High-resolution carbon flux upscaling in Arctic landscapes based on the example of Trail Valley Creek, Canada

Arctic regions play a critical role in the global carbon cycle, acting as both a sink and a source of carbon. However, it remains challenging to estimate methane (CH₄) and carbon dioxide (CO₂) fluxes across Arctic landscapes due to the sparsity of measurements and the complex interactions between environmental factors. Upscaling fluxes from local measurements to broader landscapes is challenging, especially in capturing the variability of land cover types and their unique carbon dynamics. Addressing this heterogeneity is critical to improving flux estimates and reducing uncertainties in Arctic carbon budgets.

Our study domain (~6 km²), the Trail Valley Creek area (Northwest Territories, Canada) illustrates this challenge, featuring a mosaic of upland, shrub, and lichen tundras alongside heterogeneous wetlands, each with distinct moisture regimes and carbon flux contributions. Our study integrates diverse datasets to upscale carbon fluxes with statistical and machine learning models at high spatial resolution (10 m), ensuring that small-scale variations are preserved. We combine chamber measurements of CH₄ and CO₂ fluxes from 39 sites, with different temporal resolutions ranging from high-frequency half-hourly data to a few measurements per day, spanning the entire vegetation season, with soil temperature (from topsoil to 30 cm depth)  and soil moisture data (at different depth down to 30 cm depth), remote sensing products such as Sentinel-2 imagery, UAV-derived vegetation height and classifications (1 m resolution), and DEM/DSM (10 cm resolution). Based on these remote sensing products we calculated vegetation and moisture indices (NDVI, NDWI, NDMI, TWI), which provide insight into seasonal variability, and the snow index (NDSI) highlights the timing of snowmelt and its influence on fluxes. This approach allows us to examine both the spatial heterogeneity of fluxes across different land cover types and their temporal dynamics in response to climate-driven changes in soil and vegetation conditions.