Current methods for locating new sources of uranium are time-consuming and expensive, often requiring governments and mining companies to fly planes over large swaths of lands in remote areas. With the wide variety of Earth observation data now easily accessible, allowing us to detect more and more from space at a low cost, and the recent increases in computers’ processing power, which made it possible to sort through the large amounts of data necessary for analysis in a short time, satellites have become an extremely attractive way to help detect features in remote areas.

As a Ph.D. candidate in the UAB School of Engineering, Reda El-Arafy, a geologist by training and now an assistant professor of nuclear geology and remote sensing with Egypt’s Nuclear Materials Authority, worked with mentors Sarah Parcak and Scott Brande to find a more cost-effective way – through sensor systems on Earth-observation satellites – to identify promising targets for uranium exploration in the southwestern Sinai desert.

Establishing ground truth

El-Arafy first selected 30+ areas in the southwestern Sinai in which uranium-rich deposits were suspected. A critical component of the study was to collect geological samples throughout the area for which advanced satellite imagery was available. Multi-spectral sensors can capture data in several spectral bands, with some sensors recording up to 10 different bands. Hyper-spectral sensors however record data in much narrower spectral bands, allowing us to focus our attention in very small specific parts of the electromagnetic spectrum. This is why El-Arafy chose to study data from the Landsat-8, ASTER, and HYPERION satellites, as minerals of interest in this study are highly recognizable in the short-wave infrared and the thermal-infrared spectral bands detected by multi-spectral and hyper-spectral sensors onboard.

To help with El-Arafy’s research, Egypt’s Nuclear Material Authority also shared data from a gamma-ray spectrometer they flew over the study area, which provided additional information for ground truth interpretation. This new set of data was overlain on satellite images to help identify anomalies or variations in the satellite sensor data and to later optimize algorithms for post processing.