On some methodological issues of monitoring volcanic regions of Kamchatka based on radar interferometry data: example of Avachinsky-Koryaksky group

The paper discusses the methodological issues of regular monitoring of volcanic regions of Kamchatka using satellite radar interferometry methods. The advantages and limitations of various displacement estimation methods are analyzed with respect to different satellite data sets over the Avachinsky–Koryaksky volcanic group for the period from 2016 to 2024. The radar data varied in frequency range (L and C), acquisition geome-try (ascending and descending satellite orbit), and number of acquisitions: both short series of images with long temporal intervals between acquisitions and long series of images with short temporal intervals were considered. Based on these data, displacement fields were calculated for image pairs (DInSAR method), cumulative dis-placement fields (summation method), and average displacement velocity fields (SBAS method) over the eight-year period. The dynamics of surface displacements on volcanic slopes were analyzed using time series. It was found that the average displacement rate does not exceed ±10 mm/year, while cumulative displacements on volcanic slopes reach values of approximately ±10 cm over the eight-year period. The results obtained using different approaches are in good agreement with each other, taking into account the inherent uncertainties to each method. At the same time, for each approach there are conditions related to the climatic characteristics of Kamchatka under which it is impossible to obtain satisfactory results due to low data coherence. Therefore, monitoring must take into account local terrain characteristics, climatic conditions, and the properties of satellite systems providing radar data. It is shown that the results obtained by different methods exhibit a high correlation (0.60–0.77), indicating the reliability of the displacement field estimates. Thus, the considered interferometric methods complement each other, and their combined application makes it possible to improve the accuracy of result interpretation and to ensure verification of monitoring data.