Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.〈/jats:p〉

Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change. Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta. We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta. We extracted spring snow-cover duration determined by a latitudinal gradient. The ‘regular year’ snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta. We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed. In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021. The NDVI rise since 2018 is preceded by up to 4 °C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.〈/jats:p〉