F.J. García-Haro, B. Martínez, M.A. Gilabert

University of Valencia (SPAIN)
The current development of satellite technology provides improved spatial and spectral resolution of remote sensing data, which require their careful interpretation with the aid of reflectance models describing the complex process of the radiative transfer within the canopy.

This paper presents a teaching tool, which has been designed to enhance the learning in remote sensing. The software was implemented in IDL language, and is composed of a modular Graphic User Interface (GUI). The software produces optical images of simulated heterogeneous canopies corresponding to different domains (bidirectional, spectral, and spatial) over a wide range of ecosystems. This tool has been designed in support of remote sensing teaching activities in the University of Valencia. The tool enables graduate and undergraduate students examination and quantification of the main sources of error in remote sensing estimates of land surface variables.
The GUI allows the students generate several synthetic images that imitate complex ecosystems with several types of trees and shrubs. The geometric module allows the student introducing different crown shapes, crown sizes, stem densities, and distributions (random, clumped, regular as in plantations). Model inputs include variables with intrinsic physical meaning or connected with field-measurable quantities.

The teaching GUI generates also sensitivity analysis results, which enable the student quantifying the relative contribution of vegetation properties to reflectance measurements (spectral bands, directional effects, etc.). Operational monitoring of vegetative cover by remote sensing involves the utilisation of vegetation indices (VIs), most of them being functions of the reflectance in red (R) and near-infrared (NIR) spectral bands. The GUI tool allows an immediate examination of the R-NIR plots, facilitating the practical understanding of key features (soil line concept, adjusted vegetation isolines over different soil backgrounds, etc.) and assesses the performance (linearity, sensitivity to soil brightness and color) of a family of VIs. This tool allows thus to improve the estimation of parameters by inversion, once prevailing effects have been determined.