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5th week of April

Moorthy, I., Miller, J. R., & Noland, T. L. (2008). Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level. Remote Sensing of Environment112(6), 2824-2838.
Generally leaf models were developed for broad leaves. To extend their generality to needles, the author fine tuned a broad-leaf model. 3 strategies -transmittance normalization factor, extinction coefficient and model inverted refractive index- were applied to fit needle's structure to the model. The structural information increased model performance for needles.

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4th week of june

Asner, G. P., Martin, R. E., Anderson, C. B., & Knapp, D. E. (2015). Quantifying forest canopy traits: Imaging spectroscopy versus field survey.  Remote Sensing of Environment ,  158 , 15-27. They use canopy sunlit reflectance at plot level and the trait samples from sunlit. The plot averaged refletance minimize canopy architectural effect. However actual field samples cover only 5% of a plot, the plot reflectance well explains canopy traits.

4th week of november

Bousquet, L., Lachérade, S., Jacquemoud, S. and Moya, I., 2005. Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment, 98(2-3): 201-211. Specular reflectance originates from leaf surface. On the other hand, diffuse reflectance originates from leaf intrastructure. To focus on the target traits, the two reflectance needs to be separated.

AGU 2019