autonomous atmospheric correction and model based land use ...€¦ · tiger: esa/unesco initiative...
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 1
Autonomous atmospheric correction and model based land use
classification of CHRIS data of the AquiferEx test-sites in Tunisia
Heike Bach, Wolfgang Eder, Silke Begiebing
VISTA GmbHRemote Sensing in Geosiences
www.vista-geo.de
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ESA/ESRIN – AQUIFEREOEP-DUEP-EOPS-SW04-0005
2AQUIFER Project
Remote Sensing for Management of Transboundary Aquifers in Africa
• TIGER Aquifer = demonstrator projectTIGER: ESA/UNESCO initiative with focus: Space – Water – Africa
• ESA Aquifer funded / embedded withinESA – DUE: DATA USER ELEMENT
• UNESCO SASS, UNESCO IHP
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ESA/ESRIN – AQUIFEREOEP-DUEP-EOPS-SW04-0005
3AQUIFER Project
Product and Service Responsibility
“Operational” Products and Services: - PHASE 2 and PHASE 31. Land Use/Land Cover Maps and Change Maps Local Providers/
SCOT-F2. Digital Terrain Models Telespazio -I / GAF-D3. Water Abstraction Estimation JR - A4. Surface Water Extension and Dynamics GAF - D
“Science” Products: - PHASE 35. Refined Land Use Map Product VISTA - D6. Subsidence Monitoring and Assoc. Error Maps Telespazio - I7. Refined Water Abstraction Estimation JR - A8. Water Vegetation Monitoring over entire Aquifer Uni Jena - D9. ETA and Water Balance VISTA - D
AQUIFER-PROJECT WEBSITE: http://www2.gaf.de/Aquifer/
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 4
The AquiferEx Campaign
• In the frame of AQUIFER, an ESA funded airborne campaign “AquiferEx” was conducted
• performed by the German Aerospace Establishment DLR
• 2 test sites in Tunisia were mapped using 2 sensors
• Hyperspectral: AVIS (University of Munich)
• Multifrequent + multipolarimetric Radar: ESAR (DLR).
• Ground Truth was collected by the University of Munich and DLR during the flight campaign
• AquiferEx data will be used for a refined land use / cover map
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 5
Test-sites of AquiferEx
Gabès
Ben Gardane
Tripoli
Tunis
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 6
AVIS acquisition09.11.05(VNIR)
Flight Strips of Airborne CampaignSynchronous satellite acquisitions: ASAR/AP data
Gabès, 28.11.2005 + E-SAR Ben Gardane, 25.11.2005 + AVIS
E-SAR acquis.11.11.05L-Band HH,VH,VV
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 7
Test Site: Ben GardaneSynchronous Satellite acquisitions: CHRIS
Legend
Flight strip
CHRIS NorthAcquisition date:
23.10.2005
CHRIS SouthAcquisition date:
31.10.2005
CHRIS Mode3A, NadirGSD: 17 m
700, 715, 900 nm
Ground Truth points
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 8
Legend
Flight strip
CHRIS EastAcquisition date:01.11.2005
CHRIS WestAcquisition date:09.11.2005
CHRIS Mode3A, Nadir
GSD: 17 m
700, 715, 900 nm
Ground Truth points
Test Site: GabesSynchronous Satellite acquisitions: CHRIS
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 9
Autonomous atmospheric correctionof CHRIS data
• A methodology that uses MODTRAN4 radiative transfer modelling is applied for atmospheric correction
• Acquisition parameters on solar and observation geometry are known from sensor header information
• Parameterisation of aerosol optical thickness (or visibility) and atmospheric water vapour content requires atmospheric data that is often missing (as is the case for the Tunisian test-sites)
• It will be demonstrated how hyperspectral data allow the autonomous retrieval of water vapour from spectral information
• Multidirectional observations further allow the assessment of the adequate atmospheric visibility.
• Thus, a fully autonomous atmospheric correction is possible.
