Chapter 16
A NEW APPROACH TO CHARACTERIZE
GLOBAL LAND SURFACES
Preliminary results from AVHRR data
N. Gobron, B. Pinty and M. M. Verstraete
Space Applications Institute, Ispra, Italy.
1.
INTRODUCTION
The upcoming generation of satellite sensors will provide more and much
higher quality data (specifically much better spectral and angular sampling
of the radiative fields emerging from terrestrial surfaces) than have been
available so far. These data are of primary interest for providing an improved
characterization of land surfaces which, for global scale investigations, have
traditionally been based on the temporal analysis of a vegetation index (such
as the Normalized Difference Vegetation Index) computed from AVHRR
data. Indeed, the radiance fields measured by satellites do depend on the
radiative properties of the surface (among other factors), which include a
number of key variables such as the Leaf Area Index (LAI). Estimating the
values of these surface variables on the basis of remote sensing data reduces
to the solution of an inverse problem. An algorithm designed to identify the
most probable solutions amongst a set of potential solutions that are
predefined in a Look-Up Table (LUT) has been implemented and explored.
These solutions have been derived from direct simulations achieved with a
one-dimensional physical model of radiation transfer in vegetation canopies.
In this approach, every pre-defined solution is associated to the set of values
for all the physical variables required by the radiation transfer model used to
generate the LUT. Taken together, these sets of variables constitute a
quantitative characterization of the land surfaces fully compatible with the
radiative properties measured by the particular satellite used in this
application.
143
M.M. Verstraete et al. (eds.), Observing Land from Space: Science, Customers and Technology, 143–150.
© 2000 Kluwer Academic Publishers. Printed in the Netherlands.
A NEW APPROACH TO CHARACTERIZE
GLOBAL LAND SURFACES
Preliminary results from AVHRR data
N. Gobron, B. Pinty and M. M. Verstraete
Space Applications Institute, Ispra, Italy.
1.
INTRODUCTION
The upcoming generation of satellite sensors will provide more and much
higher quality data (specifically much better spectral and angular sampling
of the radiative fields emerging from terrestrial surfaces) than have been
available so far. These data are of primary interest for providing an improved
characterization of land surfaces which, for global scale investigations, have
traditionally been based on the temporal analysis of a vegetation index (such
as the Normalized Difference Vegetation Index) computed from AVHRR
data. Indeed, the radiance fields measured by satellites do depend on the
radiative properties of the surface (among other factors), which include a
number of key variables such as the Leaf Area Index (LAI). Estimating the
values of these surface variables on the basis of remote sensing data reduces
to the solution of an inverse problem. An algorithm designed to identify the
most probable solutions amongst a set of potential solutions that are
predefined in a Look-Up Table (LUT) has been implemented and explored.
These solutions have been derived from direct simulations achieved with a
one-dimensional physical model of radiation transfer in vegetation canopies.
In this approach, every pre-defined solution is associated to the set of values
for all the physical variables required by the radiation transfer model used to
generate the LUT. Taken together, these sets of variables constitute a
quantitative characterization of the land surfaces fully compatible with the
radiative properties measured by the particular satellite used in this
application.
143
M.M. Verstraete et al. (eds.), Observing Land from Space: Science, Customers and Technology, 143–150.
© 2000 Kluwer Academic Publishers. Printed in the Netherlands.