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1st week of june

Feilhauer, H., Asner, G. P., Martin, R. E., & Schmidtlein, S. (2010). Brightness-normalized partial least squares regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer, 111(12-13), 1947-1957.
Leaf spectra varies with canopy structure, such as leaf angel, LAI etc. They affects the magnitude of spectra. Brightness normalization is one option to alleviate canopy effects. In doing so, spectral magnitude is normalized without any changes in the shape. It could improve model performance by reducing shade heterogeneity within canopy.

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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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