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.

Thaw slumps in ice‐rich permafrost can retreat tens of metres per summer, driven by the melt of subaerially exposed ground ice. However, some slumps retain an ice‐veneering debris cover as they retreat. A quantitative understanding of the thermal regime and geomorphic evolution of debris‐covered slumps in a warming climate is largely lacking. To characterize the thermal regime, we instrumented four debris‐covered slumps in the Canadian Low Arctic and developed a numerical conduction‐based model. The observed surface temperatures 20°C and steep thermal gradients indicate that debris insulates the ice by shifting the energy balance towards radiative and turbulent losses. After the model was calibrated and validated with field observations, it predicted sub‐debris ice melt to decrease four‐fold from 1.9 to 0.5 m as the thickness of the fine‐grained debris quadruples from 0.1 to 0.4 m. With warming temperatures, melt is predicted to increase most rapidly, in relative terms, for thick (~0.5‐1.0 m) debris covers. The morphology and evolution of the debris‐covered slumps were characterized using field and remote sensing observations, which revealed differences in association with morphology and debris composition. Two low‐angle slumps retreated continually despite their persistent fine‐grained debris covers. The observed elevation losses decreased from ~1.0 m/yr where debris thickness ~.2 m to 0.1 m/yr where thickness ~1.0 m. Conversely, a steep slump with a coarse‐grained debris veneer underwent short‐lived bursts of retreat, hinting at a complex interplay of positive and negative feedback processes. The insulative protection and behaviour of debris vary significantly with factors such as thickness, grain size and climate: debris thus exerts a fundamental, spatially variable influence on slump trajectories in a warming climate.

Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.

In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote-sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies >0.7). The randomness limits the applicability of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is more surfacelike, also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture observed at our site. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (~2 dB). However, contrary to polarimetric indicators, it shows a clear surface soil moisture signal. To account for the small dynamic range while retaining a 100-m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3–0.5 with in situ data at 5 cm depth and, upon calibration, root-mean-square errors of <0.04 m3m−3. As this approach is applicable to Sentinel-1 data, it can potentially provide frequent soil moisture estimates across large regions. In the long term, L-band data hold greater promise for operational retrievals.

Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.