To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (G_DN − R_DN)/(G_DN + R_DN) where G_DN is Green digital number and R_DN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.

The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, the study area was the central part of Spitsbergen, Svalbard, located between 77.28°N and 78.44°N. The goals of this study were: (1) to prepare, analyze and present a cloud-free time-series of daily Sentinel-2 NDVI datasets for the 2016 to 2019 seasons, and (2) to demonstrate the use of the dataset in mapping the onset of growth. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud-free Sentinel-2 data were used. The onset of growth was then mapped by a NDVI threshold method, which showed significant correlation (r² = 0.47, n = 38, p < 0.0001) with ground-based phenocam observation of the onset of growth in seven vegetation types. However, large bias was found between the Sentinel-2 NDVI-based mapped onset of growth and the phenocam-based onset of growth in a moss tundra, which indicates that the data in these vegetation types must be interpreted with care. In 2018, the onset of growth was about 10 days earlier compared to 2017.

Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, and decrease the environmental footprint of science. SIOS maintains and facilitates various scientific activities such as the State of the Environmental Science in Svalbard (SESS) report, international access to research infrastructure in Svalbard, Earth observation and remote sensing services, training courses for the Arctic science community, and open access to data. This perspective paper highlights the activities of SIOS Knowledge Centre, the central hub of SIOS, and the SIOS Remote Sensing Working Group (RSWG) in response to the unprecedented situation imposed by the global pandemic coronavirus (SARS-CoV-2) disease 2019 (COVID-19). The pandemic has affected Svalbard research in several ways. When Norway declared a nationwide lockdown to decrease the rate of spread of the COVID-19 in the community, even more strict measures were taken to protect the Svalbard community from the potential spread of the disease. Due to the lockdown, travel restrictions, and quarantine regulations declared by many nations, most physical meetings, training courses, conferences, and workshops worldwide were cancelled by the first week of March 2020. The resumption of physical scientific meetings is still uncertain in the foreseeable future. Additionally, field campaigns to polar regions, including Svalbard, were and remain severely affected. In response to this changing situation, SIOS initiated several operational activities suitable to mitigate the new challenges resulting from the pandemic. This article provides an extensive overview of SIOS’s Earth observation (EO), remote sensing (RS) and other operational activities strengthened and developed in response to COVID-19 to support the Svalbard scientific community in times of cancelled/postponed field campaigns in Svalbard. These include (1) an initiative to patch up field data (in situ) with RS observations, (2) a logistics sharing notice board for effective coordinating field activities in the pandemic times, (3) a monthly webinar series and panel discussion on EO talks, (4) an online conference on EO and RS, (5) the SIOS’s special issue in the Remote Sensing (MDPI) journal, (6) the conversion of a terrestrial remote sensing training course into an online edition, and (7) the announcement of opportunity (AO) in airborne remote sensing for filling the data gaps using aerial imagery and hyperspectral data. As SIOS is a consortium of 24 research institutions from 9 nations, this paper also presents an extensive overview of the activities from a few research institutes in pandemic times and highlights our upcoming activities for the next year 2021. Finally, we provide a critical perspective on our overall response, possible broader impacts, relevance to other observing systems, and future directions. We hope that our practical services, experiences, and activities implemented in these difficult times will motivate other similar monitoring programs and observing systems when responding to future challenging situations. With a broad scientific audience in mind, we present our perspective paper on activities in Svalbard as a case study.

Habitat suitability and species distribution models have both become essential tools in biodiversity conservation and management. However, very few of these studies exist from Arctic habitats and hardly any on Arctic species diversity modelling. The basic goal of this study was to develop a statistical model based on vascular plant species’ spatial distribution data on the Svalbard archipelago and their dependence on a set of available environmental variables. The obtained model was then implemented into GIS, enabling us to calculate plant diversity indices for the Svalbard archipelago. Svalbard is easily accessible for research and contains well-known flora with plentiful ancillary data layers available. This location thus constitutes a suitable study area for analysing and modelling biodiversity. Georeferenced data on vascular plant species diversity were gathered from 184 study sites widely distributed on the archipelago. Thirteen environmental raster layers were generated based on a digital elevation model, a geological map, as well as climatic and remote sensing data. Environmental data were extracted from the raster layers at each of the 184 field study plots. Both field study plots and raster layers were studied at 1 km² resolution. Analysis using forward stepwise multiple regression revealed that growth season temperature sum (GDD), mean July precipitation (PREC) and the vegetation indices ‘normalised deviation vegetation index’ (NDVI) are the best predictors of Svalbard's vascular plant biodiversity. Despite a 48% precision of the statistical model in predicting Shannon diversity index (SDI), the output map seems to reflect well the expected distribution based on knowledge of the influence of the environmental variables considered. All variables in the model, and most other data tested in the model, are easily available and with global coverage.

Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) backscatter to vegetation growth and soil moisture in an arctic environment. A combined approach using time series data from active remote sensing sensors such as SAR and passive optical sensors is a known technique in snow monitoring, while there is little knowledge of the radar C-band’s response pattern to vegetation dynamics in the arctic. First, we created multi-sensor masks using the HV backscatter coefficients from Sentinel-1 and the Normalized Difference Snow Index (NDSI) time series from Sentinel-2, monitoring the snow dynamics in Adventdalen (Svalbard) for the season from 2017 to 2018. Second, radar sensitivity analysis was performed using the HV polarized channel responses to vegetation growth and soil moisture dynamics. (1) Our results showed that the C-band radar data are capable of monitoring the seasonal variability in timing of snow melting in Adventdalen, revealing an earlier start by approximately 20 days in 2018 compared to 2017. (2) From the sensitivity analyses, the HV channel showed a major response to the vegetation component in areas with drier graminoid dominated vegetation without water-saturated soil (R = 0.69). However, the temperature was strongly correlated with the HV channel (R = 0.74) during the years with delayed snow melting. Areas of frozen tundra with drier vegetation dominated by graminoids had delayed soil thawing processes and therefore this may limit the ability of the radar to follow the vegetation growth pattern and soil moisture.