Observation of the Earth from space allows us to take the pulse of our planet in order to address global environmental problems. Recent developments in satellite-based optical sensors have provided new types of spatial information and improved capacity for large-scale environmental monitoring of terrestrial vegetation. The latest superspectral satellite missions designed for terrestrial tracking, as well as upcoming imaging spectrometers (e.g. FLEX, PRISMA, EnMAP), will provide unprecedented data flow. This data flow will open up a wide range of opportunities to quantify a wide range of biochemical and structural properties of vegetation. The processing of such wide data flows requires appropriate and precise techniques that allow the spatial-temporal obtaining of biophysical variables. In order to prepare for this new era of Earth observation, this conference will summarize the most modern methods of obtaining images that have been applied in experimental studies of image spectroscopy that infer all types of biophysical variables associated with vegetation. These methods are classified under: (1) parametric regression, including vegetation indices, shape indices, and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physics-based, including inversion of radiative transfer models (RTM) using numerical optimization and transformation tables; and (4) hybrid regression methods, combining RTM simulations with machine learning regression methods. For each of these categories, an overview of the methods used to map vegetation properties will be provided. Also, the prospects of implementing these methods in future processing chains for the operational procurement of vegetation properties from satellite will be discussed, and some examples will be discussed in view of the upcoming FLuoresence Explorer (FLEX) mission. The presentation will close with recommendations for the new generation of operational products of biophysical variables with which to quantify the trends followed by the vegetation of our changing planet.