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FS 3.167

Advancements in monitoring snow and glaciers in mountain regions using satellite

Details

Full Title
FS 3.167: Advancements in monitoring snow and glaciers in mountain regions using satellite data
Scheduled
TBA
Location
TBA
Convener
Thomas Nagler
Co-Conveners
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Assigned to Synthesis Workshop
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Thematic Focus
Cryo- & Hydrosphere, Low-to-no-snow, Monitoring, Remote Sensing, Water Resources
Keywords
Snow, Glacier, Satellite, Remote Sensing, Water Resources

Description

Quantitative observations of physical properties of the seasonal snowpack and glaciers are of great importance for water resources, climate impact and natural hazard monitoring activities. Satellite-based observation systems are the only efficient means for obtaining the required high temporal and spatial coverage on regional to global scale. In the last decades significant advancements have been achieved in retrieval of snow and glacier parameters from optical and SAR satellite data and assimilating these products into models. Optical sensors provide information on snow extent and snow albedo. Due to the sensitivity to dielectric properties and penetration capabilities, SAR systems are versatile tools for snow parameter observations such as snow melt and mass. Additionally, lidar backscatter measurements have been proven to provide accurate observations on snow height. However, there is still need for improvement of snowpack and glacier products, addressing snow depth, snow water equivalent, liquid water content, freezing state and snow morphology and accounting for topography and land surface heterogeneity. In this session the status of current products on snow and glacier parameters in mountain regions are reviewed and activities towards further improvements will be presented, taking into account satellite data of current and future satellite missions. In this context a wide range of retrieval techniques are of interest as well as assimilation techniques of satellite snow and ice products into models.

Submitted Abstracts

ID: 3.9119

Advancing satellite photogrammetric mapping of snow depth in high alpine terrain: a comparative study between Pléiades PHR and Neo

Pascal Sirguey
Miller, Aubrey; Redpath, Todd

Abstract/Description

Mapping snow depth in complex alpine terrain with satellite photogrammetry challenges the limits of 3D-Change Detection (3D-CD), particularly in steep topography and low-contrast conditions. This study evaluates and compares Pléiades PHR and the next-generation Pléiades Neo (PNEO) for snow depth mapping over Kawarau/The Remarkables in Aotearoa/New Zealand.

Tri-stereo acquisitions from PHR and PNEO during winter/spring 2022, combined with snow-free lidar (2016) and PHR (2020) datasets, establish a benchmark for sensor performance. GNSS campaigns provided photogrammetric control and validation, including an in-situ GNSS snow depth survey on and off ski trails, nearly coinciding with the PHR snow-on acquisition. The processing workflow applied an innovative bundle-block adjustment method that automatically propagated absolute georeferencing through cross-matching virtual ground control points (vGCP) from the snow-off photogrammetric model. This approach ensured sub-pixel alignment and enabled repeatable, coherent mapping of snow depth distribution with minimal convolution with the challenging topography.

The results confirm PHR’s capability for snow depth mapping with sub-meter accuracy and demonstrates PNEO’s improved spatial resolution and ability to capture finer-scale snow distribution patterns with greater precision. By validating our cross-matching technique, this study also establishes a repeatable and automated workflow for satellite photogrammetric snow depth mapping in complex terrain.

ID: 3.10549

ENSO diversity regulation of the impact of MJO on extreme snowfall events in the Peruvian Andes

Juan Sulca

Abstract/Description

Extreme snowfall events (ESEs) in the Peruvian Andes (10–18.4°S, > 4000 m) result in considerable economic losses. Despite their importance, how El Niño-Southern Oscillation (ENSO) diversity modulates the impact of the Madden–Julian Oscillation (MJO) on ESEs in the Peruvian Andes remains unexplored. Daily ERA5 reanalysis data from 1981–2018 were analyzed. This study examines 16 ESEs. A bandpass filter with a 20–90-day range was applied to isolate the intraseasonal component of the daily anomalies. Additionally, time series data from the real-time multivariate MJO (RMM) index and Eastern and Central ENSO (E and C) indices were utilized. Composites were performed to describe the atmospheric circulation patterns related to ESEs in the Peruvian Andes under neutral, El Niño and La Niña conditions in the central and eastern Pacific Ocean. Under non-ENSO conditions, the MJO alone does not trigger ESEs in the Peruvian Andes during the DJF season. The absence of a well-organized convection system over the Peruvian Andes prevents ESEs. Conversely, during the JJA season, MJO Phases 5, 6 and 7 induce ESEs in the southern Peruvian Andes by enhancing moisture flux from the east through the equatorward propagation of an extratropical Rossby wave train that crosses South America and reaches the Altiplano region. In terms of ENSO diversity, the combined effects of the Central La Niña and MJO Phases 6+7 induce ESEs across the Western Cordillera of the southern Peruvian Andes during the DJF season. During austral winter, the interaction between the Central El Niño and MJO Phases 8+1, Eastern El Niño and MJO Phases 2+3, and Eastern La Niña and MJO Phases 8+1 induce ESEs across the Peruvian Andes.

