Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity

Abstract

Several high spatial resolution thermal infrared (TIR) missions are planned for the coming decade and their data will be crucial to constrain volcanic activity patterns throughout pre- and post-eruption phases. Foundational to these patterns is the subtle (1−2 K) thermal behavior, which is easily overlooked using lower spatial resolution data. In preparation for these new data, we conducted the first study using the entire twenty-two-year archive of higher spatial, lower temporal resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. This archive presents a unique opportunity to quantify low-magnitude temperature anomalies and small plumes over long time periods. We developed a new statistical algorithm to automatically detect the full range of thermal activity and applied it to >5000 ASTER scenes of five volcanoes with well-documented eruptions. Unique to this algorithm is its ability to use both day and night data, account for clouds, quantify accurate background temperatures, and dynamically scale depending on the anomaly size. Results improve upon those from the more commonly used lower spatial resolution data, despite the less frequent temporal coverage of ASTER, and show that high spatial resolution TIR data are equally as effective. Significantly, the smaller, subtle thermal detections served as precursory signals in ∼81% of eruptions, and the algorithm's results create a framework for classifying future eruptive styles.