Spatially continuous data of environmental variables are often required for environmental
sciences and management. However, information for environmental variables is usually …

Spatial interpolation methods have been applied to many disciplines. Many factors affect the
performance of the methods, but there are no consistent findings about their effects. In this …

Forecasts of maximum and minimum air temperatures are essential to mitigate the damage
of extreme weather events such as heat waves and tropical nights. The Numerical Weather …

Spatial continuous data (spatial continuous surfaces) play a significant role in planning, risk
assessment and decision making in environmental management. They are, however …

Machine learning methods, like random forest (RF), have shown their superior performance
in various disciplines, but have not been previously applied to the spatial interpolation of …

Incoming solar radiation (insolation) is fundamental to most physical and biophysical
processes because of its role in energy and water balance. We calculated insolation maps …

Spatially high resolution climate information is required for a variety of applications in but not
limited to functional biodiversity research. In order to scale the generally plot-based research …

Long‐term time series of key climate variables with a relevant spatiotemporal resolution are
essential for environmental science. Moreover, such spatially continuous data, based on …

Seven methods of spatial interpolation were compared to determine their suitability for
estimating daily mean wind speed surfaces, from data recorded at nearly 190 locations …

Activities, goals, and overall context of a user can be inferred through statistical fusion of
multiple sources of evidence. The context data is presented to the user via the wireless …