TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE
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TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE

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  • Ici on s'interesse aux caracteristiques locales qui peuvent expliquer la variablilite spatiale des champs d'humidite. Si on ne prend en compte que la topographie et qu'on considere une profondeur de sol moyenne partout, on s'apercoit qu'on arrive a expliquer la zone humide de fond de vallon mais que l'on n'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 Presentation Transcript

  • 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. Layout
    • Quick overview of SMOS and SMAP
    • Comparison of specifications
    • Spatial resolution issue
    • Dis-aggregation
    • Freeze thaw
    • Conclusions
    YHK July 2010
  • 3. 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 angle
    YHK July 2010
  • 4. 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 operations SMOS FOV; 755 km, 3x6, 33°, 0.875  , P. Waldteufel, 2003 SMOS SMAP
  • 5. Typical SMOS browse product equivalent to SMAP data YHK July 2010
  • 6. 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
  • 7. Data acquired over one point YHK July 2010
  • 8. Case of Forest YHK July 2010
  • 9. 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)
    YHK July 2010
  • 10. Thank You! Soil moisture retrievals June 20 -23 2010
  • 11. YHK July 2010 Vegetation opacity map June 20-23 2010
  • 12. YHK July 2010
  • 13. 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
  • 14. 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
  • 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. 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
  • 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. 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. 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
  • 20. Dis-aggregation
    • With use of higher resolution data (O. Merlin 2005, 2006, 2007)
    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. 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
  • 22. Freezing Event SM drops Bare Soil
  • 23. Main synergisms
    • Long term continuity
      • Overlap
      • Same core sites
      • ECV
    • Freeze thaw
    • Vegetation optical thickness
    • Auxiliary data
    • RFI….
    YHK July 2010
  • 24. 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 sites
    YHK July 2010
  • 25. 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/
    YHK July 2010