Climate data for ecological understanding
A common problem faced when trying to combine climate data with ecological data to understand their interactions is the different spatial scales involved. Commonly, climate products such as gridded observations or reanalysis are only available at a far coarser resolution than that for which biological data is available, such as the satellite-derived normalized difference vegetation index (NDVI).
Many techniques exist to re-map coarse-resolution climate data onto finer-resolution grids - but Kriging is one of the best options. Firstly, you can include important co-variates which determine the climate fields such as elevation, slope, and slope angle. These allow the model to account for fine-scale variations in height and solar incidence, which can have a large influence on air temperature and other climate variables, when generating the high-resolution data. But Kriging also preserves the uncertainty associated with the estimate of the climate variables at this higher resolution, enabling you to include confidence in the climate data in your model of climate-ecology interaction.
One such example is shown below where we Krig the climate data from its native 64 x 64 km resolution down to the 9 x 9 km resolution of the NDVI data: