Analysis of thermal regimes at Tenerife(Canary Islands) with Independent Component Analysis applied to time series of Remotely Sensed Land Surface Temperatures

Land surface temperature (LST) is a manifestation of the surface thermal environment (LSTE) and an important driver of physical processes of surface land energy balance at local to global scales. Tenerife is one of the most heterogeneous islands among the Canaries from a climatological and bio-geographical point of view. We study the surface thermal conditions of the volcanic island with remote sensing techniques. In particular, we consider a time series of Landsat 8 (L8) level 2A images for the period 2013 to 2019 to estimate LST from surface reflectance (SR) and brightness Temperature (BT) images. A total of 26 L8 dates were selected based on cloud cover information from metadata (land cloud cover < 10%) to estimate pixel-level LST with an algorithm based on Radiative Transfer Equations (RTE). The algorithm relies on the Normalized Difference Vegetation Index (NDVI) for estimating emissivity pixel by pixel. We apply the Independent Component Analysis (ICA) that revealed to be a powerful tool for data mining and, in particular, to separate multivariate LST dataset into a finite number of components, which have the maximum relative statistical independence. The ICA allowed separating the land surface temperature time series of Tenerife into 11 components that can be associated with geographic and bioclimatic zones of the island. The first ten components are related to physical factors, the 11th component, on the contrary, presented a more complex pattern resulting possibly from its small amplitude and the combination of various factors into a single component. The signal components recognized with the ICA technique, especially in areas of active volcanism, could be the basis for the space-time monitoring of the endogenous component of the LST due to surface hydrothermal and/or geothermal activity. Results are encouraging, although the 16-day revisit frequency of Landsat reduces the frequency of observation that could be increased by applying techniques of data fusion of medium and coarse spatial resolution images. The use of such systems for automatic processing and analysis of thermal images may in the future be a fundamental tool for the surveillance of the background activity of active and dormant volcanoes worldwide.