A hidden state space modeling approach for improving glacier surface velocity estimates using remotely sensed data

Tuesday, 16 December 2014
Daniel Henke, Adrian Schubert, David Small, Erich Meier, Martin P Lüthi and Andreas Vieli, University of Zurich, Zurich, Switzerland
A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation.

GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013).

On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made.

The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the Swiss Alps (Jungfrau-Aletsch) and Greenland (Eqip Sermia). TerraSAR-X satellite images were acquired and processed over the Aletsch Glacier during the same period. For the Eqip Sermia glacier, data from the new Sentinel-1 satellite will be available soon, providing a high revisit time near the pole, increasing the expected accuracy of GSV estimates.