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The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling 

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m⁻²) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic. 

This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile. 

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic Plant Aboveground Biomass Synthesis Dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass grams per meter squared (g/m^2) on 2327 sample plots in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic. 

Abstract Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO 2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO 2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO 2 later in the season.