Improved mapping of Arctic fractional land cover and land cover change from multi-resolution optical remote sensing

Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budget, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 10 5 km 2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R 2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.