Departamento de Física Teórica, Atómica y Óptica - Quantalab ...

ABSTRACT
The Atmospheric Optics Group at the University of Valladolid (GOA-UVA)
focuses on the light-interaction processes with the atmosphere, both in the experimental
context of spectroradiometric measurement as well as in the theoretical research and
development of radiative transfer models. This work opens a new research line within
the research group, aimed at the study of the radiative transfer in vegetal canopies. One
of the main objectives is to understand the basis for the physical processes that take
place in the interaction between light and vegetation. Progress made on its measurement
and analysis will enable the understanding in obtaining information from the
environment by means of remote sensing tools.
A promising methodology for estimating leaf and canopy biophysical parameters
from spectral reflectance is the inversion of radiative transfer models. Nevertheless, in
spite of international efforts make progress on such objective, current developments do
not allow an easy and straight forward method for estimating canopy parameters unless
a previous knowledge about the initial conditions of the vegetation are known. The
retrieval of biophysical variables using canopy reflectance models is limited by the fact
that conducting canopy reflectance model inversions is an ill-posed problem. This
means that radiative transfer models could simulate a similar reflectance from different
sets of combined input parameters. Therefore, small changes in the measured
reflectance could generate critical differences in the estimated parameters by model
inversion. Lately, new research ideas suggest using seasonal spectral data as a potential
solution for the ill-posed problem.
This work aims at contributing toward a methodology to improve estimates from
canopy reflectance models, focusing on methods to reduce the ill-posed problem.
Research consisted on two approximations based on multiple analyses of temporal and
spatial datasets, which introduced improvements on the biophysical parameter
estimation by radiative transfer model inversion, as compared with traditional methods.
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