The document presents a framework for simulating high temporal resolution image time series from new and future satellite sensors. Physical models are used to generate realistic synthetic images over time to evaluate algorithms for classifying land cover from multi-temporal data and determine the best sensors for different applications. An example uses real images from Formosat-2 to simulate data from potential sensors like Venμs and Sentinel-2. The framework allows preparing processing of future satellite data and understanding land cover dynamics.

1. A Framework for the Simulation of High
Temporal Resolution Image Series
J. Inglada, O. Hagolle, G. Dedieu
25/07/2011
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 1 / 33

2. Outline
1 Introduction
2 Models
3 Example of application
4 Conclusions and future work
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 2 / 33

3. Introduction
Outline
1 Introduction
2 Models
3 Example of application
4 Conclusions and future work
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 3 / 33

4. Introduction
New sensors
Venus
Sentinel (1,2,3)
LDCM
New applications . . .
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 4 / 33

5. Introduction
New sensors
Venus
Sentinel (1,2,3)
LDCM
New applications . . .
... which require to closely monitor the temporal trajectory of the
characteristics of land surfaces.
real time classification
evolving nomenclatures
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 4 / 33

6. Introduction
The VENµS mission
France/Israel cooperation
11 spectral bands (VIS, NIR)
10 m. resolution
2 day revisit cycle (limited number of sites)
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 5 / 33

7. Introduction
Sentinel-2
Optical HR component of ESA’s Sentinel Programme
13 spectral bands (VIS, NIR, SWIR)
10/20/60. m resolution
Earth coverage every 5 days (with 2 sats)
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 6 / 33

8. Introduction
Challenges
From the annual classification . . .
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 7 / 33

9. Introduction
Challenges
From the annual classification . . .
... to the dynamic classification
Inter-crop Stubble disking Deep ploughing
Harrowing Sowing Emergence
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 7 / 33

10. Introduction
Focus
Applications
Global coverage every few days
Expectations for land cover change monitoring
Real-time: update the land-cover maps for every new acquisition
Methods
Describe temporal evolutions
Choose and combine different data sources
Integration of prior knowledge
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 8 / 33

11. Introduction
Objectives
Develop algorithms for high temporal and high spatial resolution image
time series
Evaluate and compare:
algorithms
the sensors
Need for realistic data which are representative of sensors which do
not exist
Use physical models as simulation tools
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 9 / 33

12. Models
Outline
1 Introduction
2 Models
3 Example of application
4 Conclusions and future work
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 10 / 33

13. Models
Essential Climate Variables
For climate change assessment, mitigation and adaptation:
River discharge,
Water use,
Groundwater,
Lakes,
Snow cover,
Glaciers and ice caps,
Permafrost,
Albedo,
Land cover (including vegetation type),
Fraction of absorbed photosynthetically active radiation (FAPAR),
Leaf area index (LAI),
Above-ground biomass,
Fire disturbance
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 11 / 33

14. Models
Models - Scope
They describe the physical reality
Their assumptions/simplifications are clear
Naturally use/need ancillary data (meteo, ground measures)
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 12 / 33

15. Models
Models - Scope
They describe the physical reality
Their assumptions/simplifications are clear
Naturally use/need ancillary data (meteo, ground measures)
They can be multi-sensor or better . . .
. . . Sensor Agnostic
benefit from the synergy between sensors
increase temporal sampling!
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 12 / 33

16. Models
Models - Challenges
Areas of interest:
hydrology, agriculture, forestry,
Media:
Aerial, terrestrial, aquatic, mixed
How to find the good balance
complexity,
number of input parameters and variables,
computational cost
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 13 / 33

17. Models
Open source models - some
examples
Prospect: optical model for estimating leaf-level reflectance and
transmittance
Sail: canopy reflectance model
Daisy: mechanistic simulation model of the physical and biological
processes in an agricultural field
6s: a basic RT code used for calculation of look-up tables in the
MODIS atmospheric correction algorithm
Arts: radiative transfer model for the millimeter and sub-millimeter
spectral range.
etc.
have a look at ecobas.org
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 14 / 33

18. Example of application
Outline
1 Introduction
2 Models
3 Example of application
4 Conclusions and future work
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 15 / 33

