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1st week of May

Streher, A. S., da Silva Torres, R., Morellato, L. P. C., & Silva, T. S. F. (2020). Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments. Remote Sensing of Environment, 244, 111828.
A spectral information projects its foliar traits, each of which exists with diverse proportion in a leaf. When LMA was over 300, model prediction didn't work well. In that region, other foliar traits must have contaminated LMA signals on reflectance. That uniqueness distinguishes savana from the other ecosystems.

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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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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.
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AGU 2019

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