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 10
water vapour factor for Gabès
20
25
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35
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55
60
500 600 700 800 900 1000 1100Wavelength [nm]
Spe
ctra
l Ref
lect
ance
[%]
0.51
1.51.28
Soil spectra retrieved under the assumption of different water vapour factors: 0.5 – 1.0 – 1.5
Determination of Water VapourCHRIS Mode 3 (only 18 bands)
Pixel-wise retrieved water vapourMean = 0.71 Std.dev=0.03
=> Sensor noise dominates due to low variability of water vapour, but scene average derivable
Ben Gardane
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 11
Vegetation spectra retrieved under the assumption of different water vapour factors: 0.5 – 1.0 – 1.5
Determination of Water VapourCHRIS Mode 1 (64 bands)
water vapour factor
0
10
20
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40
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60
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90
100
700 750 800 850 900 950 1000 1050 1100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
0.510.651.5
=> Influence of land surface properties very low; vegetation water separable
Pixel-wise retrieved water vapourMean = 0.65 Std.dev=0.04
Baasdorf
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 12
The Soil-Leaf-Canopy (SLC) reflectance model simulates the BRDF of a soil using the CHRIS acquisition specifications:
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20
40
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80
400 500 600 700 800 900 1000 1100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
Forward 55°Forward 36°NadirBackward 36°Backward 55°
Concept: Varying the visibility in the atmospheric correction, the most similar CHRIS spectra are selected.
Determination of Visibility
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 13
Multiangular soil spectra retrieved under the assumption of different atmospheric visibilities
Determination of Visibility
Visibility = 5km
0
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20
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400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100
Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Forw. 55 Forw. 36 NadirBackw. 36 Backw. 55
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 14
Multiangular soil spectra retrieved under the assumption of different atmospheric visibilities
Determination of Visibility
Visibility = 10km
0
10
20
30
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80
400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100
Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Forw. 55 Forw. 36 NadirBackw. 36 Backw. 55
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 15
Multiangular soil spectra retrieved under the assumption of different atmospheric visibilities
Determination of Visibility
Visibility = 23km
0
10
20
30
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400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100
Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Forw. 55 Forw. 36 NadirBackw. 36 Backw. 55
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 16
Multiangular soil spectra retrieved under the assumption of different atmospheric visibilities
Determination of Visibility
Visibility = 40km
0
10
20
30
40
50
60
70
80
400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100
Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Forw. 55 Forw. 36 NadirBackw. 36 Backw. 55
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 17
Visibility selected with BRDF similar to model results avoiding zero reflectance in the visible;
Example Result : VisibilityBen Gardane 23.10.2005
Determination of Visibility
23 km40 km
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 18
0
5
10
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30
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40
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400 450 500 550 600 650 700 750 800 850 900Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Spectrometer (11.11.)maxminAVIS (11.11.)CHRIS ( 9.11.)
Validation of atmospheric correctionSensor comparison for Gabès
Spectrometer & AVIS compared to CHRIS; harvested field
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 19
Validation of atmospheric correctionSensor comparison for Gabès
Spectrometer & AVIS compared to CHRIS; alfalfa field
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400 450 500 550 600 650 700 750 800 850 900Wavelength [nm]
Spec
tral
Ref
lect
ance
[%]
Spectrometer (11.11.)maxminAVIS (11.11.)CHRIS (9.11.)
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 20
Parameter retrieval using SLC model inversion techniques
0
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400 500 600 700 800 900 1000 1100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
LAI=0LAI=0.5LAI=1LAI=1.5LAI=2
The Soil-Leaf-Canopy (SLC) reflectance model simulates a set of possible soil background and vegetation combinations and selects the soil and LAI where RMS deviation to CHRIS observation is minimum. Sample SLC results:
Vegetation on dry, bright soil Vegetation on wet, dark soil
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10
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400 500 600 700 800 900 1000 1100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
LAI=0LAI=0.5LAI=1LAI=1.5LAI=2
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 21
Input Images Gabès to be classified
VIS (bands 1- 4- 9) Red Edge (bands 9-11-16)
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 22
SLC model inversion resultsdry soilhigh iron soilwetsoillimestonewater
LAI0.6 – 0.80.8 – 1.21.2 – 1.41.4 – 1.6> 1.6
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 23
Separation of vegetated areas under irrigation or under dry conditions
Remark: Just one moment in time!
Irrigated
Non irrigated
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 24
Model based classification resultSoil background
Vegetationcharacteristics
Agricultural management
LAI0.6 – 0.80.8 – 1.21.2 – 1.41.4 – 1.6> 1.6
dry soilhigh iron soilwetsoil
Irrigated
Non irrigated
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4th ESA CHRIS PROBA Workshop September 19- 21th 2006 No. 25
Conclusions• Based solely on radiative transfer model techniques for the
atmosphere (MODTRAN) and the land surface (SLC), it is possible to derive
– the atmospheric properties (water vapour and visibility) needed for reflectance calibration
– Bio-geo-physical parameters of the land surface that can be translated in an advanced classification
• Multiangular CHRIS data in full spectral mode are most suitable for this task.
• The developed model based approach showed promising results, but is only a very first step.
• Planned satellite sensors like ENMAP will allow to further develop, enhance and apply the presented methodology.