The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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Abstract. The Northeast Greenland Ice Stream (NEGIS) extends around 600 km upstream from the coast to its onset near the ice divide in interior Greenland. Several maps of surface velocity and topography of interior Greenland exist, but their accuracy is not well constrained by in situ observations. Here we present the results from a GPS mapping of surface velocity in an area located approximately 150 km from the ice divide near the East Greenland Ice-core Project (EastGRIP) deep-drilling site. A GPS strain net consisting of 63 poles was established and observed over the years 2015–2019. The strain net covers an area of 35 km by 40 km, including both shear margins. The ice flows with a uniform surface speed of approximately 55 m a−1 within a central flow band with longitudinal and transverse strain rates on the order of 10−4 a−1 and increasing by an order of magnitude in the shear margins. We compare the GPS results to the Arctic Digital Elevation Model and a list of satellite-derived surface velocity products in order to evaluate these products. For each velocity product, we determine the bias in and precision of the velocity compared to the GPS observations, as well as the smoothing of the velocity products needed to obtain optimal precision. The best products have a bias and a precision of ∼0.5 m a−1. We combine the GPS results with satellite-derived products and show that organized patterns in flow and topography emerge in NEGIS when the surface velocity exceeds approximately 55 m a−1 and are related to bedrock topography.

In recent years, the Sentinel-1 satellites have provided a data archive of unprecedented volume, delivering C-band Synthetic Aperture Radar (SAR) acquisitions over most of the polar ice sheets with a repeat-pass period of 6–12 days using Interferometric Wide swath (IW) imagery acquired in Terrain Observation by Progressive Scans (TOPS) mode. Due to the added complexity of TOPS-mode interferometric processing, however, Sentinel-1 ice velocity measurements currently rely exclusively on amplitude offset tracking, which generates measurements of substantially lower accuracy and spatial resolution than would be possible with Differential SAR Interferometry (DInSAR). The main difficulty associated with TOPS interferometry lies in the spatially variable azimuth phase contribution arising from along-track motion within the scene. We present a Sentinel-1 interferometric processing chain, which reduces the azimuth coupling to the line-of-sight phase signal through a spatially adaptive coregistration refinement incorporating azimuth velocity measurements. The latter are based on available ice velocity mosaics, optionally supplemented by Burst-Overlap Multi-Aperture Interferometry. The DInSAR processing chain is demonstrated for a large drainage basin in Northeast Greenland, encompassing the Northeast Greenland Ice Stream (NEGIS), and integrated with state-of-the-art offset tracking measurements. In the ice sheet interior, the combined DInSAR and offset tracking ice velocity product provides a spatial resolution of 50 × 50 m and 1-sigma accuracies of 0.18 and 0.44 m/y in the x and y components respectively, compared to GPS.

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