remote sensing Article A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study Hannah Vickers 1, * , Eirik Malnes 1 , Ward J. J. van Pelt 2 , Veijo A. Pohjola 2 , Mari Anne Killie 3 , Tuomo Saloranta 4 and Stein Rune Karlsen 1   Citation: Vickers, H.; Malnes, E.; van Pelt, W.J.J.; Pohjola, V.A.; Killie, M.A.; Saloranta, T.; Karlsen, S.R. A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study. Remote Sens. 2021, 13, 2002. https://doi.org/10.3390/ rs13102002 Academic Editor: Gareth Rees Received: 7 April 2021 Accepted: 11 May 2021 Published: 20 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 NORCE Norwegian Research Centre AS, P.O. Box 6434, NO-9294 Tromsø, Norway; eima@norceresearch.no (E.M.); skar@norceresearch.no (S.R.K.) 2 Department of Earth Sciences, Uppsala University, 75105 Uppsala, Sweden; ward.van.pelt@geo.uu.se (W.J.J.v.P.); Veijo.Pohjola@geo.uu.se (V.A.P.) 3 Norwegian Meteorological Institute, P.O. Box 43, NO-0313 Oslo, Norway; mariak@met.no 4 Hydrology Department, Norwegian Water Resources and Energy Directorate, P.O. Box 5091, NO-0301 Oslo, Norway; tus@nve.no * Correspondence: havi@norceresearch.no Abstract: Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Keywords: polar regions; snow cover; remote sensing; snow modelling; MODIS; Sentinel-2 1. Introduction Snow cover is a crucial component of the climate system, with its high albedo allowing up to 90% of incoming solar radiation to be reflected. Snow is also an important insulator, and in cold climates such as those found in the high latitude regions, it protects underlying soil and vegetation from frost damage. However, past and present changes in the global climate have been producing pronounced effects in the polar regions, as increasing temper- atures lead to loss of snow, glacier and sea ice cover which in turn reduce the surface albedo and increase absorption of solar radiation, producing even greater warming [1]. The Sval- bard archipelago, located in the High Arctic, is heavily glaciated and glaciers alone make up 57% of the total land area of Svalbard [2]. However, as a result of a warming climate, Remote Sens. 2021, 13, 2002. https://doi.org/10.3390/rs13102002 https://www.mdpi.com/journal/remotesensing