Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

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Multi-method ensemble selection of spectral bands related to leaf biochemistry (2015, unknown): “This established approach is a suitable benchmark for methodological studies since its performance characteristics are well known (Axelsson et al., 2013).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “In SVM, the support vector regression (SVR) transforms the input data into a high-dimensional feature space using a nonlinear kernel function to minimize training error and the complexity of the model (Axelsson et al. 2013).”
Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges (2019, methodology): “The former are developed to inverse forest biophysical parameters [86,87], while the latter are commonly utilized to retrieve these parameters based on parametric statistical and non-parametric machine learning algorithms [53,56,60,63].”