I am trying to validate vegetation indices estimated using Sentinel 2A images with data obtained with a spectroradiometer. I would like to know which is the better pretratament to applied to the data. Other works recomended processes such as normalizing, smoothing or correct the scatter oscattering effect. But, what is necessary to take in account to applied these preprocesses?
Thank you!!
You only need to normalise if you are think the data should fit a normal distribution or if you want to use parametric tests. Otherwise, you can use non parametric tests, where normal data is not a prerequisite. You can also fit probability distributions to your data and select the one with the lowest AIC value. Data smoothing and scatter correction may be useful to remove noise and outliers :)
It might be helpful to use the response function of the Sentinel in order to calculate the vegetation indices on ground level.
Thank you very much Andrew Paul McKenzie Pegman and Ittai Herrmann! Your answer are very useful!
dear you need to do a lot but the best just do re-sampling then calculate the vegetation indeices
Good luck
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