The Impact of Dynamic Emissivity–Temperature Trends on Spaceborne Data: Applications to the 2001 Mount Etna Eruption

Abstract

Spaceborne detection and measurements of high-temperature thermal anomalies enable monitoring and forecasts of lava flow propagation. The accuracy of such thermal estimates relies on the knowledge of input parameters, such as emissivity, which notably affects computation of temperature, radiant heat flux, and subsequent analyses (e.g., effusion rate and lava flow distance to run) that rely on the accuracy of observations. To address the deficit of field and laboratory-based emissivity data for inverse and forward modelling, we measured the emissivity of ‘a’a lava samples from the 2001 Mt. Etna eruption, over the wide range of temperatures (773 to 1373 K) and wavelengths (2.17 to 21.0 µm). The results show that emissivity is not only wavelength dependent, but it also increases non-linearly with cooling, revealing considerably lower values than those typically assumed for basalts. This new evidence showed the largest and smallest increase in average emissivity during cooling in the MIR and TIR regions (~30% and ~8% respectively), whereas the shorter wavelengths of the SWIR region showed a moderate increase (~15%). These results applied to spaceborne data confirm that the variable emissivity-derived radiant heat flux is greater than the constant emissivity assumption. For the differences between the radiant heat flux in the case of variable and constant emissivity, we found the median value is 0.06, whereas the 25th and the 75th percentiles are 0.014 and 0.161, respectively. This new evidence has significant impacts on the modelling of lava flow simulations, causing a dissimilarity between the two emissivity approaches of ~16% in the final area and ~7% in the maximum thickness. The multicomponent emissivity input provides means for ‘best practice’ scenario when accurate data required. The novel approach developed here can be used to test an improved version of existing multi-platform, multi-payload volcano monitoring systems.

Modeling of Geophysical Flows through GPUFLOW

“…In particular, analysis of 'aa' lava samples from the 2001 Mt. Etna eruption, over a wide range of temperatures (773 to 1373 K) and wavelengths (2.17 µm to 21.0 µm) showed that emissivity is temperature-dependent, leading to differences up to 20% in the final emplacement of the simulated lava flows [42]. In light of this study, we found an empirical relationship that links the emissivity and temperature of lava:…”

Infrared spectroscopy of volcanoes: from laboratory to orbital scale

“…Although Ramsey et al (2019) attempted to account for liquid vs. crust coverage by using a different emissivity value for each (i.e., the two-component emissivity model), utilized high-spatial resolution multispectral TIR camera data to link changes in the emissivity directly to the temperature (i.e., the variable emissivity model). A later study by Rogic et al (2022) also demonstrated this trend using the GPUFLOW (Capello et al, 2022) model, with their results showing changes in both the runout distance as well as the width of the lava flow compared to the results obtained by using a constant emissivity value. These are important considerations for hazard planning and risk mitigation, and so, the accurate retrieval of accurate emissivity for different lithologies at varying temperatures is essential.…”

Protocolo metodológico para obtenção dos valores de radiância, emissividade e temperatura de superfície de imagens Landsat 8 utilizando LEGAL

“…A emissividade de um determinado objeto representa a relação entre a radiância real emitida pelo corpo do mundo real e pelo corpo negro à mesma temperatura. Além disso, a emissividade é função de uma série de fatores, tais como cor, composição química, rugosidade da superfície, umidade e tantos outros (Meneses e Almeida, 2012;Rogic et al, 2021).…”