I am using remote sensing (satellite) images and am trying and differentiate (automatically i.e. using a computer and without viewing the image myself) between areas that fall under cloud cover (with respect to the sun, not with respect to the satellite), the clouds themselves, and areas that are receiving full sunlight.

It is proving difficult to find sources of data to be able to validate the method I am using and I am now looking outside the box. My question is this: would it be possible to use combination of metadata (such as mentioned in this question) from an image file to estimate whether or not the photograph was taken under clear sky conditions or cloudy conditions?

The estimation does not need to be very accurate, more along the lines of "lots of natural light", "not a lot of natural light".

EDIT for clarity:  

I am able to identify clouds in the satellite images quite easily and flag the pixels in a pre-processing step, but its is not possible to validate that all the pixels in thousands of these processed images have been correctly flagged. Thus I am concerned with using any information possible from photographs taken on the ground to see if I can validate that the pixels that contain cloud cover have been correctly flagged.


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