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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,

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Presentation on theme: "1. School of Geography, University of Southampton, UK 2. Unité Mixte de Recherche Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, INRA,"— Presentation transcript:

1 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.

2 2 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

3 3 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…)

4 4 Level 1b geometric accuracy issues

5 5

6 6 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

7 7 Step 1apply a cubic spline interpolation of the raw data to generate a continuous time series Level 4 processing - interpolation

8 8 Level 4 processing - smoothing Step 2smooth using a local weighted least squares regression (if no negative noise bias)

9 9 Level 4 processing – interpolation metrics 3

10 10 Level 4 processing – smoothing metric

11 11 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) environment Based on MERIS FRS data from 2005 – present, will deliver a range of Level 3 and Level 4 time series LAI, 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)

12 12 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

13 13 Web Map Service dissemination concept

14 14 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?

15 15 Any questions Phenological beauty is in the eye of the beholder


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