ID: 3.10982

Multi-sensor satellite observations of snow area extent and snow state conditions in mountain regions

Maria Heinrich
Nagler, Thomas; Schwaizer, Gabriele; Moelg, Nico; Hetzenecker, Markus

Abstract/Description

Detailed information on the extent and state of the seasonal snow in high mountain regions is needed for applications in snow hydrology, water management and in the field of climate impact. Due to the high spatial variability of seasonal snow in space and time, high resolution satellites provide efficient means for comprehensive snow monitoring in high mountain terrain. We report on the development of an advanced method for monitoring snow extent from multiple optical satellite data optimized for scientific and operational application in mountain areas. Regarding snow extent, we developed a multi-spectral unmixing approach with locally adaptive endmember selection (LAMSU) that accounts for variations in illumination across mountainous terrain and offers flexibility regarding the optimum use of spectral sensor capabilities. Our approach separates regions illuminated by the sun from shaded regions using spectral classification rules for detecting different snow free and fully snow covered endmembers by applying adapted spectral band combinations. The algorithm is designed to provide consistent snow extent estimates from satellite sensors with different spatial resolution and spectral channels, such as sensors of the Copernicus Sentinel-2 and Sentinel-3 missions. By combining both satellite missions, we provide daily medium resolution snow products (300m) from Sentinel-3 SLSTR / OLCI together with high resolution snow products with 20m pixel size from Sentinel-2, acquired every few days over mountain regions. Maps of uncertainty are attached to the snow extent products. Snow extent from optical satellites can be combined with snow wetness products from Sentinel-1 SAR data to classify the snow state conditions (wet/dry). A change detection algorithm is applied exploiting the strong decrease of backscatter for wet snow in comparison to snow free conditions and dry snow. An uncertainty measure for the wet snow detection is provided, accounting for dual-frequency backscatter intensity and speckle statistics. In the presentation we outline the snow mapping procedure, show examples of snow products for different mountain regions worldwide, and report on the quality of the products in comparison with snow information from other sources.

ID: 3.11773

Preparing for NASA SBG: fusing multi- and hyperspectral airborne and satellite data to create daily, high resolution snow albedo products

Ross Palomaki
Rittger, Karl; Skiles, McKenzie; Lenard, Sebastien; Avchyan, Anton

Abstract/Description

Spatially-distributed snow albedo data can be derived from multispectral satellite data, including MODIS, Sentinel-2, and Landsat platforms. While a number of snow albedo algorithms exist, a recent MODIS-focused analysis shows that approaches that use spectral mixture analysis (SMA) result in the most accurate snow albedo products. SMA uses spectral libraries to solve for the snow fraction, grain size, and the impact of light absorbing particles (LAP) in each satellite pixel; snow albedo is then estimated by combining the grain size with darkening due to LAP. The accuracy of SMA-derived snow albedo estimations increases when more spectral information is available. The upcoming NASA Surface Biology and Geology (SBG) satellite mission will provide hyperspectral data at approximately 30 m spatial resolution, which can improve snow albedo estimates compared to multispectral satellite data. However, the planned orbit of SBG will result in temporal data gaps up several weeks depending on the region of interest. These relatively sparse temporal observations will miss important snow albedo changes on daily to weekly timescales, especially large changes from early season accumulation or late season dust-on-snow events. In this presentation, we demonstrate the potential of a data fusion approach to produce daily, SMA-derived snow albedo data at high spatial resolutions using multispectral and hyperspectral imagery. Our model fuses snow albedo products instead of surface reflectance measurements, to leverage the ability of SMA to calculate pure snow albedo even in partially snow-covered pixels. We use a random forest model trained on airborne snow albedo surveys from both AVIRIS-NG and the Airborne Snow Observatory (ASO) at 50 m spatial resolution. Predictor variables include daily, 500 m MODIS snow albedo generated using SMA, as well as terrain characteristics and solar illumination. The fused product takes advantage of the more accurate and finer resolution hyperspectral data while maintaining the daily temporal resolution of multispectral MODIS imagery. This flexible approach demonstrates a potential way forward for a combined VIIRS/SBG snow albedo product in the future.

