li>surface proprieties </li></ul><ul><li>and mean </li></ul><ul><li>humidity information </li></ul> i =f(  mean , topography,surface)  i SVATSIMPLE TOPMODEL SVAT HYDROLOGICAL MODEL  mean
    17. 17. Coupling and desegregation Scheme Wg mean, W mean t SVATSIMPLE LE, Rn,H,G percolation Infiltration Subsurface flow Saturation excess Runoff TOPMODEL { Wg i }, { W i } t + dt Soil proprieties + DTM Wg mean, W mean t + dt
    18. 18. OBSERVED Simulated (topo) Simulated (topo and soil depth) DoY 275: “ wet Conditions” DoY 291:dry Conditions Surface soil moisture fields Nerrigundah basin (Williams river, MDB, Australia)
    19. 19. SMOS’ approach (2/2) <ul><li>Two prongs (Merlin et al) </li></ul><ul><ul><li>Signal based </li></ul></ul><ul><ul><ul><li>Rationale </li></ul></ul></ul><ul><ul><ul><ul><li>The soil moisture distribution is visible through the temperature field / evaporation rate </li></ul></ul></ul></ul><ul><ul><ul><li>Approach </li></ul></ul></ul><ul><ul><ul><ul><li>Use high resolution Vis / NIR and Thermal infra red data to redistribute teh SOIL moisture integrated values from SMOS </li></ul></ul></ul></ul>YHK Julay 2010
    20. 20. Dis-aggregation <ul><li>With use of higher resolution data (O. Merlin 2005, 2006, 2007) </li></ul>Measured SM (SGP ’97) Dis aggregated SM (O Merlin 2005) Dis-aggregation SMOS pixel 40x40 km AVHRR Pixels TIR 1 km Pixel to pixel comparison
    21. 21. Freeze - thaw <ul><li>Important Science issue </li></ul><ul><li>May have a very large impact on retrieval </li></ul><ul><ul><li>Wet soil becomes dry </li></ul></ul><ul><ul><li>Free water on top </li></ul></ul><ul><ul><li>Dry and wet snow issue </li></ul></ul><ul><ul><li>Infra pixel comtributions </li></ul></ul><ul><li>Medium resolution radar is the best approach </li></ul><ul><li>SMOS is limited in that field </li></ul><ul><li>While SMAP should be very adequate </li></ul>
    22. 22. Freezing Event SM drops Bare Soil
    23. 23. Main synergisms <ul><li>Long term continuity </li></ul><ul><ul><li>Overlap </li></ul></ul><ul><ul><li>Same core sites </li></ul></ul><ul><ul><li>ECV </li></ul></ul><ul><li>Freeze thaw </li></ul><ul><li>Vegetation optical thickness </li></ul><ul><li>Auxiliary data </li></ul><ul><li>RFI…. </li></ul>YHK July 2010
    24. 24. NEXT Steps <ul><li>Business as usual </li></ul><ul><ul><li>Improve SMOS algorithm and keep on Cal Val activities </li></ul></ul><ul><ul><li>Use SMOS for simulating SMAP data and test algorithms </li></ul></ul><ul><ul><li>Feed back on RFI and other issues </li></ul></ul><ul><li>Have – hopefully- an overlap SMOS Aquarius SMAP </li></ul><ul><ul><li>To intercalibrate (long time series) </li></ul></ul><ul><ul><li>To select optimal design for next generation </li></ul></ul><ul><ul><li>To improve design </li></ul></ul><ul><li>SMOS and SMAP have very close objectives and specifications </li></ul><ul><ul><li>Could be the start of a long time series of global SM fields </li></ul></ul><ul><ul><li>Need for for common and long term ground sites </li></ul></ul>YHK July 2010
    25. 25. Summary <ul><li>SMOS delivers first global maps of soil moisture and vegegation opacity and SMAP should do so in 4 years time </li></ul><ul><li>Different approaches but similar goals </li></ul><ul><ul><li>Many view angles versus better sensitivity and use of active? </li></ul></ul><ul><ul><li>Firsts tests can be carried out using SMOS data? </li></ul></ul><ul><li>Spatial resolution enhancement </li></ul><ul><ul><li>To be validated with real data when available.. Should be similar </li></ul></ul><ul><ul><li>Overall goal and specifications equivalent </li></ul></ul><ul><li>Definite advantage to SMAP for Freeze thaw issue. </li></ul><ul><li>SMOS can deliver vegetation opacity </li></ul><ul><li>Very similar goals with very different systems: </li></ul><ul><li>Need to be intercompared to identify which technology for the next generation </li></ul><ul><li>Need to use common Cal Val sites (underway!) </li></ul><ul><li>Visit our Blog  http://www.cesbio.ups-tlse.fr/SMOS_blog/ </li></ul>YHK July 2010

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