This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LST_gt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.

In order to extract LST_gt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km² centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.

The results show consistent spatial and temporal LST_gt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LST_gt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LST_gt. Results further show that areas with strong topography can result in anomalously high LST_gt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LST_gt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m² in the geothermal area, which coincides with the LST_gt flux rates obtained with the proposed approaches.

The proposed approaches are a first step in estimating LST_gt at large spatial coverage from remote sensing and LSM data series, and provide an innovative framework for future improvements.