Assessing the accuracy of land surface characteristics estimated from multi-angular remotely sensed data

New missions and technically advanced sensors are being developed by researchers to monitor our planet from space at different spatial and spectral resolutions. Characterizing the terrestrial biomes on a global scale is a key issue in understanding climate change and the evolution of the Earth's atmosphere system. New advanced models on radiation in plant stands and more heterogeneous biomes are used to interpret satellite- and airborne sensor data and identify information on the Earth's surface. This paper presents an investigation on estimation of canopy and leaf level quantities by multidirectional remotely sensed data in three spectral bands. The purpose is to evaluate the goodness of a model in a controlled environment, using artificial input data. The results of the experiment indicate that the required information on leaf optical properties can be derived with a good accuracy within the constraints of the experiment. Estimated stand structure characteristics are more prone to error. Scaling issues, including temporal, spectral and spatial resolution, and surface heterogeneity are not addressed in this experiment.