Introduction
The proposed algorithm called here TOA_VEG is based on the training of neural networks over a data base
simulated using radiative transfer models. It requires top of atmosphere MERIS reflectances and associated
geometry as inputs and allows the derivation of four vegetation biophysical variables:
- fAPAR: it corresponds to the fraction of photosynthetically active
radiation absorbed by the canopy and depends both on the canopy structure and illumination conditions.
In order to be consistent with most of the potential users who are dealing with daily integrated fAPAR value
as well as with other fAPAR products that are considering the instantaneous fAPAR value at the time of the
satellite overpass under clear sky conditions (e.g. MODIS), we decided to use the fAPAR at 10:00 solar time
under clear sky conditions. Note also that the fAPAR refers only to the green parts
(leaf chlorophyll content higher that 15µg.cm-2) of the canopy.
- fCover: it corresponds to the gap fraction for nadir direction.
It is a canopy intrinsic variable that does not depend on variables such as the geometry of illumination
as compared to fAPAR. For this reason, it is a very good candidate for the replacement of classical vegetation
indices for the monitoring of green vegetation. Note that similarly to LAI and fAPAR, only the green elements
(leaf chlorophyll content higher that 15µg.cm-2) will be considered.
- LAI: it is defined as half the developed area of green
(leaf chlorophyll content higher than 15 µg.cm-2) vegetation elements per unit of horizontal soil Note that
LAI of vegetation as estimated from remote sensing will include all the green contributors, i.e. including
understory when existing under forest canopies.
- LAI.Cab: the chlorophyll content can be calculated both at the
leaf level and at the canopy level by multiplication of the leaf level chlorophyll content by the leaf
area index. As the medium resolution scale of MERIS is generally associated with heterogeneous pixels, the
product LAI.Cab more sound than the leaf level chlorophyll content: what would be the chlorophyll content of
a pixel with half of very sparse canopy with very high leaf chlorophyll content and half of very dense
canopy with very low leaf chlorophyll content? Therefore, the estimation of LAI.Cab has been preferred to
that of the leaf chlorophyll content.
More Information
More information about the MERIS TOA-VEG processor implementation can be found in these chapters:
- TOA-VEG Algorithm
- TOA-VEG Processor