Considerable effort has been expended in developing methods of estimating physical pasture parameters such as biomass and leaf area index from remotely-sensed data. Most of this work has transformed the remotely-sensed data into a vegetation index that is used to estimate the parameters using an empirically derived regression equation. Two problems with this approach are that a vegetation index can only estimate one independent parameter and the occurrence of brown material in the canopy seriously impairs the quality of the regression equation. A different approach has been hypothesised and evaluated in this article. This approach assumes that a pasture canopy is primarily a mixture of green and brown canopy, and soil background elements. The method derives multiple independent parameters that can be used to develop regression equations to estimate multiple independent physical parameters. This article reviews the theoretical basis and experimental work done to evaluate this hypothesis using reflectance data for one summer growing pasture community in which the main species is Mitchell Grass (Astrebla lappacea). The article finds that the method produces significantly better estimates of a number of pasture parameters than does the vegetation index approach.