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3rd week of Feb

Geladi, P., et al. (2004). "Hyperspectral imaging: calibration problems and solutions." Chemometrics and Intelligent Laboratory Systems 72(2): 209-217.
One point calibration is more dependent to sensor noises, dark noise probably fluctuating. However, quadratic one can capture more subtle signal.
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3rd week of october

Wang, Z., et al. (2010). "Large variations in Southern Hemisphere biomass burning during the last 650 years." Science 330(6011): 1663-1666. Biomass burning and methane oxidation affect the amount of CO. C derived from biomass burning has higher delta C-13 than methane oxidized C. Also, there is fraction between O-16 and O-18. Using delta C-13 and delta O-18, CO amounts and their relative source can be retrieved.
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4th week of september

Brodrick, P. G., Davies, A. B., & Asner, G. P. (2019). Uncovering Ecological Patterns with Convolutional Neural Networks.  Trends in ecology & evolution . When extracting foliar information from high resolution image, it would be quite hard work to detect the leaf, which one wants to know in the map. In the map, the pixel information varies depending on individual leaf angle. And it can change depending on solar zenith angle, which changes every observation. T hough one is interested in just the information of a certain leaf, s patial pattern have to be considered.
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2nd week of Jan

Skidmore, A. K., et al. (2010). "Forage quality of savannas — Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery." Remote Sensing of Environment 114(1): 64-72. Canopy N was calculated just multiplying leaf N to LAI. In this paper, more precise estimate of canopy N was attributed to higher estimate of LAI. On leaf scale, the leaf structure itself did not account for leaf spectroscopy better than foliar traits. However, on UAV level, canopy structure affects to the spectroscopy directly.
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