From field photos to microscope images: a low-cost toolbox for glacier surface darkening and biological impurity analysis

The albedo lowering of glacier and ice sheet surface is driven by a complex combination of biogeophysical processes, including the accumulation of biologically active impurities such as glacier ice algae and dispersed cryoconite. It is therefore important to understand darkening processes by ground observations and sampling. In situ sampling requires a simple yet effective approach to quantify surface ice darkness and estimate the possible range of impurity concentrations. Traditional post-processing of snow and ice samples for cell and mineral counts can also be time and resource consuming.

In this study, we present a novel toolbox for calibrating field images acquired with consumer-grade cameras (e.g., smartphones or digital cameras) to estimate surface ice darkness. Using a data-driven approach, we tested the method on snow and ice samples collected in Greenland to predict impurity ranges. The toolbox also integrates advanced AI models for automated segmentation of microscope images and classification of snow algae, glacier ice algae, and mineral particles, enabling rapid impurity quantification. The new toolbox can enable researchers across fields to cross compare fieldwork samples. It also offers potential for integration into automated weather stations for long-term monitoring programs, advancing glacier surface darkening characterization and biogeophysical research.