| Summary: | Acknowledgements: We would like to thank everyone who helped with field data collection in the Canadian Yukon during 2022 and in Greenland during 2023, including Joseph Everest, Erica Zaja, Jiri Subrt, Sian Williams and Mariana García Criado. For help with drones and sensors, particular thanks go to Tom Wade at the University of Edinburgh Airborne Research and Innovation facility, and Alex Merrington, Jack Gillespie, Craig Atkins and Robbie Ramsay at the NERC Field Spectroscopy Facility. Additional thanks to Alan Hobbs, Colin Kay and Graham Mitchell from the NERC Geophysical Equipment Facility. We thank Tim Gyger for support and consultation on our statistical methods, Gwenn Flowers for the time taken to provide climate data for Kluane and Kirsten Schmidt-Pedersen for sharing her extensive knowledge of the people, plants and animals of Qeqertarsuaq, Greenland. Funding for this research was provided by NERC through a SENSE CDT studentship (NE/T00939X/1), the NERC Tundra Time project (NE/W006448/1), a 2023 UK-Greenland Arctic Bursary, a NERC Geophysical Equipment Facility loan (1152), and a NERC Field Spectroscopy Facility loan (891.0111). Additional funding was provided by a Scottish Alliance for GeoScience, Environment and Society (SAGES) small grant scheme award to Calum Hoad. We thank Kluane First Nation and Champagne and Aishihik First Nations for their permission to work on their lands. We gratefully acknowledge the people of Kalaallit Nunaat in general, and of Qeqertarsuaq in particular, for being able to conduct this research on their land. We thank Outpost Research Station and Arctic Station for logistical support. The authors acknowledge constructive comments from two anonymous reviewers, which greatly improved the manuscript. Satellite imagery is critical for understanding land-surface change in the rapidly warming Arctic. Since the 1980s, studies have found positive trends in the normalised difference vegetation index (NDVI) derived from satellite imagery over the Arctic—commonly referred to as ...
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