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VENµS: a new EO mission at High spatial resolution (5-10m) + High revisit frequency (2 days) + Constant viewing angle G.Dedieu 1 (CNES PI), O.Hagolle 1 , S. Garrigues 1 , et al (VENµS project team) 1: CNES, Toulouse, France. Outline. The mission Products and pre-processing

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VENµS: a new EO mission at

High spatial resolution (5-10m)

+

High revisit frequency (2 days)

+

Constant viewing angle

G.Dedieu1 (CNES PI), O.Hagolle1, S. Garrigues1, et al (VENµS project team)

1: CNES, Toulouse, France


Outline
Outline

  • The mission

  • Products and pre-processing

  • Applications



Ven s mission

Scientific mission (720 km)

2.5 years

2011

Orbit change

3 months

Technological mission (410 km) – 1 year

End of the commissioning phase

Launch

electrical propulsion system

Venµs Mission

Mission in cooperation between France and Israel:

  • Scientific demonstrator:

    • Super-spectral

    • High spatial resolution

    • Multi-temporal observations (every 2 day)

    • Constant viewing angles

  • Technological mission: Test of an electrical propulsion system

~250 kg


Mission specifications
Mission specifications

  • Venµs image characteristics

    • Resolution: 5m-10m

    • Field of View: 27 km

    • 12 spectral bands from 412 to 910 nm

    • Geometric revisit frequency: 2 days

    • Systematic acquisition: 50 sites

    • 2 stereoscopic bands with a low angle difference

    • Constant viewing angle ==> Directional effects are minimised

  • Current similar commercial satellite: Formosat-2 (NSPO, Taiwan)

    • Launched in 2004

    • Resolution 8m, Field of View 24 km

    • 1 day repeat cycle

    • 4 Spectral bands : 488, 555, 650, 830 nm

    • Constant viewing angle













Impact of constant view angle
Impact of constant view angle

Wheat field –Yaqui

VENµS: constant view angle

=> Smooth time series

Wheat field –Romania

SPOT: non constant view angle

=> Noisy time series



Ven s products
VENµS Products

Cloud detection

Atmospheric correct.

Temporal

compositing

  • Level 1 (L1) :

  • single acquisition

  • georeferenced

  • calibrated TOA reflectance

  • Level 2 (L2):

  • single acquisition

  • georeferenced

  • calibrated TOC reflectance

Level 3 (L3): temporal synthesis


Multi-temporal processing algorithms

  • Multi-temporal (recurrent) algorithms for

    • Water detection

    • Cloud/shadow detection

    • Aerosol estimation

  • L2 composite product of date D-1 used as input in the algorithm


Cloud detection

Venµs algorithm characteristics:

  • Use of stereoscopy (620 nm spectral bands)

  • Detection of surface reflectance variations in the blue

  • Detection of multi-temporal decorrelation


Atmospheric correction:

Multi-temporal AOT retrieval algorithm

TOA refl

Day D

TOA refl

Day D+2

Atmospheric corrections

(6S; SOS)

AOT(D+2)

AOT(D)

A priori

Surf. refl

Day D-t

Surf. refl

Day D

Surf. refl

Day D+2

Assumptions:

  • No directional effects

  • Surface reflectances vary :

    • Quickly with distance

    • Slowly with time

  • Aerosol optical properties vary :

    • Quickly with time

    • Slowly with distance (few km)‏

Search AOT(D) and AOT(D+2)

minimizing differences between the 3 surface reflectances

(a priori, D, D+2)‏


FORMOSAT-2 time series

Retrieved AOT

Initialisation image


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


FORMOSAT-2 time series

Retrieved AOT


Validation
Validation

Surf. Ref. Validation

AOT Validation

NIR

Estimated AOT

Surface reflectance

RED

BLUE

Measured (AERONET) AOT

Hagolle, Dedieu et al., “Correction of aerosol effects on multi-temporal images acquired with constant viewing angles:

Application to Formosat-2 images,” Remote Sensing of Environment, vol. 112, Apr. 2008, pp. 1689-1701.



Dynamic land cover monitoring
Dynamic land cover monitoring

HR (~10m) + Multi-temporal imagery + No directional effect

Classif.

