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1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA, France 3. Exploitation & Services Division, Industry Section, ESA-ESRIN PHAVEOS - the PHenology And Vegetation EO Service. Presented by: Thomas Lankester 18th June 2010 Lankester, T., Dash, J. 1 , Baret, F. 2 , Koetz, B. 3 & Hubbard, S.

1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Page 1: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

1. School of Geography, University of Southampton, UK2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA, France3. Exploitation & Services Division, Industry Section, ESA-ESRIN

PHAVEOS - the PHenology And Vegetation EO Service.

Presented by: Thomas Lankester

18th June 2010

Lankester, T., Dash, J.1, Baret, F.2, Koetz, B.3 & Hubbard, S.

Page 2: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Objectives

Provide time series of a range of vegetation parameters, utilising the unique spectral, spatial and temporal resolution of the MERIS instrument

Make spatially and temporally continuous time series available through visualisation and download of maps and phenology curves for individual locations

Support the development of a validated baseline time series (2005 - ) in advance of the launch of Sentinels 2 and 3

Page 3: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 1b to Level 3 processing approach

Convert MERIS Level 1b data to Level 3 gridded maps, on a daily basis

geometric correction

radiometric correction

atmospheric correction

derive biophysical variable(s)

resample direct to target map grid (latlong, OSGB36, Irish Grid…)

Page 4: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 1b geometric accuracy issues

Page 5: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 1b geometric accuracy issues

Page 6: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 3 geometric accuracy

To preserve geometric fidelity, resampling into the target map grid is carried out in a single step

To conserve the scene statistics area weighted (flux-conserving) resampling is used

The blue grid represents the input (swath) data grid and the yellow grid the target map grid

Page 7: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Step 1 apply a cubic spline interpolation of the raw data to generate a continuous time series

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Level 4 processing - interpolation

Page 8: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 4 processing - smoothing

Step 2 smooth using a local weighted least squares regression (if no negative noise bias)

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Page 9: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 4 processing – interpolation metrics

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Page 10: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 4 processing – smoothing metric

Page 11: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 3 validation

Moving from Stage 1 to Stage 2(+) validation requires considerable product generation

PHAVEOS is utilising the ESA Grid Processing On-Demand (G-POD) environmentBased on MERIS FRS data from 2005 – present, will deliver a range of Level 3 and Level 4 time seriesLAI, fAPAR, fCover, MTCI, NDVI, 2G_RBi, ….

Provision of Level 3 products for MODIS match up sites (N. America)

Coverage of PAR@METER sites

OnLine Interactive Validation Exercise (OLIVE)

Page 12: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Level 4 validation

Land Surface Phenology product validation methods TBD.

issues of spatial disparity where Sentinel 2 could bridge the gap.

Access to Forestry Commission intense monitoring sites (leaf litter collections, phenocams)

Access to UK Phenology Network

Access to tropical (DRC) deforestation ground truth

Page 13: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Web Map Service dissemination concept

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Page 14: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Phenology metrics – what is the point?

Why use, and validate / inter-compare, basic phenology statistics?

Loss of information from a continuous time series (are we hiding intra-annular information)

Why inter-compare on a handful of measures when full time series are available?

Extraction of metrics is sensitive to interaction of smoothing and metric extraction methods

Different users are interested in different aspects of time series (phenology curves)

Are simple metrics capturing a relevant reality?

Page 15: 1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,

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Any questionsPhenological beauty is in the eye of the

beholder