RoccoPancieraMesiano sept25 2013

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Remote Sensing campaign in Australia by Rocco Panciera. Soil moisture retrieving. SAR, Hydraprobe, Smapex

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RoccoPancieraMesiano sept25 2013

  1. 1. Towards  Global  Remote  Sensing   of  Soil  Moisture:   Rocco  Panciera Wednesday, September 25, 13
  2. 2. The  last  10  years • 2003:        Master,  University  of  Trento,                                                                       Distributed  hydrological  modeling • 2004  –  2009:    PhD,  University  of  Melbourne,                                                   Passive  microwave  remote  sensing  of  soil  moisture,   • 2009  –  2010:    Research  Fellow,  University  of  Melbourne,  Passive   and  ac9ve  microwave  remote  sensing  of  soil  moisture • 2011  –  present:  Super  Science  Fellowship,  ARC                                                                     SAR  remote  sensing  of  soil  moisture 2 Wednesday, September 25, 13
  3. 3. AcIvity  Overview Instrument   Development Field  Experiments Research • Soil  moisture   monitoring  system • Airborne  SyntheIc   Aperture  Radar  (SAR) • NAFE’05 • NAFE’06 • SMAPEx-­‐1 • SMAPEX-­‐2 • SMAPEx-­‐3 • … 3 Wednesday, September 25, 13
  4. 4. Research  Overview Remote  sensing  of  Land  surface   Soil  Moisture Land  Cover VegetaAon  Biomass 4 Wednesday, September 25, 13
  5. 5. Research  Overview Remote  sensing  of  Land  surface   Soil  Moisture Land  Cover VegetaAon  Biomass LiDAR SAR Passive Microwave OpIcal/IR SAR SAR OpIcal/IR SAR: SyntheIc  Aperture  Radar 4 Wednesday, September 25, 13
  6. 6. (MANY)Field  Experiments 2011 Soil  Moisture  AcIve  Passive  Experiment  (SMAPEx) Dec  2010 AMSR-­‐E     ValidaIon  2004 NaIonal  Airborne  Field  Experiments  (NAFE) 20062005 Jul  2010 5 Wednesday, September 25, 13
  7. 7. Instrument  Development • Hydraprobe  Data  AcquisiIon  System  (HDAS) 6 Wednesday, September 25, 13
  8. 8. Instrument  Development • Hydraprobe  Data  AcquisiIon  System  (HDAS) Soil  moisture  (vol) VegetaIon  height  (cm)VegetaIon  Type 7 Wednesday, September 25, 13
  9. 9. Instrument  Development • Polarimetric  L-­‐band  Imaging  SAR  (PLIS) 8 Wednesday, September 25, 13
  10. 10. Instrument  Development • Polarimetric  L-­‐band  Imaging  Sca`erometer  (PLIS) SAR  SensiIvity: Soil  moisture Surface  roughness VegetaIon  structure VegetaIon  water  content VegetaIon  height Flight  path 3km 3km 15° 15° 45° 45° 9 Wednesday, September 25, 13
  11. 11. Airborne  Field  Experiments • Soil  Moisture  AcIve  Passive  Experiments  (SMAPEx) ~40km Soil  moisture  Sampling Surface  roughness  &  vegetaIon   sampling Panciera,  R.,  Walker,  J.P,  Jackson,  T  J.,  Ryu,  D.,  Gray,  D.,  Monerris,  A.,  Yardley,  H.,  Tanase,  M.,  Rudiger,  C.  et  al.,“The  Soil  Moisture  Ac9ve  Passive   Experiments  (SMAPEx):  Towards  Soil  Moisture  Retrieval  from  the  SMAP  Mission”,  IEEE  Transac9ons  of  Geoscience  and  Remote  Sensing,  51(9),  2013.   Passive   AcIve   10 Wednesday, September 25, 13
  12. 12. Airborne  Field  Experiments 11 Wednesday, September 25, 13
  13. 13. Soil  Moisture  from  SAR • SensiIvity  of  SAR  to  soil  moisture  (Mv) Bare  soil Canola  ~  140cm  height Wheat  ~  50cm  height -­‐20.0000 -­‐15.0000 -­‐10.0000 -­‐5.0000 0 2 4 6 8 10 SAR  dB Days SAR  HH-­‐pol Mv SAR  VV-­‐pol 12 IrrigaAon Wednesday, September 25, 13
  14. 14. Soil  Moisture  from  SAR • SensiIvity  of  SAR  to  soil  moisture 13 Rough Surface Smooth surface Surface  RMS  [cm] Wednesday, September 25, 13
  15. 15. Soil  Moisture  from  SAR • Time-­‐series  approach:  Backsca`er  dynamic  over  short   periods  solely  due  to  soil  moisture  changes Snapshot approach Time-­‐series approach 14 Wednesday, September 25, 13
