From the description, a t-test is the way to go. To rephrase what you said, you have two sites, and multiple observations at each site. This is the classic example of a two-sample independent T-Test (wiki).
The gist of it is you need to not only see how far apart the means are, you need to know how variable the data is. That way you can say the difference in means is unlikely to be attributable to random sampling variance. The t-test tells you exactly that.
You can do a t test in excel as fast as any of the stats programs. You just need two columns of SDI values, one for each site. Then you use the function
t.test(), select your first column for the first parameter, the second column for your second parameter, use a 2-tailed test (wiki) for the third parameter (enter the number 2), and use the second option for the last parameter (homoskedastic).
If you use R, the function is also
t.test(), but there are multiple ways you can set up your data. Here is a tutorial. Basically, you could have two columns, one for each site like Excel, or one column with all your SDI values, and another column that categorizes your data (i.e. tells you whether that row was a sample from Site A or Site B).
There are a lot of other considerations, for instance your samples from your sites ideally would have similar standard deviations. If they are very different, you can use option 3 for the last parameter in Excel. I won't give a full step-by-step in R, but there are plenty of ways to test for differences in standard deviation and correct for it. However, basic t-tests assuming homogeneity of variance (i.e. what I described above) should get you going.