ID: 3.13009

Gap-filling mountain regions of the Snow CCI SWE product using Bayesian snow reanalysis

Colleen Mortimer
Sun, Haorui; Fang, Yiwen; Margulis, Steven A.; Mudryk, Lawrence; Derksen, Chris; Marin, Carlo; Barella, Riccardo; Nagler, Thomas; Schwaizer, Gabriele

Abstract/Description

The ESA CCI Snow water equivalent (SWE) product does not provide estimates over complex terrain because the grid spacing of the input passive microwave (PMW) data is incompatible with the scales of SWE variability, SWE exceeds the limit of PMW differencing methods, and in situ snow depth observations used to constrain the retrieval are insufficient to meaningfully capture elevation and topographic controls on snow depth distribution. We address this gap within the Snow CCI product line by assimilating Snow CCI snow cover fraction (SCF) (1 km resolution) into a previously developed Bayesian reanalysis approach (Margulis et al. 2016) to reconstruct SWE. Initial implementations over four mountain domains in western North America (Tuolumne and Aspen in the US, Bow and Lajoie in Canada) for water years 2001 – 2019, showed that the SWE reanalysis framework using the Snow CCI SCF product performs similarly to previous implementations in other regions and using higher resolution SCF estimates. However, there were systematic differences in performance compared to a reference analysis that assimilated LandSat-derived SCF (30 m resolution), in part attributed to the differing resolutions of the two input SCF products, although viewing geometry also played a role. Analysis showed that both the number of SCF images and their characteristics, such as zenith angle and cloud cover fraction, significantly affect the accuracy of SWE estimation. Finally, performance was worse in the Canadian domains where we applied meteorological downscaling and prior uncertainty estimates which were developed for the Western US. These aspects need to be better understood prior to large-scale implementation of the method. We will present results of the initial tests in Western North America and preliminary results from experiments designed to understand the impact of SCF spatial resolution, viewing angle geometry, and meteorological forcing. Margulis, S. A. et al. (2016) J. Hydromet., doi: 10.1175/JHM-D-15-0177.1

ID: 3.13125

Climatic and morphological factors controlling the development of glacial lakes in High Mountain Asia

Sheharyar Ahmad
Traversa, Giacomo; Salerno, Franco; Guyennon, Nicolas; Calciati, Luca

Abstract/Description

Glaciers in High Mountain Asia (HMA) play a crucial role in modulating the release of freshwater into rivers and supporting ecosystems. However, the glacier changes not only impact the water supply for the downstream area, but also alter the frequency and intensity of glacier-related hazards, such as glacial lake outburst floods (GLOFs). An increasing frequency and risk of GLOFs is threatening the Asian population. In this context, glacial lake inventories benefit the disaster risk assessment and contribute to predicting glacier–lake interactions under climate change. Studies in glacial lake inventories using satellite observations have been heavily conducted in the Tibetan Plateau. However, a recent glacial lake mapping is still absent for the overall HMA, although the recent availability of Sentinel-2 satellite with a resolution of 10 m. Here we present the GLACIAL LAKE INVENTORY for the entire HMA regions based on more than 1300 images of Sentinel-2 collected during the 2022 year. A semi-automated lake mapping method have been developed and validated in order to assess and reduce the uncertainty. This study aims to present: (1) an up-to-date glacial lake inventory using Sentinel-2 images for the overall HMA; (2) the rigorous validation methodology adopted to check and reduce the uncertainty; (3) the morphological factors, derived from the Randolph Glacier Inventory (4) and the climatic parameters, considering reanalysis products. Generally, this work updates the current knowledge on distribution of glacial lakes and on factors responsible for their development in High Mountain Asia.

FS 3.166: Alpine microclimates, biodiversity, and climate change

FS 3.168: A multidisciplinary approach to the science and conservation of global mountain spring ecosystems

#IMC: September 14 - 18 2025

FS 3.167

Advancements in monitoring snow and glaciers in mountain regions using satellite

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Assigned to Synthesis Workshop

Thematic Focus

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