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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° - PowerPoint PPT Presentation
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Soil moisture estimates over Soil moisture estimates over Niger from satellite sensorsNiger from satellite sensors
(T. Pellarin, M. Zribi)(T. Pellarin, M. Zribi)
• 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
AMSR-E onboard the AQUA platform
Passive satellite Passive satellite sensorssensors
Banizoumbou, Niger Banizoumbou, Niger (13,54°N ; 2,66°E)(13,54°N ; 2,66°E)
Djougou, Djougou, Benin (9,7°N ; Benin (9,7°N ;
1,68°E)1,68°E)
2002 2003 2004 2005 2006
2002 2003 2004 2005 2006
TBH TBV
AMSR-E raw AMSR-E raw measurements measurements
TBV - TBH
TBV + TBHPR =
Positive variation of PR during 15 consecutive days
1 july to 15 july 2004 16 july to 31 july 2004
AMSR-E raw AMSR-E raw measurements measurements
Vegetation attenuation Vegetation attenuation
Positive variation of PR during 4 consecutive days
AMSR-E raw AMSR-E raw measurements measurements
9 august to 13 august 2004
9 august to 13 august 2004
Rain does not reach the soil
AMSR-E raw AMSR-E raw measurements measurements
Meteosat MCS
Tracking
Positive variation of PR during 4 consecutive days
Rain seems to stop
Soil moisture products Soil moisture products
ISBAoutputs*
(1km²)
TB(1km²)
TB(25x25km²)
C-MEB agreggation
In-situ soil moisture
measurements
TB AMSR-E(25x25km²)
Modification of the ISBA code
Modification of the C-MEB code
Measu
rem
ents
Sim
ula
tions
Atmosph.ForcingLandCover
ISBA
ISBA outputs* : surface soil moisture, soil temperature, vegetation water content, water interception by the vegetation
Objective and methodology Objective and methodology
• Validate high resolution soil moisture maps uing low resolution AMSR TB measurement
• Look at the within pixel soil moisture variability
Surface soil moisture Surface soil moisture measurementsmeasurementsCampbell CS616Campbell CS616
Tondikiboro AMSR-E
25x25 km² reggrided
ISBA standard
Evaporation
Runoff
Drainage
Surface soil moisture Surface soil moisture simulationssimulationsSVAT vs. Campbell CS616 SVAT vs. Campbell CS616
84%
12%4%
2004
ISBA standard + Ksat(crust) = 1E-7 m/s + Ksat(sub-soil) = 5E-5 m/s
(Vandervaere et al. 1997, Esteves and Lapetite, 2003)
ISBA standard
84%
12%4%
Evaporation
Runoff
Drainage
71%
29%
0%
Surface soil moisture Surface soil moisture simulationssimulationsSVAT vs. Campbell CS616 SVAT vs. Campbell CS616
2004
2004
Rainrate from raingauges Rainrate from raingauges (5x5 km², 5 min.) (5x5 km², 5 min.)
LAI from Cyclopes LAI from Cyclopes (1km², 10 days) (1km², 10 days)
Studied area Studied area (140x120 km²)(140x120 km²)
Meso scale Meso scale simulationssimulationsISBA (1km²)ISBA (1km²)
Simulated TB 1km
Simulated TB 1km55km footprint55km
Meso scale TB simulationsMeso scale TB simulationsISBA + C-MEB (1km²)ISBA + C-MEB (1km²)C-band Microwave Emission of the Biosphere (Pellarin et al., C-band Microwave Emission of the Biosphere (Pellarin et al., 2006)2006)
Simulated TB25km-reggrided
AMSR-E TB Level 325km product
Within pixel variabilityWithin pixel variabilitySoil moisture comparison (1km² vs. 25x25 Soil moisture comparison (1km² vs. 25x25 km²)km²)
Local scale measurement Local scale measurement vs. AMSR-E productvs. AMSR-E product
Monitoring of surface soil moisture based on ASAR/ENVISAT radar data over Kori Diantandou site
(Niger)
• 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
ASAR onboard the ENVISAT platform
Scatterometer and SAR onboard the ERS platform
Active satellite sensorsActive satellite sensors
• Active sensor at 6 GHz (C-band)• 30m resolution• 2 polarizations• n incidence angles (18 to 59°)• Sun-synchroneous orbit• Measurements since 2002
Site
Soil moisture estimation in Western Africa(A new approach based on ERS/WSC)
0
5
10
15
20
25
30
0 200 400 600 800 1000 1200 1400 1600
Day (1991-1995)
Soi
l moi
stur
e es
timat
ion
(%)
moisture. Model
estimation ERS
dry season radar image Radar images SPOT/HRV DTM
* Registration * incidence angle correction of images
NDVI and NDWI mapping
Mask of high NDVI(NDVI>0.25)
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
=1*Mv1+c1
VV=2 *Mv2+c2
Mask of pools
Mv=(Mv1+Mv2)/2
Elimination of roughness effect using dry season image
•Satellite measurementsASAR-ENVISAT, SPOT
•Ground truth measurementsSoil moisture (IRD, L. Descroix)
Dantiandou site
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
Land surface
Vegetation cover dynamic
pool and relief identification
Incidence angle correction, IS1, IS2 data
y = -0.28x - 6.36
R2= 0.8367
y = -0.37x - 5.34
R2 = 0.8624
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
10 15 20 25 30
incidence angle (degrees)
Ra
da
r si
gn
al (
dB
)
IS1 data
IS2 data
Is1: incidence angle ranged between 15 and 22°Is2: incidence angle ranged between 19 and 26°
Results, application of the algorithm, HH, VV data
y = 0.32x - 0.35
R2 = 0.69y = 0.33x - 0.71
R2 = 0.66
-5
-3
-1
1
3
5
7
9
0 5 10 15 20soil moisture (%)
pro
cess
ed
ra
da
r si
gn
al (
dB
)
IS1 data
IS2 data
HH polarisation
IS1 dataIS1+IS2 data
y = 0.27x - 0.14
R2 = 0.73
y = 0.29x - 0.58
R2 = 0.70
-5
-3
-1
1
3
5
7
9
0 5 10 15 20
soil moisture (%)p
roce
sse
d r
ad
ar
sig
na
l (d
B)
IS1 data
IS2 data
IS1 dataIS1+IS2 data
vv polarisation
High correlation between radar data and soil moistureHigh coherence between IS1 and IS2 normalised data
Validation of inversion approach
0
5
10
15
20
0 5 10 15 20
Soil moisture measurements (%)
Est
imat
ed m
oist
ure
(%)
HH
VV
Rms=2.2%
•Application of inversion empirical approach overdifferent test sites
Mapping of soil moisture
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.
a b
c d
Conclusions
Considered data: IS1, IS2Normalisation of radar data to one incidence angleEstimation of radar signal over bare soil and low vegetation coverAn empirical linear relationship is established between moisture and processed radar signalA mapping of soil moisture is proposed in 15 dates in 2004 and 2005
Surface soil moisture AMSR-E product, 20060802