Geothermal Heat Flux Assessment Using Remote Sensing Land Surface Temperature and Simulated Data. Case Studies at the Kenyan Rift and Yellowstone Geothermal Areas

Abstract:

In this work we propose an innovative approach to assess the geothermal heat flux anomalies in the regions of the Kenyan Rift and the Yellowstone geothermal areas. The method is based on the land surface temperature (LST) differences obtained between remote sensing data and land surface model simulations. The hypothesis is that the model simulations do not account for the subsurface geothermal heat source in the formulation. Remote sensing of surface emitted radiances is able to detect at least the radiative portion of the geothermal signal that is not in the models. Two methods were proposed to assess the geothermal component of LST (LST_gt) based on the aforementioned hypothesis: a physical model and a data mining approach. The LST datasets were taken from the Land Surface Analysis Satellite Application Facilities products over Africa and the Copernicus Programme for North America, at a spatial resolution of 3-5 km. These correspond to Meteosat Second Generation and Geostationary Operational Environmental Satellite system satellites data respectively. The Weather Research and Forecasting model was used to simulate LST based on atmospheric and surface characteristics using the Noah land surface model. The analysis was carried out for a period of two months by using nighttime acquisitions. Higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer data were also used on the Kenyan area to produce similar outputs employing existing methods. The comparison of the results from both methods and areas illustrated the potential of the data and methodologies for geothermal applications.