Prediction of NDVI for Grassland Habitats by Fusing RapidEye and Landsat Imagery.

Data fusion is currently one of the most frequently implemented approaches for dealing with inconsistent timesseries. However, approaches to fuse multi-temporal satellite data with higher spatial resolution for the analysis of smaller landcover classes are still missing. Therefore, in this study the ESTARFM algorithm was used to fuse high spatial resolution RapidEye images with medium resolution acquisitions from Landsat 5 TM. The main objective was to predict NDVI values for heterogeneous semi-natural grassland classes for the year 2011. It can be shown, that it is possible to use the ESTARFM algorithm for predicting valuable and accurate NDVI values of small heterogeneous land-cover classes. However, the quality of the estimations depends on the temporal quality of the used input pairs and the date of prediction.