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1st week of Jan

Yang, X., et al. (2016). "Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests." Remote Sensing of Environment 179: 1-12.
Collection covers all the seasons. Summer is expected to have less variations on the plant's phonology. However, when the model was trained by a season data and then tested to the other seasons, the summer model showed the least RMSE. It means summer was the best season to collect leaves with divers status. It's harsh light must affect various level of stress to sunltis and shaded leaves.
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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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2nd week of december

El Alem, A., Chokmani, K., Agili, H., Poulin, J., Laurion, I., Venkatesan, A. E., & Dedieu, J. P. (2019, December). Potential of a Drone Hyperspectral Data-Based Model to Remote Estimate Chlorophyll-a Concentration from Sentinel 2A and 2B Sensors Data. In  AGU Fall Meeting 2019 . AGU. As for ocean chlorophyll contents, they are not distributed evenly. Rather, their amounts are polarized. The author harmonized two models, the one is classifying model and the other is retrieving model.
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