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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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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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