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2nd week of may

Singh, A., Serbin, S. P., McNeil, B. E., Kingdon, C. C., & Townsend, P. A. (2015). Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications, 25(8), 2180-2197.
When optimizing ML model, sub-models are generated at each loop. They all also project features of the training set. From them, the model's probability distribution was acquired and the uncertainty of the model prediction was evaluated.

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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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AGU 2019

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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.
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