To me it seems more natural with a linear regression model.

To just test one single value, the last year, with a mean of the rest does not seem as natural for me. (It would not be OK with a z-test unless you know the variances and so on.)

Regression seems ok. I guess that you expect a trend in the data. (Is it because of the green house effect?)

I don’t think you need to start with thinking of this in terms of “repeated measures anova”. (That stuff is about some other aspects.) Of course you do repeated measures but what you want is simply the time trend – if I have interpreted you correct. Don’t start with making it unnecessarily complicated. But you must look for autocorrelation in the residuals.

Of course you can have many sorts of non-linearity in the trend. But the natural first thing seems to be to simply plot the data, the eleven yearly points and look at it, and choose a model from that.

You might also need to transform the “y-variable”, the greenness variable. The usual assumption is the dependent variable is normally distributed, given the “x-variable” (here the time variable) but it could also have other distributions (like the gamma distribution for example in a generalized linear model).

A bigger difficulty is maybe about how to aggregate the many pixels from one image to one value for that year. You have about 800.000 pixels (10.000*20.000/250), don’t you? Without going into multi-level models, maybe it is possible to calculate a trimmed mean by throwing away the 10% highest numbers and the 10% lowest. That would make the estimate more robust to small fractions of outliers.

Hopefully someone else will comment on this. Unfortunately, on this site, it seems like if one person has responded then few others - if any – respond. There are so many here on this site that knows so much more than I do. We need to listen and learn from each other.