I'm analysing surface reflectance values from satellite imagery to detect the flowering event of a single species. I want to test whether the change in values during the peak flowering period is significant (whether flowering produces a change in SR values). The time series is only over one year, and has 75 values total from across 5 areas (15 values per area), so there might be correlation of values within the same sites. Struggling to find a way to apply change detection methods I've found to this case. Is it a matter of building a prediction model and then showing that the real values don't follow this during peak flowering months?

The peak flowering for the species is during March and April, so there are 3-4 values during this period, with the remainder of values distributed throughout the other months.