Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km × 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity.

New tools and algorithms for geological remote sensing are developed and verified at test sites throughout the world in preparation of the German hyperspectral satellite Mission (EnMAP), which is an Environmental Mapping and Analysis Program. The Aggeneys Cu-Pb-Zn deposit, situated in the arid north western part of South Africa, represents a unique field laboratory for testing these new tools. Here spaceborne hyperspectral data covering the Swartberg, Big Syncline and Gamsberg area were collected by the Hyperion sensor. New synergies between multispectral and hyperspectral spaceborne data can be demonstrated, such as the Iron Feature Depth index (IFD), which has recently been proposed for mine waste mapping in the North West Province of South Africa and for gossan detection at Haib River in South Namibia. The work presented here explores the potential of the IFD for gossan mapping and characterization at Gamsberg and Big Syncline, from EO-1 ALI and Landsat-8 OLI data together with mineral maps from expert systems such as the United States Geological Survey (USGS) Material Identification and Characterization Algorithm (MICA), and first results from EnMAPs EnGeoMAP algorithm. Field spectroscopic measurements and field sampling were carried out to validate and calibrate the results from the expert systems and the IFD. This ground truthing is a necessary complementary step to link the results from the expert systems and the IFD to in-situ field spectroscopy. Future mineral exploration initiatives may benefit from the techniques described here, because they can significantly narrow the expensive, exploration activities such as hyperspectral airborne data, field activities and drilling, by identifying the most promising mineral anomalies in an area from the spaceborne data.