Otsu's method does not really care about actual values since it tries to minimize the total variance within classes while maximizing the distance between the classes. So, you could just run Otsu on your original data (no need to rescale) and it will provide you with the optimal threshold to use to achieve the goal listed above.
I don't know what is your main goal but, if you want to segment out specific regions, one of the problems with the original Otsu's method is that it is a global method and will treat your dataset as a series of points not spatially related to each other and, given the high dynamic range of speckle noise, it would probably be hard to clearly separate the two classes "foreground" from the "background".
I would suggest the use of a speckle reducing filter (a list of some can be found here) followed by a global or adaptive local thresholding method. This will provide you with the indexes of the pixel belonging to "foreground" and "background" and you can use these indexes to segment your original image, if this is your goal.