Spatial unmixing of MERIS data for monitoring vegetation dynamics

Abstract

Monitoring vegetation dynamics is fundamental to improve Earth systems models and to increase our understanding of the terrestrial carbon cycle and the interactions biosphere-climate. Medium spatial resolution sensors, like MERIS, have a great potential to study these dynamics at regional/global scales. However, the spatial resolution provided by MERIS (300m in full resolution mode) might not be appropriate over highly heterogeneous landscapes. This is why the synergistic use of MERIS full resolution (FR) and Landsat TM data is studied in this paper.
An unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over the Netherlands in 2003. The selected data fusion approach uses the linear mixing model and the information derived from Landsat TM imagery acquired in the same year to produce images that have the spectral and temporal resolutions provided by MERIS but with the spatial resolution of Landsat TM.
After the fusion, a quantitative assessment of the quality of the fused images was done in order to assess the validity of the proposed methodology and to evaluate the radiometric characteristics of the images. Finally, the time series of fused images was used to compute land cover specific NDVI, MTCI and MGVI profiles.