  16. 16. Soil  Moisture  from  SAR • Time  series  approach  Using  1km  ALOS  PALSAR  data  in   Australia Satalino,  G.,  Maja,  F.,  Balenzano  A.,  Panciera,  R.,  Walker,  J.P,  “Soil  Moisture  Maps  from  Ime  series  of  PALSAR-­‐1  scansar  data  over  Australia”,   Proceedings  of  IEEE  Interna9onal  Geoscience  and  Remote  Sensing  Symposium  2013  (IGARSS  2013),  21-­‐26  July,  Melbourne,  Australia.   15 Wednesday, September 25, 13
  17. 17. Surface  roughness  from  LiDAR Turner,  R.,  Panciera,  R.,  Tanase,  M.,  Lowell,  K.,  Hacker,  J.,  Walker,  P.,  J.,”  Es9ma9on  of  Soil  Surface  Roughness  of  Agricultural  Soils  using  Airborne   LiDAR”,  Remote  Sensing  of  Environment,  In  review,  2013. 16 Wednesday, September 25, 13
  18. 18. Soil  Moisture  from  Passive  microwave • Algorithm  development  for  ESA’s  SMOS  for  Australian   condiIons Uncalibrated  parameter  “b”   Calibrated  parameter  “b”   (Jackson  and  Schmugge,  1991) Wheat/barley pastures Panciera,  R.,  Walker,  J.P.,  Kalma,  J.D.,  Kim  E.J.,  Saleh,  K.,  Wigneron,  J.-­‐P.,  “Evalua9on  of  the  SMOS  L-­‐MEB  passive  microwave  soil  moisture  retrieval   algorithm”.  Remote  Sensing  of  Environment,  113(2):  p.  435-­‐444,  2009.   17 Wednesday, September 25, 13
  19. 19. Soil  Moisture  from  AcIve/Passive  microwave • Downscaling  algorithm  development  for  NASA’s  SMAP   mission Airborne Simulated  SMAP AcIve/passive Downscaling  to   9km Downscaling  error   KPassive AcIve Passive AcIve 18 RMSE  =  1.5  –  5.8  K SMAP  target  =  2.4K Wednesday, September 25, 13
  20. 20. Soil  Moisture  from  Passive  &  OpIcal/NIR • Downscaled  SMOS  +  MODIS  1km  soil  moisture  product January  2-­‐14,  2011 Tropical  Cyclone  Oswald Piles,  M.,  Camps,  A.  ,  Vall-­‐llossera,  M.,  Corbella,  I.  Panciera,  R.,  Rudiger,  C.,  Kerr,  Y.  and  Walker,  J.,  “Downscaling  SMOS-­‐derived  soil  moisture  using   MODIS  visible/infrared  data”,  Accepted  for  publica9on  in  IEEE  Transac9on  on  Geoscience  and  Remote  Sensing,  TGRS-­‐2010-­‐00403.R1,  2010. 20 Wednesday, September 25, 13
  21. 21. Land  cover  from  SAR  &  opIcal   • Supervised  land  cover  classificaIon  using  Cosmos-­‐SkyMed   &  Landsat 19 Overall  classifica9on   Accuracy  (OA) Landsat5,  2  images OA  =  93% Cosmo-­‐SkyMed,  8   images,  HH  and  HV:  OA   =  80% Wednesday, September 25, 13
  22. 22. Forest  Biomass  from  SAR  and  LiDAR Tanase,  M,  R.  Panciera,  K.  Lowell,  C.  Aponte,  J.  M.  Hacker,  J.  P.  Walker,  “Forest  Biomass  Es9ma9on  at  High  Spa9al  Resolu9on:  Radar  vs.  Lidar   sensors”,  accepted  for  publica9on,  IEEE  Geoscience  and  Remote  Sensing  Le_ers;   21 Wednesday, September 25, 13
  23. 23. CollaboraIons • Consiglio  Nazionale  della  Ricerca,  Italy                                                                                               –  AcIve  microwave  &  land  cover  mapping • Jet  Propulsion  Laboratory,  Pasadena                                                                                                   –  AcIve  Microwave  (SMAP  mission) • United  States  Department  of  Agriculture                                                                                  –   AcIve/passive  microwave  (SMAP  mission) • European  Space  Agency                                                                                                                                              –   Passive  microwave  (SMOS  mission) • Australian  Defence  Science  and  Technology  OrganisaAon  (DSTO)                                                                                                                                                                                                             –  Airborne  SAR  development/calibraIon • Barcelona  SMOS  Expert  Centre                                                                                                                                 –  SMOS/MODIS  soil  moisture  product                                                                                                                                                                   22 Wednesday, September 25, 13
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