Since your're working in a cross-sensor scenario (in which the 2 images you are trying to compare possess slightly different ranges for the spectral channels and probably have also different radiometric resolution / number of channels / etc) direct image differencing cannot be done, so don't worry about the difference tool. It's likely that you will obtain bad results, unless you're lucky (images are in reflectance and changes easy).
The simplest solution is to first classify images (you'll need to create single ground truths for each image), and then compare maps. This is called post-classification comparison. After this process you may need to filter the change detection, since many transitions obtained this way are either wrong or even impossible to obtain.
A second solution, is to resample pixels to the same size (images as well, possibly), stack both images in a single hypercube, and classify this data with a ground truth indicating all the transitions occurring (both no changes and changes). This is called direct multidate classification.
A third solution, which (to my knowledge) will require some coding, is to apply a projection/rotation/transformation to each single time image so that the distribution of the unchanged pixels in the transformed space are matched. Them, you apply standard image differencing-based methods. This is called feature-representation-transfer, a specific strategy from domain adaptation literature.
I guess that in your situation, the first one is the simplest to implement. If you still have some questions, don't hesitate to ask, as I personally worked a bit in cross-sensor change detection stuff!
(PS: I don't understand why your question has been flagged as too broad, honestly. It seems a legit one! )