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Hans-Peter Marshall

Publications and source records attributed to Hans-Peter Marshall.

5 recordsLinked to original sources
USGS2026

Evaluating snow depth measurements from ground-penetrating radar and airborne lidar in boreal forest and tundra environments during the NASA SnowEx 2023 campaign

Snow is a vital component of high-latitude terrestrial systems, but environmental factors (e.g., permafrost) and complex vegetation challenge the accurate measurement of key snowpack properties. We evaluated local-scale ground-penetrating radar (GPR) and large-scale airborne lidar retrievals of snow depth collected during the NASA SnowEx 2023 campaign in tundra and boreal forest environments in Alaska along 44 short (3–12 m) transects. Compared to in situ observations, we identified modest biases for GPR snow depths (bias <0.03 m in tundra, +0.06 m in boreal forests) and larger biases for lidar snow depths in the boreal forests (–0.16 m). At the Upper Kuparuk-Toolik tundra site, lidar snow depths exhibited a small bias (–0.02 m), whereas the bias was much larger at the Arctic Coastal Plain tundra site (+0.19 m). For most sites, biases were primarily related to sub-snow vegetation, tussocks, and seasonally dynamic ground. However, we identified vertical alignment issues with the Arctic Coastal Plain lidar snow depth dataset that likely contributed to the higher bias. The complex ground surface and sub-snow vegetation in these environments present a challenge to established snow depth measurement methods, which needs to be considered when evaluating novel remote sensing approaches.

Alaska↗
USGS2025

Sentinel-1 SAR estimates of snowmelt onset coincide with SNOTEL soil moisture pulses across the western U.S.

While there have been recent advancements in synthetic aperture radar (SAR)-based snow water equivalent (SWE) retrievals, obtaining accurate estimates of SWE requires knowledge of the amount of liquid water content (LWC) in the snowpack given its strong impact on radar velocity. Recent studies have utilised Sentinel-1 SAR to identify snowmelt runoff onset in complex, high-elevation terrain based on a seasonal minimum backscatter time-series; however, detailed investigations into the snowpack state before and after snowmelt runoff onset are lacking. In this study, we integrated repeat field measurements at five sites, SNOw TELemetry (SNOTEL) station data ( n = 260) from across the Western United States, and paired Sentinel-1 SAR estimates of snowmelt runoff onset to (1) assess the snowpack state prior to and after Sentinel-1 SAR-derived runoff onset estimates, and (2) evaluate Sentinel-1 SAR estimates of runoff onset with SNOTEL-derived estimates of melt output via soil moisture ‘pulses’. We found that on the date of minimum backscatter, the snowpack was isothermal at three of the five field sites, and snow pit-measured LWC was increasing at all field sites relative to previous survey dates. SNOTEL soil moisture pulses preceded Sentinel-1 SAR estimates of snowmelt runoff onset by a median of 3 days (standard deviation = ±25.3 days) and post-dated peak SWE by a median of 3 days (standard deviation = ±18.2 days). Snow density and the number of positive degree days on soil moisture pulse date increased with latitude and longitude and decreased with elevation. Although satellite-based estimates of snowmelt runoff onset provide a promising approach for improving spaceborne retrievals of SWE, local climatological conditions exert significant influence on meltwater runoff onset signal clarity for both in situ and satellite-based estimates.

western United States↗
USGS2025

Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning

Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.

The Cryosphere↗
USGS2023

Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times ( twt ) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m −3 ). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Colorado↗