Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces

Abstract

Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over the full range of eruption temperatures and possible volcanic products. Here, four TIR datasets acquired over active lava surfaces are compared to quantify the uncertainty, accuracy, and variability in derived surface radiance, emissivity, and kinetic temperature. These data were acquired at Kīlauea volcano in Hawai’i, USA, in January/February 2017 and 2018. The analysis reveals that spatial resolution strongly limits the accuracy of the derived surface thermal properties, resulting in values that are significantly below the expected values for molten basaltic lava at its liquidus temperature. The surface radiance is ~2400% underestimated in the orbital data compared to only ~200% in ground-based data. As a result, the surface emissivity is overestimated and the kinetic temperature is underestimated by at least 30% and 200% in the airborne and orbital datasets, respectively. A thermal mixed pixel separation analysis is conducted to extract only the molten fraction within each pixel in an attempt to mitigate this complicating factor. This improved the orbital and airborne surface radiance values to within 15% of the expected values and the derived emissivity and kinetic temperature within 8% and 12%, respectively. It is, therefore, possible to use moderate spatial resolution TIR data to derive accurate and reliable emissivity and kinetic temperatures of a molten lava surface that are comparable to the higher resolution data from airborne and ground-based instruments. This approach, resulting in more accurate kinetic temperature and emissivity of the active surfaces, can improve estimates of flow hazards by greatly improving lava flow propagation models that rely on these data.

Spectral Emissivity of Phonolite Lava at High Temperature

“…Remote sensing methods based on measuring the infrared (IR) radiance from active lava bodies are widely applied, using spaceborne, airborne or ground-based platforms [2]. However, they are subject to assumptions and caveats that can propagate large uncertainties (±100 K) related to the surrounding environment [3], [4], large temperature gradients of cooling lavas as well as constant changes in their composition and texture during emplacement. Despite that reducing the uncertainties of environmental and thermal gradients when measuring emissivity is ultimately challenging, this study is aimed at minimizing the uncertainty of one of the critical, yet poorly known, parameters namely spectral emissivity (ελ).…”

“…This parameter is crucial to invert remotely-sensed spectral radiance observations of lava at high T, including in the molten state. Only a few laboratory studies have been carried out on this topic, highlighting the technical difficulties for obtaining accurate emissivity data, especially at high temperature [4]- [14].…”

“…Much of this work has been motivated by interest in structural modifications affecting the probed material (phase transitions, melting), the degree of polymerization of network (model Qn of silicates) [16]- [18] or chemical composition (e.g., Si content, nonbridging oxygens per tetrahedrally-coordinated cations, i.e., NBO/T). For remotely sensed temperature estimates made in the TIR, a number of studies have recommended characterization of emissivity across a wider spectral region [4], [12], [13]. Spectral emissivity at high temperatures is also pertinent for modelling the heat exchange between gas and melt in ascending magmas [19].…”

The influence of variable emissivity on lava flow propagation modeling

“…where is the Stefan-Boltzmann constant, h c is the heat transfer coefficient, k is thermal conductivity, is thermal diffusivity, and A is pixel area. The heat transfer coefficient is calculated for a forced convection scenario because windy conditions were present during the acquisition periods (Harris 2013;Thompson and Ramsey 2020a). The fraction of exposed melt on the surface of the flow is calculated based on previous work deriving sub-pixel temperatures (Dozier 1981;Rothery et al 1988;Harris 2013).…”

“…However, temperature was never considered a controlling variable. Prior emissivity measurements quantified the effects of surface roughness, state changes (melt vs. solid), and particle size (e.g., Simurda et al 2019;Williams and Ramsey 2019;Thompson and Ramsey 2020a). High temperature studies of cooling lava found that emissivity is significantly lower than previous assumed (Abtahi et al 2002;Lee et al 2013;Thompson and Ramsey 2020a,b).…”