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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.

AGU 2019

2nd Week of August

Schaepman-Strub, Gabriela, et al. Reflectance quantities in optical remote sensing—Definitions and case studies. Remote sensing of environment 103.1 (2006) 27-42. If a surface was not an ideal specular or diffuse surface, one could observe diffuse light as well as specular light reflected off the surface. Reflectance is affected by where the incident light comes from and where the light is observed, which is represented by an angular distribution function. So different reflectance concepts are possible, so it is needed to use the term reflectance practically.