High-resolution remote sensing and machine-learning-based upscaling of methane fluxes: a case study in the Western Canadian tundra

Abstract. Arctic methane (CH4) budgets are uncertain because field measurements often capture only fragments of the wet-to-dry gradient that control tundra CH4 fluxes. Wet hotspots are over-represented, while dry, net-sink sites are under-sampled. We paired over 13 000 chamber flux measurements during peak growing season in July (2019–2024) from Trail Valley Creek in the western Canadian Arctic with co-registered remotely sensed predictor variables to test how spatial resolution (1 m vs. 10 m) and choice of machine-learning algorithm shape upscaled CH4 flux maps over our 3.1 km2 study domain. Four algorithms for CH4 flux scaling (Random Forest (RF), Gradient Boosting Machine (GBM), Generalised Additive Model (GAM), and Support Vector Regression (SVR)) were tuned using the same stack of multispectral indices, terrain derivatives and a six-class landscape classification. Tree-based models such as RF and GBM offered the best balance of 10-fold cross-validated R2 (≤0.75) and errors, so RF and GBM were used in a subsequent step for upscaling to the study area. With 1 m resolution, GBM captured the full range of microtopographic extremes and predicted a mean July flux of 99 mg CH4 m−2 per month. In contrast, RF, which smoothed local extremes, yielded an average flux of 519 mg CH4 m−2 per month. The disagreement between flux estimates using GBM and RF correlated mainly with the Normalized Difference Water Index (NDWI), a moisture proxy, and was most pronounced in waterlogged, low-lying areas. Aggregating predictors to 10 m averaged the sharp metre-scale flux highs in hollows and lows on ridges, narrowing the GBM-RF difference to ∼75 mg CH4 m−2 per month while broadening the overall flux distribution with more intermediate values. At 1 m, microtopography was the main driver. At 10 m, moisture proxies explained about half of the variance. Our results demonstrate that: (i) metre predictors are indispensable for capturing the wet-dry microtopography and its CH4 signals, (ii) upscaling algorithm selection strongly controls prediction spread and uncertainty once that microrelief is resolved, and (iii) coarser grids smooth local microtopographic details, resulting in flattened CH4 flux peaks and wider distribution. At 10 m, however, flux estimates became more consistent between models and better represented broad moisture-driven patterns, suggesting improved generalisability despite some loss of detail. This is supported by findings for remote sensing derived seasonal subsidence which reflects moisture gradients. All factors combined lead to potentially large differences in scaled CH4 flux budgets, calling for a careful selection of scaling approaches, spatial predictor layers (e.g., vegetation, moisture, topography), and grid resolution. Future work should couple ultra-high-resolution imagery with temporally dynamic indices to reduce upscaling bias along Arctic wetness gradients.