A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.

Global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. In Fennoscandia, as well as Northern Europe, the advances of phenological events in spring have been recorded in recent decades. In this study, we analyzed the start of the growing season within five different vegetation regions in Fennoscandia using the 30-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset. We applied a previously developed pixel-specific Normalized Difference Vegetation Index (NDVI) threshold method, adjusted it to the NDVI3g data and analyzed trends within the different regions. Results show a warming trend with an earlier start of the growing season of 11.8 ± 2.0 days (p < 0.01) for the whole area. However, there are large regional differences, and the warming/trend towards an earlier start of the growing season is most significant in the southern regions (19.3 ± 4.7 days, p < 0.01 in the southern oceanic region), while the start was stable or modest earlier (two to four days; not significant) in the northern regions. To look for temporal variations in the trends, we divided the 30-year period into three separate decadal time periods. Results show significantly more change/trend towards an earlier start of the growing season in the first period compared to the two last. In the second and third period, the trend towards an earlier start of the growing season slowed down, and in two of the regions, the trend towards an earlier start of the growing season was even reversed during the last decade.

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.

Arctic landscapes occupy a nexus of environmental change processes, globally significant soil carbon stores, wildlife populations, and subsistence-based human societies. In response to rapid climate warming, tundra ecosystems are experiencing widespread changes to vegetation and underlying permafrost, coupled with an array of ecological disturbances that are expected to intensify in the future. Declines in the extent of the cryosphere on land (permafrost and seasonal snow) and offshore (sea-ice) raise the question of whether and for how long warmer portions of the Low Arctic will fit established concepts of “what is Arctic,” given the influence the cryosphere has historically had on tundra ecosystem structure and function. The era of spaceborne observation of circumpolar tundra greenness, in the form of the Normalized Difference Vegetation Index (NDVI), has entered its fifth decade and provides foundational information concerning ecosystem conditions and responses to climatic trends, variability, ecological disturbance, and successional processes. Here we review the evolving story of Arctic greening, and synthesize long-term spaceborne records of NDVI, climatic data, field observations, and the knowledge base of Arctic residents to place the last four decades of Arctic environmental change in context, and establish expectations and research priorities for the coming decade. Greenness dynamics display high spatio-temporal variability, reflecting complex interactions of climatic warming and variability, landscape history, ecological disturbance, and other factors. Nonetheless, long-term increases in NDVI—commonly known as “the greening of the Arctic”—remain prominent across large areas in all available long-term spaceborne datasets and align with long-term shifts in vegetation structure documented in disparate Arctic regions. Common shifts reported from the Low Arctic, such as shrubification, generally portend declines in floristic diversity, and shifts in fauna that favor boreal forest species. Despite lingering uncertainties regarding trend attribution and sources of interannual variability, the sequence of record-high circumpolar tundra greenness values observed since 2020 provides strong evidence that Arctic tundra ecosystems have entered a state without historic precedent on timescales approaching a millennium.