Multi- and hyperspectral geologic remote sensing: A review

Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes.

Multi - and hyperspectral geologic remote sensing : a review

Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines

https://www.geo.uzh.ch/microsite/rsl-documents/research/publications/peer-reviewed-articles/2011_Mulder_TheUseOfRemSens-3206019584/2011_Mulder_TheUseOfRemSens.pdf

The use of remote sensing in soil and terrain mapping — A review

M. van der Meijde

Papers

Spectral characteristics of salt-affected soils: A laboratory experiment

Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain

Detecting salt affected soils; Integrating surface and sub-surface measurements

A new approach in the detection of salt affected soils : integrating spectroscopy and geophysical measurements : abstract