Massive data processing in GRASS GIS 7: a new gap-filled MODIS Land Surface Temperature time series data set

Using GRASS GIS 7, we have reconstructed a seamless and gap-free time series for land surface temperature (LST) at continental scale for Europe from daily MODIS LST products, keeping the temporal resolution of four records per day and enhancing the spatial resolution from 1 km to 250 m. Our method constitutes a unique new combination of weighted temporal averaging with statistical modelling and spatial interpolation. In order to propose a worldwide reproducible method, we selected as auxiliary variables datasets which are globally available. In our presentation we will illustrate the technical challenges of managing a huge number of raster maps in the multi-terabyte range: how we mastered the limitations of file systems, and the data transfer within a cluster system. GRASS GIS 7 gained a lot from this multi-annual effort which has now been made available to the public. In our presentation we will provide examples for MODIS LST applications, such as disease risk assessment, epidemiology, environmental monitoring, and identification of temperature anomalies at continental scale. Aggregated derivatives of our dataset (following the BIOCLIM variable scheme) will be freely made online available for direct usage in GIS based applications.