The general idea is to use an Optimal Interpolation procedure to fill the gaps. Depending on the temporal scales you are investigating you can also create weekly or monthly dataset where clouds should play a minor role.

I do not know Seadas, but you can easily find routines in matlab, Fortran, C, R and so on.

In the Med there are operational gaps-free products, you can check their procedure here:

http://marine.copernicus.eu/documents/PUM/CMEMS-OSI-PUM-010-021-022.pdf

Hi

the idea of creating a synthesis of images over several days is very useful and quite efficient, if you are not in the very heart of the convection zone.

Otherwise you can create and refer to a set of average situations, and calculate the deviation from this from observed data to interpolate within the cloud covered areas.

To my opinion it is always better to refer as much as possible to observed data than to purely use statistics.

Gil

If you compile the averages over a few weeks or months, you will be able to produce a composite image without the cloud cover issues.  Just mask the clouds out of the image.  This can be done in ENVI.  I believe there is an example in the help section. Best of Luck.

DINEOF is a software doing exactly what you describe. Take a look here: 

http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF

There are several options depending on what you want to do and the spatial and temporal size of the gaps in data. I've seen that for small gaps ppl only uses spatial filters or linear interpolation, for example. You could also empirically set something that interpolates both in space and time. 

In my case, over the argentinean sea (using MODIS L3, 8-day composites, 4.6 km), I had huge regions with no data for 3 months every year. I used HANTS (Harmonic ANalysis of Time Series, available as an Add-on in GRASS GIS 7) and DINEOF (available through the package sinkr in R). But for my case, DINEOF worked much better :)