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Remote Sensing data product Contribution from data providers/algorithm development team.

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Presentation on theme: "Remote Sensing data product Contribution from data providers/algorithm development team."— Presentation transcript:

1 Remote Sensing data product Contribution from data providers/algorithm development team

2 Data from many moderate resolution remote sensing sensor, mainly vegetation indices at a compositing period We broadly follow three steps to derive phenological matrices Data filtering Temporal smoothing (many methods) Derived matrices ( many method and many matrices) JÖNSSON and EKLUNDH, 2004

3 MODIS NACP Phenology Products Retrieved Phenology Metrics 1.Beginning of season 2.End of season 3.Length of season 4.Base VI value 5.Peak time 6.Peak value 7.Amplitude 8.Left derivative 9.Right derivative 10.Integral over season - absolute 11.Integral over season - scaled 12.Maximum value 13.Minimum value 14.Mean value 15.RMSE of fitting

4 MODIS NACP Phenology Products Availability and Status Availability: From Products: phenology metrics derived from LAI/EVI/NDVI, and original, smooth/gap-filled LAI, FPAR, EVI & NDVI. Temporal Coverage: From 2001 to Spatial Coverage: Full North America, partially South America. Asia is under processing. Online data services: Subset by geographic area Subset by data layer Reproject Mosaic Aggregation Re-format (to GeoTIFF).

5 MCD12Q2 C5 Product Global database –Annual since 2001, 500-m Includes 7 metrics –Onset of EVI increase –Onset of EVI maximum –Onset of EVI decrease –Onset of EVI mimimum –Min EVI –Max EVI –Sum of growing season EVI Validation: –Opportunistic, largely in New England –Current focus on PhenoCam Data Timing Annual Metrics Mark Friedl

6 USGS EROS Vegetation Dynamics Availability: From Products: Nine annual remote sensing phenological indicators (served as raster data sets) are available at two spatial resolutions (1000 m2 and 250 m2) based on NDVI Temporal Coverage: AVHRR ( ) MODIS ( ) Spatial Coverage: conterminous U.S. Method : Delayed Moving Average (DMA) method (Reed et al., 1994). Considerable QA checking done on USGS phenological data Jesslyn Brown

7 Phenological metrics available at multiple resolutions Jesslyn Brown

8 Phenological metrics available at multiple resolutions Jesslyn Brown

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10 The VGT4Africa phenology product Algorithm developed by the Joint Research Centre (European Commission) Product generated by VITO (Belgium) Based on the processing of a moving time-window of 1.5 year of NDVI from the VEGETATION instrument Updated within 3 days after every 10-day period (dekad) Covers the whole African continent Provides dekad dates for start of growth, max NDVI and half-senescence Availability: from VITO through ftp and EUMETCast, jan 2007 until present Product description: Combal B. & Bartholomé E. 2006: Phenology. In: Bartholomé edit: VGT4Africa user manual 1st edition, European Commission ref EUR EN: Method: Combal B. & Bartholomé E. 2010: Retrieving phenological stages from low resolution Earth observation data. In: Maselli & al.: Remote Sensing Optical Observations of Vegetation Properties, Research Signpost, Kerala, India, Bartholomé

11 Start dates as observed on 3rd dekad of Dec 2011 (note: actual time resolution of the product is the dekad, not the month)

12 VIP Data Explorer:30 Years of Multi-Sensor VI and Phenology Data Availability: From vip.arizona.edu/viplab_data_explorer.php Products: Vegetation index and phenology from AVHRR, VEGETATION, MODIS (Sensor independent) Temporal Coverage: 30+ Spatial Coverage: Global Spatial resolution : 0.05 deg Considerable data quality assessment Kamel Didan

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14 PHAVEOS – the Phenology And Vegetation EO Service A service to provide: Vegetation maps of several biophysical variables relevant to models of bio-geochemical cycles Leaf Area Index (LAI) fraction of Absorbed Photosynthetically Active Radiation (fAPAR) MERIS Terrestrial Chlorophyll Index (MTCI) fraction of green land cover (fCover) Continuous time series to support phenology studies and monitoring Visualisation of individual maps and phenology curves for individual locations Thomas Lankester

15 MERIS / MODIS Sentinel 3 Sentinel 2 (LDCM) Biophysical processing and mapping Data sources HiProGen and Overland Daily Level 3 and Level 4 data dissemination WebServer Web client on user PC

16 Level 3 daily product examples fCover LAIfAPAR

17 ftp://l3-server.infoterra.co.uk/pub/SNL/MTCI_L4_ _comparison.gif Spring 2009 – 2010 comparison

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19 Phenology Land Product Validation Workshop Core Site Selection Original Sites (2010 Dublin Workshop): Do we keep the original sites? Are more sites needed? What are the essential variables and is it necessary for every site to offer the same set of core variables/instruments? Site NameCountryCover TypeLatLonMETFLUXPheno Camera Radiometer PARPheno Observations Torgnon – TellinodItalyGrassland XXXXX Torgnon – TronchaneyItalyLarch Forest XXXXX Park FallsUSADeciduous Broadleaf XXX HyytialaFinlandBoreal Conifer XXXXX HarvardUSAMixed Forest XXXX BartlettUSAMixed Forest XXX HowlandUSABoreal Hardwood Trans XXX TakayamaJapanDeciduous Broadleaf XXXX TakayamaJapanEvergreen Coniferous XXXX BarraxSpainCropland Hubbard BrookUSADeciduous Hardwood43.93XXXX Vaira RanchUSAGrassland XXXX …other suggestions? particularly Asian or Southern Hemisphere locations.

20 Phenology Land Product Validation Workshop Panel Discussion Working across scales: Are site specific nested datasets (in-situ, phenocam, RS) and validation results applicable to validation of continental/global RS phenology products? Do PhenoCams need to be validated with in-situ observations? What standards need to be set for Phenology LPV: Are standardized definitions needed for metrics? – Start of Season, End of Season Are standardized methods needed to calculate metrics? – Curve fitting, Derivative peaks, etc. What do we mean by Phenology Validation? Is it setting a realistic offset/error range between phenocam or in-situ and RS metrics? Is this application specific? What are best practices for LPV using in-situ data?

21 Phenology Land Product Validation Workshop Pilot Project Definition Core Sites Selection and Considerations: Do we agree upon the site selections? Is all data freely available? Creation of formal data sharing agreement. Data Collections/Bundles: RS products – size of subset over each site, 100km? Centralized Storage and Access Ground/In Situ Site Data – centralized storage? Project Objectives: Do we allow for a flexible structure and let researchers dictate site by site analysis OR do all projects follow a set protocol? Timeline – What is a realistic expectation? The LPV 5yr Plan states Validation Protocol established by Responsible Parties: Data Collections/Bundles – must be available by…? Who will conduct the research? PhD Students, Post-Docs, Staff Scientists.

22 Phenology Land Product Validation Workshop Workshop Review Did we meet our objectives? Provide a synopisis of the majority available data sets. Review and discuss validation methods, current limitations and concerns. Selection of Core Sites. Agreement on data subsets, storage and access. Define Pilot Projects. Set a course for future Land Surface Phenology Validation For the future: Do responsible parties understand their tasks (providing data, analysis, etc.) Write up of a Meeting Summary Publication – EOS. Summary Poster for AGU – Jadu and Matt with input from committee. Informal Meeting at AGU 2012 to discuss progress.


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