(SVM)

FORMOSAT time series

MAIZE

90% of maize pixel detected

=>predicting crop water demand

Classification accuracy

Ducrot et col. 2008


Change detection formosat examples
Change detection(FORMOSAT examples)

Image Before

Image After

Burned area

Klaus storm effect


Crop water demand monitoring1 Method

Multitemp.

Classif

Land cover

FORMOSAT

time series

Biomass

RT

inversion

Crop Model

(SAFYE)

Leaf Area Index

Evapotrans.

Change

detection

Sowing dates

B. Duchemin et al., "A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index," Environmental Modelling and Software, vol. 23, 2008, pp. 876-892.


Crop water demand monitoring2 Results

Simulated irrigation

Measured irrigation

Measured irrigation

Over-estimation due to

not declared groundwater pumping

Simulated irrigation

(model driven by FORMOSAT-2 data)

Sink

Overestimation

Declared irrigation


Conclusions
Conclusions

  • high resolution (~10m) + high revisit frequency (2 d) + constant viewing angle

  • Venµs, new EO mission concept:

  • Benefit for accurate surface reflectance time series :

    • Cloud discrimination

    • Aerosol optical thickness estimation

  • Benefit for EO applications

    • Vegetation/crop monitoring

    • Water resources monitoring

    • Surface change detection

  • Potential for the validation of global veg. product

  • ~12 FORMOSAT-2 time series available for scientific use

  • Preparing future operational EO mission (SENTINEL-2/ESA)


Ven s simulated lai formosat images 2006
VENµS simulated LAI(FORMOSAT images 2006)

DEMAREZ et al., 2009.



Evapotranspiration et rendement maroc cesbio
Evapotranspiration et rendementMaroc – CESBIO

  • 50 images Formosat-2 (11-2005 => 11-2006)‏

  • Méthode :

    • 1. Carte d'occupation des sols (Blé, jachères)‏

    • 2. Estimation du LAI (Leaf Area Index) sur les données Formosat-2

      • Formule empirique à partir fu NDVI, calée sur données in-situ

    • 3. Détection des interventions agricoles

      • Labour => date de semis

    • 4. Modélisation avec SAFY (modèle simplifié développé au CESBIO)‏

      • Calage du modèle par le LAI et la date de semis

      • Calcul de rendement du blé en t/ha

      • Calcul de l'évapotranspiration et de la demande en eau

      • Calcul des apports d'eau

  • [1] B. Duchemin et al., "A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index," Environmental Modelling and Software, vol. 23, 2008, pp. 876-892.


Ven s products1
VENµS Products

  • Niveau 1 :

    • Mono acquisition, pas d'hypothèse sur le paysage observé

    • Données étalonnées (Réflectances TOA) et en projection géographique. Les données

  • Niveau 2 :

    • Mono acquisition, hypothèses sur le paysage observées possibles

    • 2a : réflectances de surface (après détection des nuages et corrections atmosphériques)

      • Ce produit devrait être le produit de base pour les utilisateurs de Venµs

    • 2b : variables biophysiques, peu d'efforts dans le cadre de Venµs

  • Niveau 3 :

    • Synthèse temporelle pour une courte période



Atmospheric correction1
Atmospheric correction

  • Accurate models for radiative transfer exist

    • Reference codes :

      • 6S for gaseous transmission ==> SMAC

      • SOS (LOA-CNES) for scattering ==> Look-up tables

  • Main difficulties

    • Water vapour

      • Low impact

      • Presence of 910 nm spectral band

      • Use of POLDER algorithm, LOA agrees to compute coefficients

    • Aerosols

      • Main source of errors

    • Adjacency effect

      • Necessary at Venµs resolution

      • A simple correction was tested (works well)‏

    • Slope illumination correction

      • A simple correction works well


Atmospheric correction algorithm
Atmospheric correction algorithm

  • Reference codes :

    • 6S for gaseous transmission ==> SMAC

    • SOS (LOA-CNES) for scattering ==> Look-up tables

  • Multi-temporal algorithm for Aerosol (AOT) estimation:

  • Accounts for Adjacency effect (atmospheric PSF)

