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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 Environment, 112(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 August

Farquhar, G. D., von Caemmerer, S. V., & Berry, J. A. (1980). A biochemical model of photosynthetic CO 2 assimilation in leaves of C 3 species.  Planta ,  149 (1), 78-90. Carbon assimilation is mainly controlled by electron, light which is reducing electron from water molecule, O2 and CO2. They consist of the model.
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4th week of october

Dechant, B., et al. (2017). "Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism." Remote Sensing of Environment 196: 279-292. Photosynthetic traits has its mechanism by leaf internal components. Comparing the spectroscopic absorption of causing components and their traits, they can be detected and correlation. In the case of Vcmax, N dominantly controls it.
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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.
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