Resolution Enhancement of Incomplete Thermal Data of Earth by Exploitation of Temporal and Spatial Correlation

Many remote sensing applications require the availability of radiometric surface temperature information with both high acquisition rate and high spatial resolution, but unfortunately this requirement is still not achievable through a single sensor. However, the huge amount of remote sensed data provided by several heterogeneous spaceborne sensors allows to use data fusion in order to overcome this issue. In this paper, we propose a method 14 sharpening thermal images in a nearly real-time scenario, also capable to deal with missing data due to cloudy pixels. Moreover, we analyze the robustness of the method with respect to cloud mask misclassifications and assess its effectiveness via numerical simulations based on SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data.