    • Necessary at Venµs resolution

    • A simple correction was tested (works well)‏

  • Slope illumination correction


AOT retrieval

  • Simulations of aerosol inversion :

    • Good results even with stable aerosol conditions

    • Convergence requires varying aerosol conditions

    • A small negative bias is observed


Atmospheric correction

  • Algorithmic details

    • Inversion is done at 100m resolution

    • Inversions performed for a neighbourhood of ~50 pixels

    • The aerosol model is constant for one site

      • Only the AOT is inverted

    • If reflectances in the NIR change too much, pixels are discarded

      • A minimum number of 25 pixels is necessary

    • If the standard deviation of reflectances is too small

      • Not enough information

      • The neighbourhood is extended

      • More details provided in Mireille's Presentation


Aerosol estimates with formosat la crau new version
Aerosol estimates with FORMOSAT La crau(New version)‏


Validation of atmospheric correction

Date

AOT

18/11/05

0.05

21/11/05

0.30

Validation of atmospheric correction

  • Reflectance comparison for two dates with different AOT


Atmospheric correction2
Atmospheric correction

  • Validation at La Crau Calibration site (ROSAS)‏

Dots : La crau station

Triangles : Formosat 2

Formosat 2 was very

useful to correct bugs

on ROSAS software



Ven s formosat 2
Venµs – Formosat 2

  • Venµs image characteristics

    • Resolution 5m-10m, Field of View 27 km

    • 2 day repeat cycle

    • 12 Spectral bands from 412 to 910 nm

    • Systematic acquisition of 50 sites every second day

    • Constant viewing angle

  • Formosat-2 images : NSPO (Taiwan) satellite

    • Resolution 8m, Field of View 24 km

    • 1 day repeat cycle

    • 4 Spectral bands : 488, 555, 650, 830 nm

    • Constant viewing angle

    • 1000 € / image

    • Launched in 2004


Data availability
Data availability

  • More than 10 Formosat2 time series are available

  • Data are available for free for future Venµs Users

    • LPV is already member the Venµs User list (S. Garrigues proposal)‏

  • Interested ?


Accessibility
Accessibility

Altitude: 720 km

Inclination: 98.27°

Sun-synchronous

Local Time : 10h30


Spectral bands
Spectral bands

Simulated vegetation reflectances for 3 different Chlorophyl content


Quelques exemples d imageurs haute r p titivit haute r solution angle de vis e constant
Quelques exemples d'imageursHaute répétitivité, Haute résolution, Angle de visée constant



Morocco
Morocco

  • CESBIO/IRD site

11/2005 à 11/2006 :

  • Sunphotometer

  • One gap in spring


Sudouest
SudOuest

  • CESBIO site Near Toulouse

03/2005 à 12/2007 :

  • Sunphotometer

  • Many data gaps


La crau
La Crau

02/2006 à 10/2006 :

  • Sunphotometer

  • Many data gaps

  • VZA 41°

  • Calibration site


Cestas france
Cestas, France

  • From 24/05/2005 to 21/07/2005 :

    • 21 images

    • No sunphotometer


Montr al canada
Montréal, Canada

Period :

  • From 05/06/2005 to 03/07/2005 :

    • 9 dates

    • No sunphotometer

    • Sunglint conditions


Bardonecchia italie
Bardonecchia, Italie

  • Period :

    • From 04/09/2005 to 03/11/2005 :

      • 11 dates peu nuageuses

      • Snow

      • No Sunphotometer


Agoufou mali
Agoufou, Mali

  • Period :

    • From 06/2007 to 10/2007 :

      • One image /4 days

      • Sunphotometer

        • Half of the time

      • VZA : 51°

      • AOT up to 1.


Dr me france
Drôme, France

  • Period :

    • From 06/2007 to 08/2007 :

      • One image /week

      • Sunphotometer


Yaqui mexico
Yaqui , Mexico

  • Period :

    • From 11/2007 to 06/2008 :

      • One image /5 days

      • Sunphotometer

      • AOT up to 0.1 !


Svalbard norway
Svalbard , Norway

  • Period :

    • From 03/2007 to 09/2007 :

      • Few images

      • Some snow

      • No Sunphotometer


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