VENµS: a new EO mission at High spatial resolution (5-10m) + High revisit frequency (2 days) + Constant viewing angle - PowerPoint PPT Presentation

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VENµS: a new EO mission at High spatial resolution (5-10m) + High revisit frequency (2 days) + Constant viewing angle

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  1. 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

  2. Outline • The mission • Products and pre-processing • Applications

  3. I The Mission

  4. 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

  5. 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

  6. Formosat-2 time seriesYaqui, Mexico

  7. Formosat-2 time seriesYaqui, Mexico

  8. Formosat-2 time seriesYaqui, Mexico

  9. Formosat-2 time seriesYaqui, Mexico

  10. Formosat-2 time seriesYaqui, Mexico

  11. Formosat-2 time seriesYaqui, Mexico

  12. Formosat-2 time seriesYaqui, Mexico

  13. Formosat-2 time seriesYaqui, Mexico

  14. Formosat-2 time seriesYaqui, Mexico

  15. Formosat-2 time seriesYaqui, Mexico

  16. Formosat-2 time seriesYaqui, Mexico

  17. 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

  18. II VENµS Products

  19. 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

  20. 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

  21. 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

  22. 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)‏

  23. FORMOSAT-2 time series Retrieved AOT Initialisation image

  24. FORMOSAT-2 time series Retrieved AOT

  25. FORMOSAT-2 time series Retrieved AOT

  26. FORMOSAT-2 time series Retrieved AOT

  27. FORMOSAT-2 time series Retrieved AOT

  28. FORMOSAT-2 time series Retrieved AOT

  29. FORMOSAT-2 time series Retrieved AOT

  30. FORMOSAT-2 time series Retrieved AOT

  31. FORMOSAT-2 time series Retrieved AOT

  32. FORMOSAT-2 time series Retrieved AOT

  33. 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.

  34. III Applications

  35. 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

  36. Change detection(FORMOSAT examples) Image Before Image After Burned area Klaus storm effect

  37. 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.

  38. 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

  39. 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)

  40. VENµS simulated LAI(FORMOSAT images 2006) DEMAREZ et al., 2009.

  41. ADDITIONAL SLIDES

  42. 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.

  43. 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

  44. Atmospheric correction

  45. 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

  46. 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

  47. AOT retrieval • Simulations of aerosol inversion : • Good results even with stable aerosol conditions • Convergence requires varying aerosol conditions • A small negative bias is observed

  48. 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

  49. Aerosol estimates with FORMOSAT La crau(New version)‏

  50. Date AOT 18/11/05 0.05 21/11/05 0.30 Validation of atmospheric correction • Reflectance comparison for two dates with different AOT