Estimation and monitoring of bare soil/vegetation ratio with SPOT VEGETATION and HRVIR

Leaving fields with a vegetation cover during the winter is one of the main ways to reduce water pollution, in restricting pollutant fluxes towards rivers. The bare soils/vegetation ratio monitoring can be carried out daily at a coarse spatial resolution with SPOT VEGETATION (1 km), and also at a higher spatial resolution with SPOT HRVIR (20 m), but with less repetitive and spatially more restricted data. Land-cover changes detected at a regional scale with this ratio can be explained by winter vegetation covering changes as well as by influence of climatic events. Therefore, observed changes have to be validated from a local scale analysis. The aim of this study is to develop a method that allows to assess high or low variations detected at a regional scale from SPOT VEGETATION images with data registered at a higher scale, SPOT HRVIR images in our case. In this study, the link between the images of the two sensors is set up from the design of an Artificial Neural Network method based on a Kohonen Self-Organizing Features Map. The originality of this method lies in the use of temporal dimension to solve such a change of scales.