1st Week of October

Kim, H. S., Palmroth, S., Thérézien, M., Stenberg, P., & Oren, R. (2011). Analysis of the sensitivity of absorbed light and incident light profile to various canopy architecture and stand conditions. Tree Physiology, 31(1), 30-47.

In this paper, they analyzed canopy light absorption, considering leaf angle distribution, leaf clumping and stand density. To compare canopy properties, they set models and compare how well each model estimates measured canopy light absorption. Canopy properties are controlled in 7 models (v1 – v7). The simplest model (v1), only considering Beer-Lambertian gap fraction, was developed to most complex model (v7) by gradually adding another properties. They are compared in respect of various LAI and sky condition.

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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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