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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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AGU 2019

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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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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.
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