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5th week of April

Moorthy, I., Miller, J. R., & Noland, T. L. (2008). Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level. Remote Sensing of Environment, 112(6), 2824-2838.
Generally leaf models were developed for broad leaves. To extend their generality to needles, the author fine tuned a broad-leaf model. 3 strategies -transmittance normalization factor, extinction coefficient and model inverted refractive index- were applied to fit needle's structure to the model. The structural information increased model performance for needles.

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1st week of december

Féret, J.B. et al., 2019. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231. Physical model has more ability to generalize itself then empirical or statistical models. But the calibration and the inversion algorithm can be essential and complicated step.
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4th week of may

Jain, V., Biesinger, M. C., & Linford, M. R. (2018). The Gaussian-Lorentzian Sum, Product, and Convolution (Voigt) functions in the context of peak fitting X-ray photoelectron spectroscopy (XPS) narrow scans.  Applied Surface Science ,  447 , 548-553. Spectral peaks are generally assumed to be Gaussian function. The width of its half maximum defines spectral resolution of the sensor. When interpolating spectral signal, linear one is risky especially near the peak wavelength.
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4th week of october

Dechant, B., et al. (2017). "Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism." Remote Sensing of Environment 196: 279-292. Photosynthetic traits has its mechanism by leaf internal components. Comparing the spectroscopic absorption of causing components and their traits, they can be detected and correlation. In the case of Vcmax, N dominantly controls it.
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