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PIs: Noormets, Chen, Schwartz

Developing combined phenological indices for reducing uncertainty in the magnitude and interannual variation of carbon, water and energy fluxes in mid-latitude forests. PIs: Noormets, Chen, Schwartz Collaborators: DeForest (SR), Reed (RS), Flux & Phenology data sharing (7 flux sites).

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PIs: Noormets, Chen, Schwartz

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  1. Developing combined phenological indices for reducing uncertainty in the magnitude and interannual variation of carbon, water and energy fluxes in mid-latitude forests PIs: Noormets, Chen, Schwartz Collaborators: DeForest (SR), Reed (RS), Flux & Phenology data sharing (7 flux sites)

  2. Overview • Problem • What can be done to solve it • Expected obstacles • Specific tasks • Significance

  3. Problem (1) Sources of uncertainty • Area • Disturbance • Land use change • Sink strength • Regulation • GSL When does GS start? for GPP - for ER - for SR

  4. Problem (2) • MODIS phenology products not validated • Compositing over 8 and 16 days

  5. How to define SOS? Seasonal midpoint NDVI Maximum increase Delayed moving average Inflection point Reed et al., 2003, Phenology

  6. When does GS start?

  7. What causes the transition? Suni et al., 2003, JGR

  8. We propose: • Detect phenological transition in fluxes • GPP • ER • SR • Seek concomitant signals in • VI-s (NDVI, EVI) • surface water (LSWI) • snow (NDSI, MOD10A1) AR = ER - SR

  9. Special requirements • High temporal resolution • historical data from flux sites with phenological observations  couple with MODIS data • High spatial resolution • Chequamegon, existing flux towers. Up to 10 days difference in transition of b.

  10. Obstacles • Cover-type specificity • re-classify MOD12 at local scale • validate against Landsat land cover & age maps • Daily frequency • 50-55% sunny • transitions occur during warm & clear periods • Spatial continuity • express fluxes in terms of SI-s • SI-s linear between 275 and 282 K (annual Tmean)

  11. Tasks (1) – collect existing data • Flux, MODIS & phenology data for different flux sites • MODIS products that are available at daily time step: MOD09refl, MOD10snow, MOD11tº

  12. Tasks (2) – re-processing • Optimize MOD12 for detecting composition and age differences identified from Landsat • Recalculate MODIS products at daily interval if not available - LSWI, MOD43Albedo, MOD15LAI, MOD13VI

  13. Tasks (3) – timing of transition • Identify SOS for individual fluxes • Analyze the time course of MODIS products for signatures indicating the SOS. Develop CPI.

  14. Tasks (4) – spatial resolution • Within a landscape, patches differ • How small units can the CPI resolve? • Depends on the need for coarser resolution products – land cover, temperature, LAI & FPAR

  15. Tasks (5) – spatial continuity • Calculate spring indices (SI) for indicator species – cloned lilac & honeysuckle throughout NE US • Evaluate the relationship between developed CPI and SI

  16. Significance • Reduce uncertainties of interannual variability in C flux estimates • Reduce dependence of biogeochemical models on ground-based and generic observations of phenological change

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