WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops

By Dong Li, T. Cheng, Kai Zhou, Hengbiao Zheng, Xia Yao, Yongchao Tian, Yan Zhu, W. Cao. 2017

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Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress (2018, background): “Li et al. (2017) showed that validated R2 ranged from 0.74 to 0.86 and RMSE varied from 0.25 g m−2 to 4.45 μg cm−2 at the canopy and leaf levels for the canopy chlorophyll content range of 0–2 g m−2 of cereal crops.”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, methodology): “Some of these studies mention the superiority of these advanced ANN designs as compared to standard ANN designs or other machine learning approaches in estimating vegetation properties (Du et al. 2016; Li et al. 2017; Pham et al. 2017).”