Satellite images (also known as Earth observation imagery, spaceborne photography, or simply satellite photos) are images of Earth collected by imaging satellites that provide a large-scale view of geographical areas (essential for mapping, topographic analysis, and land use studies). Satellite imaging companies sell these images by licensing them to governments and businesses such as Apple Maps, Google Maps and Mapbox. The spectrum of satellite images is diverse, including visible light, near-infrared light, infrared light, radar and many others. In mineral exploration, satellite imagery is used to identify geological formations, mineral-rich regions, and surface anomalies that could indicate subsurface mineral deposits. The most common type of satellite imagery used in mineral exploration includes multispectral (ASTER, Sentinel 2) and hyperspectral data, which allow for the analysis of specific wavelengths to detect particular minerals or rock types.

Satellite data is collected using remote sensing technology (imaging satellites in orbit observing the earth at distance), which can be passive (capturing reflected sunlight) or active (emitting signals like radar and capturing their reflections). After capturing images, satellites transmit the data to ground stations using radio waves. This data can then be processed and enhanced to provide detailed images.

A lot of satellite imagery comes in RGB image formats such as .png, .ecw, or .jpeg/JPEG2000 (JP2), with metadata (i.e, information about the acquisition, including geographic bounds, quality indicators, and band-specific details) accompanying in an .xml file. For VRIFY AI’s purposes, we require the actual rendered data grids in .grd, .ers or GeoTIFF format which come with embedded geographic reference information (such as projection & spatial resolution) and are easy to visualize in GIS software.

Common platforms for viewing and analyzing satellite data include software like ArcGIS, QGIS, and Google Earth Engine. The data can be displayed as:

Satellite imagery can be visualized via many different techniques, including true (RGB) colour imagery or false colour composites, heatmaps, and 3D visualizations (Google Earth).

In both geospatial and mineral exploration, satellite imagery has revolutionized how scientists and geologists analyze the Earth’s surface. Its ability to cover vast areas quickly and repeatedly has made it an invaluable tool for monitoring changes and detecting potential resource-rich regions. Pertinent applications include:

Visible light imagery, Infrared, Sentinel and Aster datasets are brought into the VRIFY AI workflow as individual feature layers (2D raster images); when multiple bands/channels are involved, each band has its own discrete layer in the data stack. Satellite images can be used as a raw input feature to generate targets (especially in areas with low vegetation or soil cover) or to derive other features such as soil geochemical maps or alteration maps.

We utilize both private/proprietary and publicly available datasets, checking for coverage compared to the AOI, correct projection, and separate channel inputs. Metadata retained throughout the workflow includes Company/Asset, import date, minimum and maximum corner coordinates, elevation, cell size, projection/EPSG, and the original file name.

For more information on ASTER and Sentinel-2, see index at the bottom of this article.

The ASTER is an imaging instrument onboard Terra, the flagship satellite of NASA's Earth Observing System (EOS) launched in December 1999. The “Advanced Spaceborne Thermal Emission and Reflection Radiometer” obtains high-resolution images of Earth in 14 different wavelengths of the electromagnetic spectrum, ranging from visible to thermal infrared light. Scientists use ASTER data to create detailed maps of land surface temperature, emissivity, reflectance, and elevation.

ASTER's ability to capture data in multiple spectral bands is particularly useful for identifying surface materials. In mineral exploration, ASTER data can be used to identify the spectral signatures of various minerals. For example, ASTER's shortwave infrared (SWIR) bands are effective in detecting hydrothermal alteration zones—areas where hot fluids have chemically altered the rocks. These zones often indicate the presence of valuable minerals like copper, gold, and silver. The thermal infrared bands, on the other hand, help in distinguishing between rock types based on their heat-emitting properties, making it easier to identify potential mineral-bearing areas.

ASTER data is also widely used for mapping geological structures, such as faults, folds, and fractures, which are crucial for understanding the subsurface geology, and helps geologists focus on regions where mineral deposits are more likely to be found.

Sentinel-2 is a satellite constellation operated by the European Space Agency (ESA) as part of the Copernicus program. Launched in 2015, Sentinel-2 carries multispectral sensors that capture data across 13 spectral bands, from visible light to shortwave infrared. The spatial resolution varies between 10 and 60 meters, depending on the spectral band. Sentinel-2 data has become increasingly important in geospatial and mineral exploration applications due to its high revisit frequency (every 5 days with two satellites) and wide area coverage (290 km swath width).

In mineral exploration, Sentinel-2’s shortwave infrared (SWIR) bands are particularly valuable. These bands can detect specific minerals and alteration zones that are not visible to the naked eye. The SWIR bands are useful for identifying clay minerals, which often form during hydrothermal alteration. These alteration zones are indicators of potential mineral deposits, such as porphyry copper systems, which are typically associated with large-scale mineralization.

Sentinel-2’s visible and near-infrared (VNIR) bands are useful for mapping vegetation cover, which can also provide indirect clues to the underlying geology. In some cases, vegetation anomalies (areas where plant health is unusually poor) may indicate the presence of metal-rich soils or toxic minerals at or near the surface. Sentinel-2's high temporal resolution allows geologists to track seasonal changes in vegetation and other land cover, which may reveal hidden geological structures.

Moreover, Sentinel’s multispectral can be combined with other satellite datasets (such as ASTER or Landsat) to enhance mineral detection capabilities. This synergy between different satellite sensors allows for more detailed and accurate geological mapping, especially in remote or difficult-to-access areas.

Reach out to your dedicated VRIFY AI Contact or email Support@VRIFY.com for more information.