A 20-Year MODIS-Based Snow Cover Dataset for Svalbard and Its Link to Phenological Timing and Sea Ice Variability

Abstract

The climate in Svalbard has been warming dramatically compared with the global average for the last few decades. Seasonal snow cover, which is sensitive to temperature and precipitation changes, is therefore expected to undergo both spatial and temporal changes in response to the changing climate in Svalbard. This will in turn have implications for timing of terrestrial productivity, which is closely linked to the disappearance of seasonal snow. We have produced a 20-year snow cover fraction time series for the Svalbard archipelago, derived from MODIS (Moderate Resolution Imaging Spectroradiometer) Terra data to map and identify changes in the timing of the first snow-free day (FSFD) for the period 2000–2019. Moreover, we investigate the influence of sea ice concentration (SIC) variations on FSFD and how FSFD is related to the start of the phenological growing season in Svalbard. Our results revealed clear patterns of earlier FSFD in the southern and central parts of the archipelago, while the northernmost parts exhibit little change or trend toward later FSFD, resulting in weaker trends in summer and winter duration. We found that FSFD preceded the onset of the phenological growing season with an average difference of 12.4 days for the entire archipelago, but with large regional variations that are indicative of temperature dependence. Lastly, we found a significant correlation between variations of time-integrated SIC and variations in FSFD, which maximizes when correlating SIC northeast of Svalbard with FSFD averaged over Nordaustlandet. Prolonged sea ice cover in the spring was correlated with late snow disappearance, while lower-than-average sea ice cover correlated with early snow disappearance, indicating that proximity to sea ice plays an important role in regulating the timing of snow disappearance on land through influencing the regional air temperature and therefore rate of spring snowmelt.

SIOS’s Earth Observation (EO), Remote Sensing (RS), and Operational Activities in Response to COVID-19

“…The SIOS special issue which was launched in the beginning of the March-2020 proved to be an effective platform for researchers to publish their research. At the time of writing, we have 3 submissions [29,30] in this special issue. The top-five presentations by early career researchers from the SIOS online conference have been selected and the authors have been invited to submit full manuscripts to the special issue.…”

Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data

“…According to a report by the National Snow and Ice Data Center (NSIDC), the Arctic sea ice extent reached its second lowest value in history on September 2020, behind only September 2012 [2]. Sea ice plays an important role in the study of global climate change [3][4][5] and biodiversity [6]. Sea ice monitoring will help us to better understand the impact of climate change on species' habitats.…”

A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

“…The Svalbard 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, the spatiotemporal characteristics of seasonal snow cover on Svalbard have undergone significant changes in the past two decades, with large parts of the archipelago exhibiting trends of earlier spring snowmelt and disappearance [3,4]. Projected changes in climate, as outlined in a recent report on the future climate of Svalbard, indicate that by 2100, increases of 3-4 • C and 6-8 • C in the mean annual temperature can be expected for the west coast and northeastern regions respectively, compared with the 1961-1990 average [5].…”

“…Several generations of optical sensors have now been acquiring data globally for several decades; the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites has been acquiring optical images since 2000, from which the Normalized Difference Snow Index (NDSI) can be derived [14] as well as fractional snow cover [15]. Recently, a 20-year MODIS snow cover fraction (SCF) dataset for Svalbard based on the NASA MOD10A1-product [16] at 500 m spatial resolution has been produced and investigated [4]. Other spaceborne optical sensors include the Advanced Very High-Resolution Radiometer (AVHRR) instrument, which has flown onboard polar orbiting satellites since the late 1970s and provides observations for monitoring snow cover extent (SCE).…”

“…Therefore, the objective of this study is to demonstrate the similarities and differences of snow cover observations made using remote sensing datasets and snow models, and how these differences can affect the extraction of derived parameters linked to the dynamical processes such as snow melt and disappearance. We examine snow cover fraction derived using the AVHRR dataset at 4 km resolution and Sentinel-2 observations produced at 20 m resolution and how they compare with the recently published MODIS SCF dataset for Svalbard at 500 m resolution [4]. In addition, two independent snow models developed by the University of Uppsala (Energy balancesnow and firn model; EBFM) and the Norwegian Water Resources and Energy Directorate (seNorge) that provide estimates of SWE and fractional snow-covered area, are used to derive snow cover extent maps.…”