Using Landsat-8 Data in Preliminary Exploration for Geothermal Resources

To characterize geothermal potential areas, conventional surface exploration activities involve field surveys, gathering geothermal information from locals and review of any existing geothermal literature. This is not only time consuming and costly but also unreliable for inaccessible geothermal potential areas. Thus, this study explores the cost-effectiveness and powerful tools of satellite remote sensing in preliminary land surface characterization for expansive geothermal exploration. The main approach entailed the use of free-access Landsat-8 and atmospheric data to retrieve land surface temperature (LST) using split-window and single channel algorithm, analysis of retrieved surface products, validation using in-situ ground temperature data, and finally delineation of surface thermal anomalies associated with geothermal features. Gilgil district and Baringo County in Kenya made the study areas. The former is a known and confirmed geothermal area while the latter is only a geothermal prospect. The two areas sit on the central section of the Kenyan rift; geothermal belt, and combined form a suitable case study for preliminary exploration using Landsat-8 data. The main objective of the study was to demonstrate the use of satellite remote sensing data to identify surface thermal anomalies associated with geothermal features as a cost-effective geothermal exploration support tool. Identify the best LST retrieval method between split window and single channel method using Landsat 8 data, and finally employ the better retrieval method to characterize geothermal prospect area and suggest targets for further investigations. Results showed that free-access satellite remote sensing imagery can conveniently be used to identify and map surface thermal anomalies associated with geothermal features and thus can be employed to complement the main geothermal exploration studies namely geological, geochemical and geophysical. Further, single channel method had better LST retrieval results compared to split-window method when using Landsat-8 data