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Development of biophysical products for PROBA-V Interest of 300m resolution

Development of biophysical products for PROBA-V Interest of 300m resolution. F. Baret, M. Weiss & L. Suarez. Interest of 300m resolution for Agriculture. Patch/ object identification. Impact of the spatial resolution. Variability within pixels:

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Development of biophysical products for PROBA-V Interest of 300m resolution

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  1. Development of biophysical products for PROBA-VInterest of 300m resolution F. Baret, M. Weiss & L. Suarez

  2. Interest of 300m resolution for Agriculture

  3. Patch/object identification

  4. Impact of the spatial resolution Variabilitywithin pixels: Normalized Standard Deviation (NSRDV) Number of classes per pixel Number of patches per pixel Purity (patches/classes)

  5. Conclusion (obvious) • High spatial resolutionismandatory for agriculture applications (field/specieslevel) • Large differencesbetween 1km and 300m resolution • But large differencealsobetween 300m and 100m! • Impact on: • classification • disaggregation • LAI estimates (non linearity)

  6. Development of LAI, FAPAR, FCOVER near real time productsfrom PROVA-V Objective: Developbiophysicalproducts for PROBA-V • LAI, FAPAR, FCOVER • 10 daysfrequency • Near real time • Consistent with VEGETATION products (GEOV1/2) • 300m resolution Impact of higher spatial resolution • No climatologyavailable • Climatology/pixel meaningless • No use of SWIR

  7. General principles

  8. Step A: Instantaneous estimates

  9. Learning from CYCLOPES and MODIS

  10. Instantaneousestimates

  11. Step B: Compositing 25 parameters to control step B

  12. Near real time

  13. Compositing: eliminatingoutliers

  14. Compositing: EBF case

  15. Compositing: no_EBF case

  16. Compositing: Fillingsmall gaps GEOV3 GEOV1 MODIS

  17. Conclusion • Associatedqualityindicators • Flag • RMSE • Confidence intervalfrom polynomial fit • Consistencybetween variables • Sameselection of observations used (mostlybased on LAI) • Importance of the compositingstep • Withoutclimatology • For short term projection • Needs to bedone: • Implementation of the algorithm • Verification/Validation • Checkingconsistencywith GEOV2 (aggregationat 1km) • Updating the algorithmwhenenoughactual PROBA-V data willbeavailable • Building a longer time series: process back the MERIS archive? • Combinationwith S2 -> 20m observations everyday?

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