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5th week of Jan

Banskota, A., Wynne, R. H., Thomas, V. A., Serbin, S. P., Kayastha, N., Gastellu-Etchegorry, J. P., & Townsend, P. A. (2013). Investigating the utility of wavelet transforms for inverting a 3-D radiative transfer model using hyperspectral data to retrieve forest LAI. Remote Sensing, 5(6), 2639-2659.
Continuum wavelength transformation is similar to CNN in deep learning. Pooling layer of CNN adjusts the resolution of 2D image. On the image, we see objects ranging from infinitesimal one to broad one. CNN imitates how human being sees. Similarly, CWT well captures signals from microscopic signal, Chlorophyll, to wide signal, such as LMA and water.
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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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1st week of August

The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography The paper mapped above ground biomass using satellite radar and LiDAR fusion. Satellite radar systems provides global data, as it passes through clouds. However, radar cannot produce vertical profile. Satellite LiDAR system, the paper targeting, provides accurate canopy point clouds. On the contrary, clouds occlude LiDAR incident wave. As the author mentioned, it only covered only 4% of the surface for 2 years. To combine LiDAR with radar, LiDAR data was compared to radar and then extended to radar. Validation was done plots where the coverage of LiDAR and radar overlapped. Reference Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., ... & Armston, J. (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing, 1, 100002.
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AGU 2019

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