Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble salts can provide a record of annual humidity and temperature. In fact, temporal monitoring of salt efflorescence from mine wastes at a mine site in the Iberian Pyrite Belt (Huelva, Spain) has been achieved using hyperspectral airborne Hymap data. Furthermore, climate variability estimates are possible based on oxidation stages derived from well-known sequences of minerals, by tracing sulfide oxidation intensity using archive spectral libraries. Thus, airborne and spaceborne hyperspectral remote sensing data can be used to provide a short-term record of climate change, and represent a useful set of tools for assessing environmental geoindicators in semi-arid areas. Spectral and geomorphological indicators can be monitored on a regular basis through image processing, supported by field and laboratory spectral data. In fact, hyperspectral image analysis is one of the methods selected by the Joint Research Centre of the European Community (Ispra, Italy) to study abandoned mine sites, in order to assess the enforcement of the European Mine Waste Directive (2006/21/EC of the European Parliament and of the Council 15 March 2006) on the management of waste from extractive industries (Official Journal of the European Union, 11 April 2006). The pyrite belt in Andalucia has been selected as one of the core mission test sites for the PECOMINES II program (Cracow, November 2005), using imaging spectroscopy; and this technique is expected to be implemented as a monitoring tool by the Environmental Net of Andalucía (REDIAM, Junta de Andalucía, Spain).

The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.

The identification and mapping of the mineral composition of by-products and residues on industrial sites is a topic of growing interest because it may provide information on plant-processing activities and their impact on the surrounding environment. Imaging spectroscopy can provide such information based on the spectral signatures of soil mineral markers. In this study, we use the automatized Gaussian model (AGM), an automated, physically based method relying on spectral deconvolution. Originally developed for the short-wavelength infrared (SWIR) range, it has been extended to include information from the visible and near-infrared (VNIR) range to take iron oxides/hydroxides into account. We present the results of its application to two French industrial sites: (i) the Altéo Environnement site in Gardanne, southern France, dedicated to the extraction of alumina from bauxite; and (ii) the Millennium Inorganic Chemicals site in Thann, eastern France, which produces titanium dioxide from ilmenite and rutile, and its associated Séché Éco Services site used to neutralize the resulting effluents, producing gypsum. HySpex hyperspectral images were acquired over Gardanne in September 2013 and an APEX image was acquired over Thann in June 2013. In both cases, reflectance spectra were measured and samples were collected in the field and analyzed for mineralogical and chemical composition. When applying the AGM to the images, both in the VNIR and SWIR ranges, we successfully identified and mapped minerals of interest characteristic of each site: bauxite, Bauxaline^® and alumina for Gardanne; and red and white gypsum and calcite for Thann. Identifications and maps were consistent with in situ measurements.

Reflectance spectroscopy is a widely used technique for mineral identification and characterization. Since modern airborne and satellite-borne sensors yield an increasing number of hyperspectral data, it is crucial to develop unsupervised methods to retrieve relevant spectral features from reflectance spectra. Spectral deconvolution aims to decompose a reflectance spectrum as a sum of a continuum modeling its overall shape and some absorption features. We present a flexible and automatic method able to deal with various minerals. The approach is based on a physical model and allows us to include noise statistics. It consists of three successive steps: (i) continuum pre-estimation based on non-linear least-squares; (ii) pre-estimation of absorption features using a greedy algorithm; (iii) refinement of the continuum and absorption estimates. The procedure is first validated on synthetic spectra, including a sensitivity study to instrumental noise and a comparison to other approaches. Then, it is tested on various laboratory spectra. In most cases, absorption positions are recovered with an accuracy lower than 5 nm, enabling mineral identification. Finally, the proposed method is assessed using hyperspectral images of quarries acquired during a dedicated airborne campaign. Minerals such as calcite and gypsum are accurately identified based on their diagnostic absorption features, including when they are in a mixture. Small changes in the shape of the kaolinite doublet are also detected and could be related to crystallinity or mixtures with other minerals such as gibbsite. The potential of the method to produce mineral maps is also demonstrated.