The MASSIVE project team will revolutionize glacier area mapping and glacier mass balance estimation. We aim at vastly expanding the applicability of today’s glacier surface mass balance regression techniques using deep learning for image classification and regression. First we will harvest unprecedented amounts of freely available remote sensing data to build data cubes of a variety of glacierized regions in the world. These databases will contain information on relevant mass balance predictors including snow cover area, glacier facies, albedo and glacier elevation changes. In this big data approach we will use state-of-the-art database software necessary to efficiently handle the ever increasing amount of remote sensing data. To achieve the highest possible quality for the predictors we will design novel glacier facies and snow cover classification algorithms based on deep learning. Our method using convolutional networks will be superior compared to today’s commonly used classification based on band ratio and indices as they can be trained with multi-resolution and multi-sensor data (including optical and synthetic aperture radar data) in a single classification framework. Output of the classification will be an updated multi-temporal glacier inventory. Once the regional data cubes are build we will use advanced regression techniques e.g. deep regression to extract a consistent and decade-long time series of surface mass balance for a variety of different glacier types. We will first design the methodology for glaciers in Norway, Svalbard and European Alps and other regions with different glacier characteristics and with solid training and validation data for the period 2000-2020. We will then test the transferability to glacierized regions with less base data available. Based on the regression parameters we also aim at prolonging the mass balance time series in a sensor-independent approach solely from snow cover and albedo maps.
Project leader: Thomas Schellenberger
Institution: Institutt for geofag