VALIDATION OF THE USE OF MULTIPLE LINEAR-REGRESSION AS A TOOL FOR UNMIXING COARSE SPATIAL-RESOLUTION IMAGES

When estimating agricultural production, we are faced with a problem o f scale, be they scales of observation, modeling, or representation. I n the context of the farming landscape of Europe, the relationships be tween scales and modeling take on special importance as the aim is to extract information on a smaller scale than the scale of observation, the latter necessarily being large relative to the average size of fie lds in order to obtain the required time resolution. This article pres ents a method for unmixing coarse resolution signals (of the NOAA-AVHR R type) through the use of multiple linear regression. This allows the signal for each mixed coarse resolution pixel to be broken down thank s to a knowledge of land use and a linear mixture model. It is then po ssible to calculate the individual radiometric contribution of each co nstituent of a mixed pixel. The work presented here is an assessment o f the performance of this method using SPOT-HRV data, degraded to simu late the coarse resolution and as normally provided to produce the lan d use information. Simulation with SPOT-HRV data also enables various parameters to be calculated, from which it is possible to verify the i nterest and reliability of multiple linear regression as a method for spectral unmixing.