High-resolution debris-cover mapping using UAV-derived thermal imagery: Limits and opportunities

Abstract

Debris-covered glaciers are widespread in high mountain ranges on earth. However, the dynamic evolution of debris-covered glacier surfaces is not well understood, in part due to difficulties in mapping debris-cover thickness in high spatiotemporal resolution. In this study, we present land surface temperatures (LSTs) of supraglacial debris cover and their diurnal variability measured from an unpiloted aerial vehicle (UAV) at a high (15cm) spatial resolution. We test two common approaches to derive debris-Thickness maps by (1) solving a surface energy balance model (SEBM) in conjunction with meteorological reanalysis data and (2) least squares regression of a rational curve using debris-Thickness field measurements. In addition, we take advantage of the measured diurnal temperature cycle and estimate the rate of change of heat storage within the debris cover. Both approaches resulted in debris-Thickness estimates with an RMSE of 6 to 8cm between observed and modeled debris thicknesses, depending on the time of the day. Although the rational curve approach requires in situ field measurements, the approach is less sensitive to uncertainties in LST measurements compared to the SEBM approach. However, the requirement of debris-Thickness measurements can be an inhibiting factor that supports the SEB approach. Because LST varies throughout the day, the success of a rational function to express the relationship between LST and debris thickness also varies predictably with the time of day. During the period when the debris cover is warming, LST is heavily influenced by the aspect of the terrain. As a result, clear-sky morning flights that do not consider the aspect effects can be problematic. Our sensitivity analysis of various parameters in the SEBM highlights the relevance of the effective thermal conductivity when LST is high. The residual and variable bias of UAV-derived LSTs during a flight requires calibration, which we achieve with bare-ice surfaces. The model performance would benefit from more accurate LST measurements, which are challenging to achieve with uncooled sensors in high mountain landscapes.