Soil temperature has been recognized as a property that strongly influences a myriad of hydro-biogeochemical processes and reflects how various properties modulate the soil thermal flux. In spite of its importance, our ability to acquire soil temperature data with high spatial and temporal resolution and coverage is limited because of the high cost of equipment, the difficulties of deployment, and the complexities of data management. Here we propose a new strategy that we call distributed temperature profiling (DTP) for improving the characterization and monitoring near-surface thermal properties through the use of an unprecedented number of laterally and vertically distributed temperature measurements. We developed a prototype DTP system, which consists of inexpensive, low-impact, low-power, and vertically resolved temperature probes that independently and autonomously record soil temperature. The DTP system concept was tested by moving sequentially the system across the landscape to identify near-surface permafrost distribution in a discontinuous permafrost environment near Nome, Alaska, during the summertime. Results show that the DTP system enabled successful acquisition of vertically resolved profiles of summer soil temperature over the top 0.8 m at numerous locations. DTP also enabled high-resolution identification and lateral delineation of near-surface permafrost locations from surrounding zones with no permafrost or deep permafrost table locations overlain by a perennially thawed layer. The DTP strategy overcomes some of the limitations associated with – and complements the strengths of – borehole-based soil temperature sensing as well as fiber-optic distributed temperature sensing (FO-DTS) approaches. Combining DTP data with co-located topographic and vegetation maps obtained using unmanned aerial vehicle (UAV) and electrical resistivity tomography (ERT) data allowed us to identify correspondences between surface and subsurface property distribution and in particular between topography, vegetation, shallow soil properties, and near-surface permafrost. Finally, the results highlight the considerable value of the newly developed DTP strategy for investigating the significant variability in and complexity of subsurface thermal and hydrological regimes in discontinuous permafrost regions.

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-to-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring.

The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes in areas of ice-rich permafrost. These lakes are hotspots of greenhouse gas emissions (CO₂ and CH₄), but with substantial spatial and temporal heterogeneity across Arctic and sub-Arctic regions. In Central Yakutia (Eastern Siberia, Russia), nearly half of the landscape has been affected by thermokarst processes since the early Holocene, resulting in the formation of more than 10,000 partly drained lake depressions (alas lakes). It is not yet clear how recent changes in temperature and precipitation will affect existing lakes and the formation of new thermokarst lakes. A multi-decadal remote sensing analysis of lake formation and development was conducted for two large study areas (~1200 km² each) in Central Yakutia. Mask Region-Based Convolutional Neural Networks (R-CNN) instance segmentation was used to semi-automate lake detection in Satellite pour l’Observation de la Terre (SPOT) and declassified US military (CORONA) images (1967–2019). Using these techniques, we quantified changes in lake surface area for three different lake types (unconnected alas lake, connected alas lake, and recent thermokarst lake) since the 1960s. Our results indicate that unconnected alas lakes are the dominant lake type, both in the number of lakes and total surface area coverage. Unconnected alas lakes appear to be more susceptible to changes in precipitation compared to the other two lake types. The majority of recent thermokarst lakes form within 1 km of observable human disturbance and their surface area is directly related to air temperature increases. These results suggest that climate change and human disturbances are having a strong impact on the landscape and hydrology of Central Yakutia. This will likely affect regional and global carbon cycles, with implications for positive feedback scenarios in a continued climate warming situation.

Monitoring active layer dynamics is critical for improving the understanding of near-surface thermal and hydrological processes in the cryosphere. This study presents the laboratory test of a low-cost ground-penetrating radar (GPR) system within a laboratory experiment of active layer freezing and thawing monitoring. The system is an in-house-built low-power monostatic GPR antenna coupled with a reflectometer piloted by a single-board computer (SBC) and was tested prior to field deployment. The correspondence between the frozen front electromagnetic (EM) reflection and temperature allowed us to test the ability of the system to closely monitor the frozen front and bottom of the active layer reflection.

The detection of water leakage along its transportation network has important societal impacts, such as avoiding a large volume of water wasted along the waterways or preventing water-related chemical or physical surrounding media deterioration. Among the vast domain of destructive techniques, Ground-Penetrating Radar (GPR) is a common and efficient tool used for detection in many near-surface contexts, and it is particularly efficient in civil engineering cases, such as utility detection, due to its fine resolution and the ease of data acquisition. A peculiar form of signal enhancement appears in GPR profiles recorded over spheres and cylinders where velocity contrasts exist between the body’s material and the surrounding medium. We used this enhancement to detect potential water leakages in water pipes. After exhibiting the signal enhancement effect in a laboratory sandbox experiment using a spherical glass ball, we verified the results with numerical experiments with varied sphere and cylinder sizes and dielectric properties. We then investigated field and numerical experiments of GPR transects above a “real life” water-leaking PVC pipe. Our results show that the water cylinder and water infiltration bulb produced a characteristic signal that could be used for detecting water leakages along water pipes. The largest amplitude in the GPR signal is caused by a bottom pipe reflection enhanced by the water bulb and not by the top of the pipe. We stress the risk of miscalculating the pipe’s depth during velocity estimation when amplitude enhancement conditions are met. Beyond civil-engineering impacts, knowledge on signal amplification phenomena can help GPR data interpretations in sedimentology and hydrogeology studies.