Landsat is available back to the 80s, it may overlap the dates of your project, excepting of course the 1950s.
edcsns17.cr.usgs.gov/NewEarthExplorer/ will let you easily browse the archive, once you apply for a username.
With that in mind you could potentially get a series of three satellite scenes, two of which tie in with the aerial imagery.
For land use cover you may be interested in a number of features, but probably NDVI is a good place to start. After processing to NDVI you could do a couple different types of classification depending on your intent.
Be aware that processing to NDVI should include atmospheric correction if you want to get quality results. And for going back this far in the Landsat Archive you will (as far as I know) come up against calibration issues. A good resource on these is the LEDAPS project, which also has a free tool to do processing on Linux, which I haven't used.
http://ledaps.nascom.nasa.gov/docs/docs.html
A more detailed answer could be given if your were more specific about the question you are trying to investigate.
I also recommend GRASS as a suitable platform for processing Landsat imagery. I successfully use the development version, WinGrass7 and the modules i.landsat.toar i.landsat.acca in the atmospheric corrections stages. If you are on Linux use the current release and install these plugins as options. GRASS has many other functions suited to Landsat including the creation of near true colour images, which I have not used.
GRASS
If you intend to do a lot of cartography on the output from GRASS, consider using QGIS for that, it is easy to display the GRASS rasters in QGIS.
Fundamentally the question here is "what does 'scientifically valid' mean". If you are looking to do spectral modelling on the data, then the answer is possibly different than if you are looking at doing classification / image segmentation. Pansharpening (depending on the method) is simply going to change the range of the values a fairly small amount and shouldn't put your reflectance values outside the realm of possibility.
All in all, it depends a lot on what application you are going to be using the data for. Furthermore, the impact of pansharpening may also be worth documenting as a partial side result in whatever study you are performing. The result may be that it doesn't add anything, except four times as many pixels, meaning four times as long a processing time, which in some cases is a showstopper.
Edit: My database of articles on this topic is not huge, but I have these two where pansharpend data is used (with reasonable results) for image segmentation:
Shackelford, A. K., & Davis, C. H. (2003). A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(10), 2354–2364. http://doi.org/10.1109/TGRS.2003.815972
Fernández, I., Aguilar, F. J., Aguilar, M. A., & Álvarez, M. F. (2014). Influence of Data Source and Training Size on Impervious Surface Areas Classification Using VHR Satellite and Aerial Imagery Through an Object-Based Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4681–4691.
Best Answer
Please mention the sensor of Landsat 5, is it MSS or TM? Assuming it is Thematic Mapper data, you have visible red and shortwave infrared data. You can directly infer from the band reflectance values about where vegetation patches lie and hence moisture content.
Band 3 (Red) can help you discriminate vegetation slopes and Band 5 (SWIR) can help you discriminate moisture content of soil and vegetation. You can calculate band ratios or normalized band indices to ascertain drought stress or soil moisture.
You can calculate Normalized Difference Water Index (NDWI) or Normalized Difference Soil Index (NDSI) and Normalized Difference Vegetation Index (NDVI).
For Landsat TM, NDWI = (Band 3 - Band 5)/ (Band 3 + Band 5). This is the only index you can calculate with the data available to you. If you have access to Band 4 (NIR), you can additionally compute Soil index,NDVI,NVMI and Soil adjusted vegetation index (SAVI). Hope this helps.