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Soil moisture estimates over Niger from satellite sensors (T. Pellarin, M. Zribi)

Soil moisture estimates over Niger from satellite sensors (T. Pellarin, M. Zribi). Passive satellite sensors. AMSR-E onboard the AQUA platform. Passive sensor at 6, 10, 18, 36, 85 GHz 55 km (regridded to 25 km) 2 polarizations 1 incidence angle 55°

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Soil moisture estimates over Niger from satellite sensors (T. Pellarin, M. Zribi)

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  1. Soil moisture estimates over Niger from satellite sensors (T. Pellarin, M. Zribi)

  2. Passive satellite sensors AMSR-E onboard the AQUA platform • Passive sensor at 6, 10, 18, 36, 85 GHz • 55 km (regridded to 25 km) • 2 polarizations • 1 incidence angle 55° • Sun-synchroneous orbit (1.30 am 1.30 pm) • Measurements since june 2002

  3. 2002 2003 2004 2005 2006 2002 2003 2004 2005 2006 AMSR-E raw measurements TBH TBV Banizoumbou, Niger (13,54°N ; 2,66°E) Djougou, Benin (9,7°N ; 1,68°E)

  4. TBV - TBH PR = TBV + TBH Vegetation attenuation Vegetation attenuation AMSR-E raw measurements Positive variation of PR during 15 consecutive days 1 july to 15 july 2004 16 july to 31 july 2004

  5. AMSR-E raw measurements Positive variation of PR during 4 consecutive days 9 august to 13 august 2004

  6. Rain seems to stop Rain does not reach the soil AMSR-E raw measurements Positive variation of PR during 4 consecutive days Meteosat MCS Tracking 9 august to 13 august 2004

  7. Soil moisture products

  8. Objective and methodology • Validate high resolution soil moisture maps uing low resolution AMSR TB measurement • Look at the within pixel soil moisture variability ISBA outputs* (1km²) TB (1km²) TB (25x25km²) agreggation ISBA C-MEB Simulations Modification of the C-MEB code Modification of the ISBA code Atmosph. Forcing Land Cover TB AMSR-E (25x25km²) Measurements In-situ soil moisture measurements ISBA outputs* : surface soil moisture, soil temperature, vegetation water content, water interception by the vegetation

  9. Tondikiboro  AMSR-E 25x25 km² reggrided Surface soil moisture measurementsCampbell CS616

  10. 4% 12% Evaporation Runoff Drainage 84% Surface soil moisture simulationsSVAT vs. Campbell CS616 2004 ISBA standard

  11. 0% 29% 4% 12% Evaporation Runoff Drainage 71% 84% Surface soil moisture simulationsSVAT vs. Campbell CS616 2004 ISBA standard 2004 ISBA standard + Ksat(crust) = 1E-7 m/s + Ksat(sub-soil) = 5E-5 m/s (Vandervaere et al. 1997, Esteves and Lapetite, 2003)

  12. Meso scale simulationsISBA (1km²) Rainrate from raingauges (5x5 km², 5 min.) Studied area (140x120 km²) LAI from Cyclopes (1km², 10 days)

  13. Simulated TB 1km 55km footprint 55km AMSR-E TB Level 3 25km product Meso scale TB simulationsISBA + C-MEB (1km²)C-band Microwave Emission of the Biosphere (Pellarin et al., 2006) Simulated TB 1km Simulated TB 25km-reggrided

  14. Within pixel variabilitySoil moisture comparison (1km² vs. 25x25 km²)

  15. Local scale measurement vs. AMSR-E product

  16. Monitoring of surface soil moisture based on ASAR/ENVISAT radar data over Kori Diantandou site (Niger)

  17. Active satellite sensors Scatterometer and SAR onboard the ERS platform ASAR onboard the ENVISAT platform • Active sensor at 6 GHz (C-band) • 30m resolution • 2 polarizations • n incidence angles (18 to 59°) • Sun-synchroneous orbit • Measurements since 2002 • Active sensor at 6 GHz (C-band) • 55 km resolution • 2 polarizations • n incidence angles (18 to 59°) • Sun-synchroneous orbit (10.30 am 11.00 pm) • Measurements since 1991

  18. Site

  19. Soil moisture estimation in Western Africa(A new approach based on ERS/WSC)

  20. Radar images dry season radar image SPOT/HRV DTM NDVI and NDWI mapping * Registration * incidence angle correction of images Mask of high NDVI (NDVI>0.25) Mask of pools Mask of high slopes (m>3%) global mask • A mean radar signal estimation on • 100 X 100 pixels (out of the mask) • More than 20% of pixels must be • out of the mask Elimination of roughness effect using dry season image sHH=a1*Mv1+c1 sVV=a2 *Mv2+c2 Mv=(Mv1+Mv2)/2

  21. Dantiandou site • Satellite measurements • ASAR-ENVISAT, SPOT • Ground truth measurements • Soil moisture (IRD, L. Descroix)

  22. Date sample spacing size Polarisations Angle Orbital path 17-02-2004 12.5m X 12.5 m HH/VV IS1 descending 05-08-2004 12.5m X 12.5 m HH/VV IS1 ascending 30-08-2004 12.5m X 12.5 m HH/VV IS1 descending 09-09-2004 12.5m X 12.5 m HH/VV IS1 ascending 14-09-2004 12.5m X 12.5 m HH/VV IS1 descending 01-02-2005 12.5m X 12.5 m HH/VV IS1 descending 15-02-2005 12.5m X 12.5 m HH/VV IS2 ascending 05-07-2005 12.5m X 12.5 m HH/VV IS1 ascending 07-07-2005 12.5m X 12.5 m HH/VV IS2 descending 21-07-2005 12.5m X 12.5 m HH/VV IS1 ascending 26-07-2005 12.5m X 12.5 m HH/VV IS1 descending 09-08-2005 12.5m X 12.5 m HH/VV IS2 ascending 11-08-2005 12.5m X 12.5 m HH/VV IS2 descending 30-08-2005 12.5m X 12.5 m HH/VV IS1 descending 15-09-2005 12.5m X 12.5 m HH/VV IS2 descending Radar images details

  23. Land surface

  24. Vegetation cover dynamic

  25. pool and relief identification

  26. Incidence angle correction, IS1, IS2 data Is1: incidence angle ranged between 15 and 22° Is2: incidence angle ranged between 19 and 26°

  27. Results, application of the algorithm, HH, VV data • High correlation between radar data and soil moisture • High coherence between IS1 and IS2 normalised data

  28. Validation of inversion approach • Application of inversion empirical approach over • different test sites

  29. Mapping of soil moisture b a d c Figure 9. Estimated soil maps of the Kori Dantiandou region, generated from ASAR data and our soil moisture algorithm on four different dates: (a) 6 July 2004; (b) 14 September 2004; (c) 11 August 2005; (d) 30 August 2005.

  30. Conclusions • Considered data: IS1, IS2 • Normalisation of radar data to one incidence angle • Estimation of radar signal over bare soil and low vegetation cover • An empirical linear relationship is established between moisture and processed radar signal • A mapping of soil moisture is proposed in 15 dates in 2004 and 2005

  31. Surface soil moisture AMSR-E product, 20060802

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