Abstract Thermal observations of volcanic activity when the volcano is partially covered by clouds or observed under a wide-scan angle are often removed from further analyses. In the event of a volcanic crisis, such a reduced set of data is not adequate. Even when the observation conditions are favourable, the full observation set is still required to provide decision-makers with quality information about the data. Automatic quality estimation and outlier detection was not estimated in the past. We propose to analytically define the uncertainty for individual observations based on the measurement circumstances. To additionally reduce the temporal noise of the radiant power ( RP ) time series we apply a Kalman Filter (KF). The KF is able to recursively analyse an unevenly sampled time series. Based on some proposed rules, it can also detect outliers. We apply the proposed methodology to the 2008–09 Etna eruption monitored by MODIS (Moderate Resolution Imaging Spectroradiometer). The analysis of the results shows that the topography has a greater influence on RP than previously considered.

Abstract The activity status of a volcano can be evaluated from the remotely measured radiant power ( RP ). The measured RP contains noise due to reasons such as atmospheric effects and instrument characteristics. Here we first show how to estimate the uncertainty of each single RP measurement. To additionally reduce the temporal noise of the RP time series we apply a Kalman filter. The Kalman filter is able to recursively analyse an unevenly sampled time series. To validate our filtering scheme, we applied it to an eruption of Etna in 2002, as well as to the eruption of Nyamuragira in 2010. In the case of the Etna eruption, the denoised time series agrees well with in situ observations of a waxing and waning flow. For the case of Nyamuragira, the enhanced time series of RP shows more fluctuation probably due to cloud coverage. Thus, we propose a multiple instrument approach that increases the temporal resolution of the RP time series and reduces the associated noise.