Abstract
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.
| Original language | English |
|---|---|
| Article number | 114717 |
| Journal | Remote Sensing of Environment |
| Volume | 323 |
| DOIs | |
| State | Published - Jun 1 2025 |
Funding
We thank Dr. Andrew Cunliffe and two anonymous reviewers for constructive and thoughtful feedback during peer review. This material is based upon work supported by the National Aeronautics and Space Administration (NASA) Early Career Investigator Program in Earth Science (Grant No. 80NSSC21K1364 to LTB), the National Science Foundation (NSF) Navigating the New Arctic Program (Grant No. 2127273 to LTB and SJG), and the NASA Arctic Boreal Vulnerability Experiment (ABoVE; Grant No. 80NSSC22K1247 to SJG and LTB). We thank Google for providing additional computing resources on Earth Engine. Further support was provided by US, Canadian, and European entities. HEG acknowledges support from the NASA Terrestrial Ecology Program (Grant No. NNX12AK83G) and NASA Earth Science Fellowship (Grant No. NNX15AP04H). HE acknowledges support from the NASA Land Cover Land Use Change Program and NSF Biocomplexity Program. MML acknowledges support from the NSF Office of Polar Programs (OPP; Grant No. 1417745). MCM acknowledges support from the NSF Division of Environmental Biology (DEB; Grant No. DEB-2224776 and DEB-1636476). MSB-H acknowledges support from NSF OPP (Grant No. 1936752) and NSF DEB (Grant No. 1556481). CMI, VGS, and JK acknowledge support from The Next Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project that is supported by the Biological and Environmental Research Program in the Department of Energy's Office of Science. Canadian entities provided support for this research. ERH acknowledges support from POLAR Knowledge Canada (POLAR) and Natural Sciences and Engineering Research Council of Canada (NSERC). NAS acknowledges support from the Northern Scientific Training Program (NSTP), International Polar Year (IPY), NSERC, ArcticNet, and Queen's University. EL acknowledges support from the Fonds de Recherche du Québec-Nature et technologies (Grant No. FRQNT-2018- PR- 208107), NSERC Discovery Program, and Natural Resources Canada Polar Continental Shelf Program (NRC PCSP). GG acknowledges support from the FRQNT, NSERC, POLAR, ArcticNet, NRC PCSP, and Faculté des sciences de l'agriculture et de l'alimentation of Université Laval. SB acknowledges support from the Québec Ministère de la Forêt, de la Faune et des Parcs. European entities provided further support for this research. AM acknowledges support from the Independent Research Fund Denmark (Grant No. 0135-00140B and 2032-00064B). MBS acknowledges support from the European Union (ILLUQ) and Swedish Research Council (Grant No. 2021-05767). A-MV acknowledges support from the Otto Malm foundation, Nordenskiöld samfundet, and Societas Pro Fauna et Flora Fennica. JM acknowledges support from the Academy of Finland, Finnish Center of Excellence Program, and EU FP7. The Academy of Finland also supported TK and MV (Grant No. 330319), HY (Grant No. 330845), and ML (Grant No. 1342890). BCF acknowledges support from the European Commission Research and Innovation (CHARTER; Grant No. 869471).
Keywords
- Climate change
- Landsat
- Pan Arctic
- Plant biomass
- Remote sensing
- Vegetation distribution
- Woody plant dominance