LAUSR

ABSTRACT In this study, we reviewed methods, sensors and study areas in estimation of vegetation biophysical (VBph) and biochemical (VBch) parameters using satellite optical remote sensing, in terms of (1) their performance and (2) extent of their use. Performance of parametric regression methods is dependent on the type of sensor, vegetation parameter, vegetation cover and biome of study area. Artificial neural network, partial least-squares regression, random forest, support vector machine and Gaussian process regression (GPR) had acceptable performances in the estimations. Performance of physically based methods depends on selecting appropriate radiative transfer model, inversion method and cost function, optimal solution (e.g. mean of some of the best lookup-table solutions) and adding appropriate Gaussian noise. In recent years, the use of active learning techniques (especially Euclidean distance-based diversity), to optimize training datasets against field datasets, with machine learning regression algorithms (MLRAs) (especially GPR) has led to acceptable performances by hybrid methods in estimation of VBph and VBch parameters using real and/or simulated (Re/Si) satellite hyperspectral (and multispectral) data with dimensionality reduction techniques in the spectral domain (especially principal component analysis with 20 principal components). In overall, Re/Si satellite optical sensors data containing red-edge (mostly for VBch parameters) and/or short-wave infrared (mostly for VBph parameters) spectral band(s) performed well. Emerging trend observed in recent years in the estimations by using Re/Si medium spatial resolution sensors data with relatively good spectral resolutions (e.g. precursore iperspettrale della missione applicativa, environmental mapping and analysis program and Sentinel-2 multispectral instrument) and non-parametric regression (especially MLRAs) and hybrid methods.