19. Example of application
Purpose
Which is the best sensor to recognize these:
sol nu sec
60 végétation
eau
Réflectance (%)
40
20
0
0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 2,6
Longueur d'onde (µm)
visible proche infrarouge moyen infrarouge
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 16 / 33

20. Example of application
Purpose
Or these
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 17 / 33

21. Example of application
Principle
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 18 / 33

22. Example of application
Results
Spot 5
1 Quickbird
Pleiades
0.8 Landsat TM
Accuracy
Ikonos
0.6 Formosat
Meris
0.4
0.2
0
Ve
So
M
M
an
in
ge
ils
e
-m
ra
ta
ls
ad
tio
n
e
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 19 / 33

23. Example of application
Results
Spot 5
1 Quickbird
Pleiades
0.8
Accuracy
Landsat TM
0.6 Ikonos
Formosat
0.4 Meris
0.2
0
R
C
C
R
Ig
M
Se
Al
Ar
En
In
M
oa
on
on
oo
ne
et
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fis
id
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J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 20 / 33

24. Example of application
But we said HTR . . .
How to simulate a multi-t mission?
Venus, Sentinel-2
Realistic temporal evolutions
Use existing image time series
Formosat-2
8 m., 4 bands (B,V,R,NIR), 3 days
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 21 / 33

25. Example of application
Example of series
March 14, 2006
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26. Example of application
Example of series
July 17, 2006
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 23 / 33

27. Example of application
Example of series
November 2, 2006
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 24 / 33

28. Example of application
Available data
49 images in 2006
Orthorectification OK
Radiometric corrections OK
TOC and aerosol corrections
Cloud screening
Land-cover map available
Leaf pigments data base for several vegetation types (LOPEX’93)
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 25 / 33

29. Example of application
Spectral responses
Relative Spectral Responses
1.0
0.8
Formosat-2
0.6
0.4
0.2
0.0 500 1000 1500 2000
wavelength
1.0
0.8
Venus
0.6
0.4
0.2
0.0 500 1000 1500 2000
wavelength
1.0
0.8
Sentinel-2
0.6
0.4
0.2
0.0 500 1000 1500 2000
wavelength
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 26 / 33

30. Example of application
Simulator architecture
LAI (t)
Formosat-2 RSR
Formosat-2 Input Series
PROSPECT+SAIL Full Spectra Venµs RSR
Car Sentinel-2 RSR
Cab
N
Land Cover Map
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 27 / 33

31. Example of application
Example of application
1.0 0.80
FSAT-2
0.75 Venus
0.8 Sentinel-2
0.70
0.6 0.65
Kappa Index
0.4 0.60
0.55
0.2 Cloud %
40 dates 0.50
30 dates
0.0 0.45
6 6 6 6 6 6 6 6 6
200 200 200 n 200 Jul 200 ug 200 ep 200 ct 200 ov 200
Mar Apr May Ju A S O N 0.400 10 20 30 40 50
Number of dates
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 28 / 33

32. Conclusions and future work
Outline
1 Introduction
2 Models
3 Example of application
4 Conclusions and future work
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 29 / 33

33. Conclusions and future work
As a conclusion
New missions in the coming years
Venus, Sentinel, LDCM
Nowadays: Formosat-2
How to prepare the use of future systems
Algorithm design and validation
Understanding phenomena
Use of simulation
Completely synthetic
From very high resolution(s) data
The third way!
Use real time series
but with lower resolutions
Use physical models
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 30 / 33

34. Conclusions and future work
What we’ve got
Source code available for many simulators
Ongoing work for
Prospect, Sail & Daisy integration
new hyper/multi- spectral/temporal algorithm integration
http://www.orfeo-toolbox.org
J. Inglada, O. Hagolle, G. Dedieu IGARSS’11, Vancouver 25/07/2011 31 / 33

35. Conclusions and future work
What we need
Engineering - Development
Improve image simulation: MTF, realistic landscapes
Hide physical models under common interfaces
Research
Learn to select the best model set for a given problem
Incorporate domain expert knowledge
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36. Conclusions and future work
Creative Commons Attribution-ShareAlike 3.0 Unported License
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