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Found 3 papers, 2 papers with code
Coupling in situ and remote sensing data to assess $α$- and $β$-diversity over biogeographic gradients
Despite prior investigations predominantly relying on limited datasets, our study endeavors to employ both in situ and RS data to assess plant and spectral species diversity across France at a high spatial resolution, integrating diverse metrics to unravel different biogeographical structures while gaining in understanding the relationship between plant and spectral diversity within and across bioregions.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping.
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents
Models of radiative transfer (RT) are important tools for remote sensing of vegetation, as they facilitate forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical properties from vegetation optical properties.
Quantitative Methods
