In my most recent published paper, I analysed the effects of incoming solar UV-B radiation on the geochemistry of Atlas cedar pollen, focused on the Middle Atlas Mountains in Morocco. The study area was relatively small, with sample sites fairly close together.
The UV-B data was obtained from the glUV: Global UV-B radiation dataset, which combines data from NASA's Ozone Monitoring Instrument (OMI) onboard the Aura spacecraft, into grid cells containing average erythemally weighted estimates of daily UV-B radiation. You can read full details of the methods used in the associated research paper (Beckmann et al. 2014) (Available open access).
Gridded datasets are an excellent source of data for doing global or macro-scale studies. However, if working in a relatively small area, you may find that your study area is covered by just a few grid cells due to the often low resolution of gridded data. And this can sometimes make it more difficult to carry out analysis.
To overcome the problem, you can interpolate the data to increase the resolution. After interpolation, the gridded data will go from looking like the image on the left, to looking like the image on the right, which is much more detailed for the study area.
Read on to find out how to do this in R!
