ABSTRACT
MODIS Moderate-resolution imaging spectrometer NDVI Normalized di¦erence vegetation index NNT Neural network PAI Plant area index POLDER Polarization and directionality of Earth’s re¨ectance RT Radiative transfer SLC Soil leaf Canopy SVM Support vector Machine VI Vegetation index
Estimates of canopy biophysical characteristics are required for a wide range of agricultural, ecological, hydrological, and meteorological applications. Ÿese should cover exhaustively large spatial domains at several scales: from the very local one corresponding to precision agriculture where cultural practices are adapted to the within-¤eld variability, through resources and environmental management generally approached at the landscape scale, up to biogeochemical cycling and vegetation dynamics investigated at national, continental, and global scales. Remote sensing observations answer these requirements with spatial resolution spanning from kilometric down to decametric resolution observations according to the nomenclature proposed by Morisette (2010). Further, remote sensing from satellites brings the unique capacity to monitor the dynamics required to access the functioning of the vegetation.
