Mapping the land cover of a Northern Sweden watershed using Sentinel-1 & 2 data and an optimized Random Forest

Due to accelerating global warming [1], the polar regions, and in particular the Arctic, are subject to many changes and cascading effects [2]. Northern lands are facing a generalized rise in soil temperature causing changes in the surface cover [3], the hydrological and mechanical state of the subsoil including permafrost thaw [4], [5] and potentially triggering massive release of greenhouse gases [6]. As land cover is a key control parameter for permafrost state, the survey of surface changes is of great importance. Consequently, monitoring the evolution of surface boreal ecosystems over large time scales, satellite imagery combined with reliable and proven methodologies is crucial for understanding the impact of climate change on polar continental regions. In this study, we use a Random Forest algorithm to analyze satellite images from the Copernicus (ESA) Sentinel-1 and Sentinel-2 programs in combination with ground truth data collected in July 2024, to monitor changes in the surface ecosystem over a 480 km² area in the Abisko region (Arctic Sweden). Random Forest method applied to features derived from satellite images allows the production of reliable land cover maps (>87% accuracy). Our results demonstrate that radar imagery is a vital source of information for overcoming the inherent limitations of optical imagery caused by frequent and dense cloud cover, particularly in summer, when average monthly cloud cover can reach up to 85% [7]. Additionally, they highlight that combining optical and radar imageries with a robust machine learning approach enables the production of high-quality land cover maps, providing significant added value for long term and high temporal resolution monitoring of land cover changes in northern continental regions.

 

[1]           P. M. Forster et al, ‘Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence’, 2024

[2]           Intergovernmental Panel on Climate Change (IPCC), The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change, 2022

[3]           M. Wenzl et al, ‘Vegetation Changes in the Arctic: A Review of Earth Observation Applications’, 2024

[4]           E. J. Burke et al, ‘Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change’, 2020

[5]           T. Xavier et al, ‘Future permafrost degradation under climate change in a headwater catchment of central Siberia: quantitative assessment with a mechanistic modelling approach’, 2024

[6]           R. M. Varney et al, ‘Evaluation of soil carbon simulation in CMIP6 Earth system models’, 2022

[7]           J. E. Kay et al, ‘Recent Advances in Arctic Cloud and Climate Research’, 2016