Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications (Reconstructed MODIS Land Surface Temperature Dataset - http://gis.cri.fmach.it/eurolst/).
Source: Metz M, Rocchini D, Neteler M. (2014) Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail.Remote Sensing 6(5): 3822-3840. doi: 10.3390/rs6053822
