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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Remote sensing imagery is being used intensively to estimate the biochemical
content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of
our need for vegetation biochemical information and our increasing ability to obtain
canopy spectral data, a few techniques have been explored to scale leaf-level biochemical
content to the canopy level for forests and crops. However, due to the contribution of
non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in
semi-arid grasslands, it is difficult to obtain information about grassland biochemical
content from remote sensing data at the canopy level. This paper summarizes available
methods used to scale biochemical information from the leaf level to the canopy level and
groups these methods into three categories: direct extrapolation, canopy-integrated
approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we
conclude that all methods are useful, but none are ideal. It is recommended that future
research should explore a systematic upscaling framework which combines spatial pattern
analysis, canopy-integrated approach, and modeling methods to retrieve vegetation
biochemical content at the canopy level
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