MODIS time series remote sensing for epidemiological modeling

In epidemiological modeling, survey data are usually collected at sampling sites and then regionalized within Geographical Information Systems (GIS). To enhance the data density, continuous field data such as land surface temperatures (LST), snow coverage, vegetation indices are commonly derived from satellite data. The recent launches of the new satellite systems Terra and Aqua significantly improve the situation of data availability for scientific purposes and epidemiological studies and predictions. The most interesting sensor onboard is MODIS which daily delivers two global coverages at 250m (Red, NIR), 500m (MIR) and 1000m resolution (TIR). The paper focuses on two of the numerous MODIS data products: Land Surface Temperatures (LST), and vegetation index 16-day composites. The integration of MODIS satellite data into a GIS requires several pre-processing steps, such as the reprojection from MODIS-ISIN or MODIS-SIN projections to another more common projection (UTM, national coordinate systems etc.). The resulting maps are filtered pixelwise by applying the related quality maps which are provided along the data products. Due to limitations in the official cloud detection algorithm used to create these land surface temperature quality maps, an outlier detection has been implemented. Based on the scene statistics, this outlier filter aims at removing all pixels which contain cloud temperatures instead of the desired land surface temperatures. Another set of MODIS time series data are NDVI and EVI vegetation indices. They can be implemented into epidemiological models to introduce vegetation dynamics. The 16-day composite product minimizes cloud cover and reflects at a sufficient temporal resolution the current vegetation status. The integration of MODIS data into epidemiological research enhances the spatio-temporal resolution of climatological data in particular in mountainous regions. The study area, a region of approximately 20000 sqkm, is of complex terrain with elevation ranging from nearly sea level to 3800 meters with a varying density of meteorological stations. The recent implementation of general time series processing for GRASS raster maps supports univariate statistics for a series of MODIS scenes. By selecting various time ranges and operators, a number of indicators can be calculated. The comparison of LST with ground truth time series from climatic stations showed that the LST match quite well with ground temperatures. While surface and aerial temperatures differ by definition, it is possible to transform surface to aerial temperatures by a regression model. Results and comparisons will be presented in the pape