I have spatio-temporal albedo (roughly, the 'reflectivity' of earth's surface) dataset, from NASA's MODIS satellite, for a 130 square kilometer area. The dataset contains raster files in the NetCDF format, with a file for each day, and a grid size of 500 m*500 m. There are a lot of 'NA' values in each file, due to cloud cover, satellite errors etc. Till now, I have simply spatially averaged the albedo data from the dataset to construct a simple time-series. I use this time-series to create a machine-learning based model to predict snow water equivalent.
I want to see if there's a way to include the spatial variability in the dataset, in the time-series. I'm also curious to know what would be the best way to spatially interpolate the data.
- Is there a way I can condense the variability, which might be due to factors such as elevation, aspect and slope of the area, into one or more time-series?
- I have looked at Principal Component Analysis/Empirical orthogonal functions to do the above. Can such methods be used for spatial averaging?
- What would be the best way to spatial interpolate, considering the numerous NA value cells? Is there a way to take into account the elevation, and other factors, into the interpolation?
Any suggestions would be greatly appreciated. Thanks!
Note: I use R for my analysis.