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.
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.
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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