Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for timely spatial inventory data identifying Arctic wetlands with sufficient accuracy, resolution, and detail. Wetland mapping at large scales necessitates the processing of large volumes of Earth observation (EO) data, a challenge known as "Big Data". Consequently, in this study, we present a cloud-based methodology exploiting the remarkable collection of EO data and computational power of Google Earth Engine (GEE) to map Arctic wetlands at 10 m spatial resolution. Our workflow evaluated temporally aggregated optical and radar satellite imagery and novel hydro-physiographic layers as inputs into a robust Random Forest (RF) machine learning (ML) algorithm. Both pixel and object-based classification approaches were assessed, whereby ML models were calibrated with a training dataset of sufficient and comprehensive samples. The study was conducted over Canada's Southern Arctic ecozone (830,000 km2). GEE enabled the efficient preprocessing and classification of large volumes of EO data and resulted in excellent yet similar statistical performance for both pixel and object-based approaches, achieving overall accuracies of > 89 % and mean F1-scores of > 0.79. Moreover, McNemar tests indicated that these classifications were not statistically different, which has significant implications regarding computing time and processing efficiencies. These results demonstrate the efficacy and scalability of our cloud-based GEE methodology, and as such can support future endeavors around Pan-Arctic wetland mapping and monitoring.

Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for timely spatial inventory data identifying Arctic wetlands with sufficient accuracy, resolution, and detail. Wetland mapping at large scales necessitates the processing of large volumes of Earth observation (EO) data, a challenge known as "Big Data". Consequently, in this study, we present a cloud-based methodology exploiting the remarkable collection of EO data and computational power of Google Earth Engine (GEE) to map Arctic wetlands at 10 m spatial resolution. Our workflow evaluated temporally aggregated optical and radar satellite imagery and novel hydro-physiographic layers as inputs into a robust Random Forest (RF) machine learning (ML) algorithm. Both pixel and object-based classification approaches were assessed, whereby ML models were calibrated with a training dataset of sufficient and comprehensive samples. The study was conducted over Canada's Southern Arctic ecozone (830,000 km2). GEE enabled the efficient preprocessing and classification of large volumes of EO data and resulted in excellent yet similar statistical performance for both pixel and object-based approaches, achieving overall accuracies of > 89 % and mean F1-scores of > 0.79. Moreover, McNemar tests indicated that these classifications were not statistically different, which has significant implications regarding computing time and processing efficiencies. These results demonstrate the efficacy and scalability of our cloud-based GEE methodology, and as such can support future endeavors around Pan-Arctic wetland mapping and monitoring.

Rapid shrub expansion has been observed across the Arctic, driving a need for regional-scale estimates of shrub biomass and shrub-mediated ecosystem processes such as rainfall interception. Synthetic-Aperture Radar (SAR) data have been shown sensitive to vegetation canopy characteristics across many ecosystems, thereby potentially providing an accurate and cost-effective tool to quantify shrub canopy cover. This study evaluated the sensitivity of L-band Advanced Land Observing Satellite 2 (ALOS-2) data to the aboveground biomass and Leaf Area Index (LAI) of dwarf birch and alder in the Trail Valley Creek watershed, Northwest Territories, Canada. The σ° VH /σ° VV ratio showed strong sensitivity to both LAI (R 2 = 0.72 with respect to in-situ measurements) and wet aboveground biomass (R 2 = 0.63) of dwarf birch. Our ALOS-2-derived maps revealed high variability of birch shrub LAI and biomass across spatial scales. The LAI map was fed into the sparse Gash model to estimate shrub rainfall interception, an important but under-studied component of the Arctic water balance. Results suggest that on average across the watershed, 17 ± 3% of incoming rainfall was intercepted by dwarf birch (during summer 2018), highlighting the importance of shrub rainfall interception for the regional water balance. These findings demonstrate the unexploited potential of L-band SAR observations from satellites for quantifying the impact of shrub expansion on Arctic ecosystem processes. • L-band SAR is a skillful predictor for tundra shrub biomass and leaf area index. • High spatial variation in tundra shrub cover captured by L-band SAR. • Distributed rainfall interception by shrub mapped across the watershed. • Amount of interception closely linked to shrub leaf area index.

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.