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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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AGU 2019

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.

1st Week of August

Jacquemoud, S., et al. "Estimating leaf biochemistry using the PROSPECT leaf optical properties model."  Remote sensing of environment  56.3 (1996): 194-202. Leaf reflectance is affected by leaf biochemicals as well as by pigments or water. Inspecting NIR reflectance, N which is highly correlated to protein and C which are highly correlated to  cellulose and lignin  can be detected.