1. U.S. Department of the Interior U.S. Geological Survey Charge to workshop: Develop an international protocol to quantify the accuracy of remote sensing phenology products and initiate a validation- based inter-comparison

2. The Leaf Area Index example…

3. Inter-comparison General Timeline LAI Phenology… Topical meeting to establish data requirements Decide on Sites Develop data sharing infrastructure Field Campaigns & individual product analysis Synthesis of results Boston, USA Privette et al. 1998 Frascati, Italy Privette et al. 2001 Montana, USA Morisette et al. 2004 Post-doc: Garrigues et al. 2008 Dublin workshop, 2010 (we’ll need a published, citable, report)

4. Reference for previous slide  Garrigues, S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R, Plummer S., Shabanov NV, Myneni RB, Knyazikhin Y, and Yang W, 2008. Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, doi:10.1029/2007JG000635.  Morisette, J.T. JL. Privette, Jaime Nickeson, Frèdéric Baret, Ranga B. Myneni, and Nikolay Shabanov, Summary of the Third International Workshop on LAI Product Validation, Earth Observer, Sept./Oct. 2004, v.16, n.5, p.28-31 (available on-line at http://eospso.gsfc.nasa.gov/eos_observ/pdf/Sept-Oct04.pdf).  Privette, J., J. Morisette, R. Myneni, C. Justice, 1998, Global Validation of EOS LAI and FPAR Products, The Earth Observer, Nov/Dec. v.10, n.6, p.39-42.  Privette, J.L., J. Morisette, F. Baret, S. T. Gower, and R. B. Myneni, 2001, Summary of the International Workshop on LAI Product Validation, Earth Observer, July/Aug, v. 13, n. 3, p. 18, 22.

5. LAI inter-comparison example: Garrigues, S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R, Plummer S., Shabanov NV, Myneni RB, Knyazikhin Y, and Yang W, (2008).Validation and Intercomparison of Global Leaf Area Index Products Derived From Remote Sensing Data, JGR., v. 113.

6. The LAI example Inter- comparison sites Direct Validation sites

7. Inter-comparison: The BELMANIP Global Network of Sites representative sampling of global land surface types about 400 sites from several networks: direct validation sites (D: BIGFOOT, VALERI… ), AERONET (A), FLUXNET (F)… Baret, F., J. Morisette, et al., 2006, Evaluation of the representativeness of networks of sites for the validation and inter-comparison of global land biophysical products. Proposition of the CEOS- BELMANIP, IEEE TGARS, 44(7)1794-1803.

8. Direct validation: Product versus 81 LAI reference Maps Morisette, J.T., F. Baret, J. L. Privette, R. B. Myneni, J. Nickeson, et al., 2006. Validation of Global moderate resolution LAI Products: a framework proposed within the CEOS Land Product Validation subgroup, IEEE TGARS, 44(7)1804-1817.

9. Overview of meeting  Remote sensing phenology products  Potential reference data  Discussion

10. Anticipated outcome 1. Citable article to report on this meeting 2. Action items and writing assignments for a phenology validation protocol 3. Network and strategy for future proposals 4. More fun at Friday night social!

11. General consideration: We can validate a specific metric, such as start of season (a la White et al) or We could consider the more general issue of time series analysis (mainly summaries) from satellite-based vegetation monitoring.

12. Can we agree on a criteria?  Robustness  Tractability  Transparency  Sophistication  Extendibility  Dimensionality (Keyantash and Dracup, 2002 BAMS Aug. p. 1167-1180)  Resolution (spatial, temporal)  Duration

13. Can we list and prioritize uses - and list required accuracy and appropriate reference data for each?  Habitat modelling (Bourke, Wilkovich)  Animal migration (Beck, Karlsen)  Vegetation component of match/mis-match hypothesis (Thackeray)  Pollen monitoring (Karlsen)  Tourism or recreation, hunting (Richardson, Schaaf)  Public awareness (“eye candy” with citizen science activities, Beaubien)  Input to coupled land-ocean-atmospheric climate models  Input to carbon modelling  Monitoring & forecasts of agriculture  Fire risk mapping  Insect or other bio-disturbance