Uncertainty of ICESat-2 ATL06- and ATL08-Derived Snow Depths for Glacierized and Vegetated Mountain Regions

Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho\u27s Owyhee Mountains and Wolverine Glacier in southcentral Alaska\u27s Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes \u3c 5° to \u3e 1 m for slopes \u3e 20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt

Using Remotely Sensed Data to Track Sít’ Kusá (Turner Glacier) Surge Movement

Although you cannot easily tell that a glacier is moving when you see one in nature, glaciers are capable of moving at speeds of up to tens of meters per day. Some glaciers switch between periods of slow and rapid flow at regular intervals, known as surges. A glacier surge is a relatively short time period over which a glacier moves at rates 10-100 times more than what is normal for that glacier. While it is known that the processes at base of the glacier play a significant role in surging, there are still many unknowns regarding the exact triggers of surges. In an effort to better understand controls on surges, our research group has been investigating the most recent surge (2019-2021) of Turner Glacier in southeast Alaska. We have been mapping changes in glacier speed by tracking distinct features on the glacier surface that are visible in Landsat 8 and Sentinel-2 images with NASA’s autoRIFT algorithm. These images have 15-meter and 10-meter resolution (square meters per pixel), respectively, and are collected on weekly timescales. We found that the glacier is often obscured by clouds, hindering our analysis. My research focuses on pioneering the use of daily 3 meter-resolution images from PlanetScope to create velocity maps of the Turner Glacier with more detailed coverage in space and time. Here, I present the record of Turner Glacier’s surface velocities from all usable PlanetScope image pairs in 2019, prior to the start of the surge. In the future, I plan to expand the analysis to the entire surge period. Additionally, I will continue to refine this technique and apply it to other glaciers in the St. Elias Mountain Range in order to compare glacier surge timing and propagation across multiple glaciers that surged over similar time periods

Improved records of glacier flow instabilities using customized NASA autoRIFT (CautoRIFT) applied to PlanetScope imagery

<jats:p>Abstract. En masse application of feature tracking algorithms to satellite image pairs has produced records of glacier surface velocities with global coverage, revolutionizing the understanding of global glacier change. However, glacier velocity records are sometimes incomplete due to gaps in the cloud-free satellite image record (for optical images) and failure of standard feature tracking parameters, e.g., search range, chip size, or estimated displacement, to capture rapid changes in glacier velocity. Here, we present a pipeline for pre-processing commercial high-resolution daily PlanetScope surface reflectance images and for generating georeferenced glacier velocity maps using NASA's autonomous Repeat Image Feature Tracking (autoRIFT) algorithm with customized parameters. We compare our velocity time series to the NASA Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) global glacier velocity dataset, which is produced using autoRIFT, with regional-scale feature tracking parameters. Using five surge-type glaciers as test sites, we demonstrate that the use of customized feature tracking parameters for each glacier improves upon the velocity record provided by ITS_LIVE during periods of rapid glacier acceleration (i.e., changes greater than several meters per day over 2–3 months). We show that ITS_LIVE can fail to capture velocities during glacier surges but that both the use of custom autoRIFT parameters and the inclusion of PlanetScope imagery can capture the progression of order-of-magnitude changes in flow speed with median uncertainties of <0.5 m d−1. Additionally, the PlanetScope image record approximately doubles the amount of optical cloud-free imagery available for each glacier and the number of velocity maps produced outside of the months affected by darkness (i.e., polar night), augmenting the ITS_LIVE record. We demonstrate that these pipelines provide additional insights into speedup behavior for the test glaciers and recommend that they are used for studies that aim to capture glacier velocity change at sub-monthly timescales and with greater spatial detail. </jats:p&gt