1. Xiangming Xiao Institute for the Study of Earth, Oceans and Space University of New Hampshire, USA The third LBA Science Conference, July 27, 2004, Brasilia, Brazil Spatial Patterns and Temporal Dynamics of Photosynthesis and Transpiration in Amazon Basin -- A satellite-based novel approach: Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM)

2. Publication of the Vegetation Photosynthesis Model Xiao, X., et al., 2004, Remote Sensing of Environment, 89: 519-534 Xiao, X., et al., 2004, Remote Sensing of Environment, 91:256-270 Xiao, X., et al., 2004, Remote Sensing of Environment, (in review) Xiao, X., et al., 2004, Ecological Applications, (in review)

3. Topics of the presentation What are the fundamental differences between the VPM model and existing Production Efficiency Model (e.g., CASA, GLO-PEM, MODIS-PSN)? What are the key hypotheses we proposed and employed in the VPM model? What are the implications of those hypotheses on field observations and models? A case study in the Tapajos km67 flux tower site

4. Is the VPM model just another Production Efficiency Model?  Yes: GPP =  g x PAR absorbed  No: VPM uses three novel approaches Leaf chloroplast Leaf water content Leaf age

5. Composition perspective Leaf and canopy are composed of photosynthetically active vegetation (PAV, chloroplast) and non-photosynthetic vegetation (NPV, e.g., cell wall, branch). Leaf/canopy (g /m 2 ) = NPV + PAV FAPAR = FAPAR NPV + FAPAR PAV Structure perspective : canopy ------------------  leaf ------------------  chloroplast Plant Area Index Leaf Area Index (branch, twigs) (cellwall, vein) Fraction of PAR absorption FAPAR canopy ----------  FAPAR leaf ----------  FAPAR chl Alternative approach to the LAI – NDVI – FAPAR paradigm ---- Leaf chloroplast

6. Satellite-based Production Efficiency Model (PEMs) e.g., MODIS-PSN (Running et al., 1999; Turner et al., 2003) CASA (Potter et al., 1993), GLO-PEM (Prince et al., 1995) Motivation LAI NDVI FAPAR LAI – NDVI – FAPAR paradigm

7. Chlorophyll-related vegetation indices Enhanced Vegetation Index (EVI, Huete et al., 1997 ) Alternative approach to the LAI – NDVI – FAPAR paradigm ---- Leaf chloroplast

8. A case study at Tapajos km67 site

9. Model and analysis at individual flux tower sites Comparison between NDVI and EVI at Tapajos km67 site from 10-day composite VGT images (4/1998 – 12/2002) NDVIEVI What drives the difference between NDVI and EVI? What are the ecological implications?

10. Model and analysis at individual flux tower sites Seasonal dynamics of Enhanced Vegetation Index Hypothesis: leaf phenology (leaf litterfall, leaf emergence)

11. Water-related vegetation indices Land Surface Water Index (LSWI, Xiao et al, 2002) Alternative approach to soil water, vapor pressure deficit ---- Leaf water content (g /m 2 )

12. Model and analysis at individual flux tower sites Seasonal dynamic of Land Surface Water Index Hypothesis: forests with deep root system

13. GPP = (  0  T scalar  W scalar  P scalar )  FAPAR PAV  PAR LSWI EVI Surface reflectance (VGT, MODIS) Climate data CO 2 eddy flux tower Validation Literature Satellite-based Vegetation Photosynthesis Model (VPM) Satellite-based Vegetation Transpiration Model (VTM) TR =  0  GPP

14. Model and analysis at individual flux tower sites Validation of the VPM model for evergreen broadleaf forest site-specific CO 2 flux and climate data (from Steven Wofsy)

15. Model and analysis at individual flux tower sites Validation of the VTM model for evergreen broadleaf forest site-specific CO 2 flux and climate data (from Steven Wofsy)

16. Take-home messages 1.Chlorophyll – EVI – FAPAR PAV hypotheses offer information on leaf chlorophyll, leaf phenology and leaf age of tropical forests.  link to leaf litterfall and leaf emergence data 2.LSWI offers information on leaf water content and deep root system of tropical forests,  map forests with deep root systems. 3.Hypotheses implemented in the VPM model still need to be tested across biomes, using both radiative transfer model and field/laboratory measurements. 4.The VPM/VTM models may offer much improved capability for quantifying seasonal dynamics and spatial patterns of photosynthesis and transpiration in Amazon basin.

17. Acknowledgement Remote sensing and modeling Remote sensing and modeling Qingyuan Zhang, Stephen Boles, Michael Keller, Stephen Frolking, Berrien Moore III University of New Hampshire CO 2 eddy flux tower sites CO 2 eddy flux tower sites Tropical evergreen broadleaf forest – Tapajos km67 site in BrazilTropical evergreen broadleaf forest – Tapajos km67 site in Brazil Steven Wofsy, Scott Saleska and Lucy Hutyra Steven Wofsy, Scott Saleska and Lucy Hutyra Harvard University Plinio De Camargo Plinio De Camargo Centro de Energia Nuclear na Agricultura (CENA), Universidade de Săo Paulo, Brazil

18. Ecosystem Carbon Fluxes NEE = GPP – R e Approaches to estimate GPP 1.Process-based models (TEM) 2.Remote sensing (GLO-PEM, MODIS-PSN) 3.CO 2 eddy flux tower Motivation

19. Change in Satellite Observation Platforms for Vegetation Study The era of Meteorological Satellite – AVHRR (1980s - present) NDVI – LAI – FAPAR paradigm The era of Earth Observation System – (late 1990s to present) VEGETATION sensor onboard the SPOT-4/5 satellites MODIS sensor onboard the Terra/Aqua satellites Can we go beyond the NDVI – LAI – FAPAR?