11 citations as recorded by crossref.

1. Light and dark conditions control the nitrous oxide uptake and emission dynamics in a subarctic, nutrient-poor permafrost peatland N. Triches et al. https://doi.org/10.1038/s43247-026-03698-3
2. Accelerated lowland thermokarst development revealed by UAS photogrammetric surveys in the Stordalen mire, Abisko, Sweden M. Thomas et al. https://doi.org/10.5194/tc-20-5271-2026
3. Upscaling Chamber-measured Greenhouse Gas Emissions in Rewetted Peat Extraction Sites Using Drone and Satellite Imagery A. Isoaho et al. https://doi.org/10.1007/s00267-026-02582-2
4. Carbon and nitrogen stocks and distributions associated with different vegetation covers and soil profiles in Abisko, northern Sweden H. Potier et al. https://doi.org/10.1139/as-2023-0049
5. Quantifying landcover-specific fluxes over a heterogeneous landscape through coupling UAV-measured mixing ratios with a large-eddy simulation model and Eddy-covariance measurements T. Yazbeck et al. https://doi.org/10.5194/amt-18-6917-2025
6. Estimating permafrost ice content from independent frequency inversion of high-frequency IP Data: a case study from Heliport Mire, Abisko, Sweden M. Sugand et al. https://doi.org/10.1093/gji/ggag029
7. Practical guidelines for reproducible N2O flux chamber measurements in nutrient-poor ecosystems N. Triches et al. https://doi.org/10.5194/amt-18-3407-2025
8. Multitemporal UAV lidar detects seasonal heave and subsidence on palsas C. Renette et al. https://doi.org/10.5194/tc-18-5465-2024
9. Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al. https://doi.org/10.3390/rs17132202
10. High-resolution remote sensing and machine-learning-based upscaling of methane fluxes: a case study in the Western Canadian tundra K. Ivanova et al. https://doi.org/10.5194/bg-23-233-2026
11. Advancing the Arctic Methane Permafrost Challenge (AMPAC) With Future Satellite Missions A. Bartsch et al. https://doi.org/10.1109/JSTARS.2025.3538897