Estimation of Crop LAI using hyperspectral vegetation indices and a hybrid inversion method

By Liang Liang, Liping Di, Lianpeng Zhang, Meixia Deng, Zhihao Qin, Shuhe Zhao, Hui Lin. 2015

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Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, methodology): “Therefore, hybrid inversion methods have the advantages of empirical methods’ simplicity and physical models’ universality while being able to estimate the properties of vegetation more accurately and quickly (Liang 2005; Liang et al. 2015).”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “With the advantage of distinguishing subtle spectral difference, HSIs have been widely applied in many fields [1]–[5].”
Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification (2017, background): “fields, such as agriculture [1], environment management [2], and mineral exploitation [3].”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, background): “Various of these band selection studies investigated combinations of VIs that led to the best prediction models (Chen et al. 2015; Feng et al. 2016; Jia et al. 2013; Liang et al. 2015; Mutanga and Kumar 2007; Pôças et al. 2017; Schlerf and Atzberger 2006).”