soil moisture estimates over niger from satellite sensors (t. pellarin, m. zribi)

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