1. Remote Sensing of LAI Conghe Song Department of Geography University of North Carolina Chapel Hill, NC 27599

2. Small Leaves do the Big Job CO 2 H2OH2O Losing water is the price to pay for gaining CO 2 stoma Underside

3. Big-Leaf Scaling up

4. Net Radiation Q QrQr LaLa LsLs Radiation Budget: Energy Budget: Net radiation is used for heat storage in the soil and plant body, evaporate water (latent heat), heating the air near the surface (sensible heat), and photosynthesis

5. Latent Heat (Evapotranspiration) TranspirationEvaporation T=f(R net, g s, g a, VPD, T) Uniform Canopy vs. Gappy Canopy (two-leaf scaling up) + = Evapotranspiration H2OH2O H2OH2O

6. Carbon Assimilation Plant Resp. Soil Resp. Net PSN Tower Measurement: Net Ecosystem Exchange (NEE) Measured data cannot separate Net PSN from plant (stems, leaves and roots) and soil (dead biomass) respiration. A model is needed to estimate the plant and soil respiration in order to get Net PSN.

7. Spectral Information of Remotely Sensed Imagery NDVI: [-1.0, 1.0] Red NIR

8. Challenges 1.Very difficult to get ground truth data: (1) Destructive Sampling: Based on forest inventory approach, cut a tree and measure its total leaf weight, take a sample of leaves to estimate the area to weight ratio (specific leaf area) and convert to the total leaf area for the tree. You will need to do multiple trees so that you can develop an allometric relationship between leaf area and diameter at breast height. The allometric relationship changes with species. Therefore, you have to do this for each species. (2) Optical measurements: Based on the light intensity inside and outside a vegetation canopy measured with optical instruments, and the estimate LAI under certain assumptions. 2. Many factors confound with remote sensing signals that can cause errors in estimating LAI (1) landscape heterogeneity: different types of vegetation, land-cover types. (2) leaf angle distribution, different degrees of leaf clumping. (3) variation of soil reflectance

9. Empirical Studies: LAI vs NDVI/SAVI Turner et al. 1999. RSE, 70: 52-68.

10. Empirical Study between LAI and NDVI/SR Chen and Cihlar, 1996. RSE, 55: 153-162.

11. NDVI vs. LAI from Ikonos Imagery Colombo et al. 2003, RSE,86: 120-131.

12. Empirical Studies Song and Dickinson, in press, IJRS.

13. LAI and Spatial Signals Song and Dickinson, in press, IJRS.

14. Physical Based Model Inversions from Remote Sensing MODIS/MISR LAI Product: (1)Global vegetation classified into 6 biomes: Broadleaf Forests, Needleleaf Forests, Shrubs, Savannas, Broadleaf Crops, Grasses and Cereal Crops. (2)Use 3-D radiation transfer algorithms to invert for LAI and built a look- up-table (LUT) to retrieve LAI operationally from remotely sensed signals. (3)Independent validations found that MODIS LAI provide a good estimates when LAI is low, while it can be significantly overestimated (Cohen et al., 2006).

15. Validation of MODIS LAI version 3 Cohen et al. 2003. RSE, 88:233-255

16. Validation of MODIS LAI version 4 Cohen et al., 2006. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1843-1857,