Abstract. Rock glaciers are a prominent component of many alpine landscapes and constitute a significant water resource in some arid mountain environments. Here, we employ satellite-based interferometric synthetic aperture radar (InSAR) between 2016 and 2019 to identify and monitor active and transitional rock glaciers in the Uinta Mountains (Utah, USA), an area of ∼3000 km2. We used mean velocity maps to generate an inventory for the Uinta Mountains containing 205 active and transitional rock glaciers. These rock glaciers are 11.9 ha in area on average and located at a mean elevation of 3308 m, where mean annual air temperature is −0.25 ∘C. The mean downslope velocity for the inventory is 1.94 cm yr−1, but individual rock glaciers have velocities ranging from 0.35 to 6.04 cm yr−1. To search for relationships with climatic drivers, we investigated the time-dependent motion of three rock glaciers. We found that rock glacier motion has a significant seasonal component, with rates that are more than 5 times faster during the late summer compared to the rest of the year. Rock glacier velocities also appear to be correlated with the snow water equivalent of the previous winter's snowpack. Our results demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.

<p>Atmospheric errors in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscure real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, developing a technique for atmospheric correction that performs well in high-relief terrain is increasingly important. Here, we developed and implemented a statistical machine learning-based atmospheric correction that relies on the differing spatial and topographic characteristics of periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data (40 m, 12 days), does not require external atmospheric data, and can correct both stratified and turbulent atmospheric noise. Using Sentinel-1 data from 2015-2022, we trained a convolutional neural network (CNN) on atmospheric noise from 136 short-baseline interferograms and displacement signals from time-series inversion of 337 interferograms. The CNN correction was then tested on a densely connected network of 202 Sentinel-1 interferograms which were inverted to create a displacement time series. We used the Rocky Mountains in Colorado as our training, validation, and testing areas. When applied to our validation data, our correction offers a 690% improvement in performance over a global meteorological reanalysis-based correction and a 209% improvement over a high-pass filter correction. We found that our correction reveals previously hidden time-dependent kinematic behavior of three representative rock glaciers in our testing dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications.  </p>

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&lt;p&gt;Rock glaciers are common landforms in many alpine permaforst landscapes that play an important role in alpine hydrology and landscape evolution, principally through the release of seasonal meltwater and the downslope transport of coarse material. Here, we use satellite-based interferometric synthetic aperture radar (InSAR) to identify and monitor rock glaciers in the Western USA. We focus on the movement of active and transitional rock glaciers in Utah (Uinta, Wasatch, and La Sal Mountains), and Wyoming (Wind River Mountains) between 2015 and 2022. Using the new framework established by the International Permafrost Association (IPA) Action Group, we identified 255 active and transitional rock glaciers in the ~3500 km&lt;sup&gt;2&lt;/sup&gt; Uinta Mountains, 45 rock glaciers in the ~200 km&lt;sup&gt;2&lt;/sup&gt; La Sal Mountains, 55 rock glaciers in the ~135 km&lt;sup&gt;2&lt;/sup&gt; Wasatch Mountains, and 120 rock glaciers in the ~3000 km&lt;sup&gt;2&lt;/sup&gt; Wind River Mountains. These rock glaciers currently occur under different climatic regimes based on data from the 30 year (1991-2020) normal Parameter-elevation Relationships on Independent Slopes Model (PRISM). The La Sals and Wasatch are warmer and wetter with a mean annual air temperature (MAAT) of ~3.0&amp;#177; 1.9 &amp;#730;C and&amp;#160; 2.7 &amp;#177; 1.1 &amp;#730;C and a mean annual precipitation (MAP) of ~92 &amp;#177; 13 cm and ~130 &amp;#177; 17 cm, respectively, whereas the Uintas and Wind Rivers are cooler and drier with a MAAT of ~0.24 &amp;#177; 1.4 &amp;#730;C and&amp;#160; -0.87 &amp;#177; 1.4 &amp;#730;C and a MAP of ~87 &amp;#177; 11 cm and ~81 &amp;#177; 10 cm. The mean line-of-sight (LOS) velocities for individual rock glaciers range from ~1 to 10 cm/yr. We also examined the time-dependent relationship between the motion of the rock glaciers and local climatic drivers such as temperature and precipitation. We found that rock glaciers exhibit seasonal and annual velocity changes, likely driven by liquid water availability (from snowmelt and rainfall), with accelerated motion during summers and during wetter years. Our findings demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.&lt;/p&gt;

&lt;p&gt;Atmospheric errors in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscure real signals, especially in mountainous terrain. By taking advantage of the differing spatial characteristics of periglacial landforms and atmospheric noise, we trained a deep convolutional neural network (CNN) to remove atmospheric noise from individual interferograms. Unlike existing corrections, which rely on coarse climate reanalysis or radiometer data, this computer vision correction is applied at the spatial and temporal resolution of the interferogram. We processed Sentinel 1 interferograms of the Colorado Rocky Mountains using the Alaska Satellite Facility&#039;s Hybrid Pluggable Processing Pipeline (ASF HyP3) and used the Miami INsar Time-series software in PYthon (MintPy) package to generate low-noise line-of-sight (LOS) velocity maps containing primarily rock glacier and hillslope motion. These maps were combined with noisy short temporal-baseline interferograms to contrive a training dataset. Model performance was assessed using the structural similarity index measure (SSIM) and compared to that of other widely used corrections. We find that our CNN significantly outperforms standard corrections and that previously hidden intraseasonal kinematic behavior is revealed in Colorado rock glaciers. We suggest that insights from external validation against GNSS data and sensitivity analysis could be used to further improve model performance and assess model scalability and transferability.&amp;#160;&lt;/p&gt;