Dynamic Arctic wetland mapping: A multi-mission satellite time series approach

Arctic-boreal wetlands play a central role in global carbon cycles, biodiversity, and hydrological regulation, yet their extent and dynamics remain poorly characterized. Static wetland maps often fail to capture short-term fluctuations in inundation caused by snowmelt, rainfall, river flooding, and permafrost degradation, contributing to an underestimation of wetland area and associated greenhouse gas emissions. This study evaluates a multi-sensor framework under two scenarios: (I) a typical static scenario based on multi-sensor median summer composites from 2021 to 2025, and (II) a dynamic scenario that integrates Sentinel-1 time-series backscatter with Sentinel-2 vegetation indices to represent seasonal and interannual inundation behavior. Results for two 500 × 500 km2 study areas indicate substantial improvements under the dynamic scenario. Overall accuracy reached 95.2% in the Northwest Territories, Canada, and 96.1% in northern Sweden, compared with 91% and 92% for the static products. Gains are most evident for seasonally flooded and vegetated classes, with User's and Producer's accuracies increasing by 3–7% relative to the static scenario. The dynamic scenario also identified that 14–18% of wetland area exhibits seasonal or episodic inundation, information that static methods fail to reveal. In addition to higher accuracy, the dynamic framework provides ecologically meaningful outputs such as maximum inundation extent, inundation frequency, and seasonal hydroperiod maps, offering direct value for methane modelling, biodiversity assessment, and water resource management.