Thermal remote sensing of lava lakes: a comparison of approaches

Satellite-based thermal remote sensing is a useful tool for monitoring volcanoes. It involves detecting thermal anomalies, called 'hotspots,' and calculating the radiative energy emitted by volcanic activity. Various volcanic hot spot detection algorithms already exist in the literature. However, every algorithm has its advantages and disadvantages, as they are limited depending on the tradeoffs made during algorithm development, the sensor used for aquisition, and  the geometry of acquisition. Depending on the algorithm used, different results are obtained from the same data and, hence, different interpretations can be made in terms of, e.g., energy emitted, effusion rates, and eruption duration. In the present work, we aim at creating a new hotspot detection algorithm using MODIS and VIIRS imagery, which allows us to efficiently look at the dynamics of thermal emissions coming from persistent lava lakes, i.e., bassins of lava maintained molten through thermal convection and outgassing. We investigate the applicability of sensor fusion ideas, using multiple bands, and incorporating cloud cover information. We expect that by combining all available data the robustness of the detection process will increase.