Combining vegetation index with mineral identification for detection of high-geothermal-potential zones using hyperspectral satellite data

Geothermal energy represents a large-output, high-capacity, and sustainable energy source for electric power generation, with critical implications for the transition toward a low-carbon society; hence, it is crucial to accurately explore and assess geothermal resources. Many areas rich in geothermal resources are located in non-arid, densely vegetated regions. Therefore, the purpose of this study was to develop a method, applicable at the first stage of regional resource exploration, using hyperspectral remotely-sensed images to detect surface geothermal manifestations with high reliability in densely vegetated areas. The Patuha geothermal field in West Java, Indonesia, was selected as the study area given the availability of accumulated survey data to validate our proposed method. A single satellite image acquired by the Hyperion sensor was used for the case study. Two vegetation indices were defined to detect spectral features of stressed vegetation: a blue shift of the red edge and an increase in shortwave-infrared reflectance. These indices were suitable to detect vegetation stress under soil acidification conditions caused by ascending geothermal water and gases. After normalization to a zero mean and unit standard deviation, these indices were combined into a single vegetation index considering blue shift and shortwave-infrared reflectance (VIBS). The advantage of the VIBS over the normalized difference vegetation index was demonstrated by better correspondence with geothermal manifestations and better consistency along major faults. By further combining the VIBS values (in vegetated areas) with mineral weights calculated by linear spectral unmixing for kaolinite (in non-vegetated areas), we proposed a new index, the geothermal manifestation potential (GMP). General matching between high-GMP zones and geothermal manifestations or fault traces demonstrated the usefulness of this index; this was confirmed by field survey measurements of reflectance spectral features characterizing vegetation under geothermal stress. Additionally, the highest-GMP zones were located near surface water possessing high sulfate concentrations and above a deep vapor-dominated underground reservoir.