A remote sensing data fusion approach to monitor agricultural areas

Describes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation; then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior