I have backscatter (radar image value) measurements of corn fields taken at multiple points along the growing season. I can estimate the expected backscatter value of corn by plotting the mean backscatter response at each time point. For any one time point I can calculate the standard deviation, which is useful to know because at some times during the year there is a wider range of plant behavior than at other times. The model of corn behavior can be used along with models for other crops to do crop classification.
Now I want to take the model I built during 2016 and try to apply it to images collected during 2017 (without having to collect a bunch of 2017 field data and build a new model from scratch). The radar images won't be collected on exactly the same dates in the two years, so I would like to estimate the average backscatter response at new dates. For example if I used images from May 1st and May 20th in the 2016 model, but for 2017 had an image from May 5th. To estimate the mean backscatter response I can do a simple linear interpolation between the two nearest time points in the model. Can I do the same thing for the standard deviation measurements? I'm trying to not accidentally make totally wrong mathematical assumptions with my standard deviation values (not being a statistician by profession).
In this image I've illustrated my original data (top), and the model I am trying to build (bottom).