Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

Deschamps-Berger, César; Gascoin, Simon; Berthier, Étienne; Deems, J. S.; Gutmann, E. D.; Dehecq, Amaury; Shean, David; Dumont, Marie

doi:10.5194/tc-14-2925-2020

Cited by 86 publications

(105 citation statements)

References 45 publications

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“…We opted for a FN-FB set as it offered a larger disparity in point clouds when calculated within the ASP routine, and therefore found as the best trade-off in reducing intersection errors and data gaps due to terrain occlusion. The semi-global matching, binary transform routine in ASP was found by Deschamps-Berger et al (2020) to reduce the random error and root mean squared error of both snowcovered and stable, snow-free terrain compared to local search algorithms previously implemented for snow depth mapping using Pléiades (Marti et al, 2016;Shaw et al, 2020a). Due to saturation in the SnowON_2017 images (see discussion Limitations of Pléiades for Multi-Annual Snow Depth Derivation), correlation failure of the stereo process in ASP resulted in a loss of ∼17% of the total pixels in the catchment (Shaw et al, 2020a).…”

Section: Digital Elevation Model Generationmentioning

confidence: 99%

“…A key challenge remains for linking precipitation events, wind variability and snow depth at the highest reaches of the mountain catchments, especially because the available data for snow depth offers only a snap shot in time (Figure 4). Nevertheless, the emergence of decimetre accuracy Pléiades and World-View products to estimate snow depth (Marti et al, 2016;McGrath et al, 2019;Shaw et al, 2020a;Deschamps-Berger et al, 2020) may offer new potential in how we can constrain high mountain precipitation, though further steps are required to relate localised in situ meteorological observations to catchment-wide snow volumes on an inter-annual basis.…”

Section: Snow Depth Vs Meteorology Of the Central Andesmentioning

confidence: 99%

“…The advent of high resolution, optical satellite constellations has lead to many valuable contributions in the discipline of Earth sciences (Zhou et al, 2015;Bagnardi et al 2016), including quantification of glacier mass balance (Berthier et al, 2014;Melkonian et al, 2016;Belart et al, 2017;Brun et al, 2018;Bƚaszczyk et al, 2019;Ren et al, 2020) and spatial snow volumes (Marti et al, 2016;McGrath et al, 2019;Deschamps-Berger et al, 2020;Shaw et al, 2020a). The application of sub-metre resolution stereo imagery for elevation model generation, namely that of Pléiades and WorldView products, has demonstrated decimetre accuracy when comparing to high resolution, albeit typically small scale, reference data (Marti et al, 2016;Shaw et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…The application of sub-metre resolution stereo imagery for elevation model generation, namely that of Pléiades and WorldView products, has demonstrated decimetre accuracy when comparing to high resolution, albeit typically small scale, reference data (Marti et al, 2016;Shaw et al, 2020a). Recent work by Deschamps-Berger et al (2020) found a sub-metre random error and low bias of Pléiades-derived snow depths when comparing to the Airborne Snow Observatory ("ASO") over an extensive (137 km 2 ) area of the Tuolumne Basin, United States. The direction of this research suggests that the use of such high spatial and temporal resolution (tri-) stereo satellite imagery will continue to be a valuable tool for understanding the mountain snowpack, regardless of the limitations that it presents, notably that of cloud cover occlusion and a reduced accuracy for steep terrain (Deschamps-Berger et al, 2020;Shaw et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

et al. 2020

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Quantifying the high elevation winter snowpack in mountain environments is crucial for lowland water supply, though it is notoriously difficult to accurately estimate due to a lack of observations and/or uncertainty in the distribution of meteorological variables in space and time. We compare high spatial resolution (3 m), satellite-derived snow depth maps for two drought years (2017 and 2019) in a high mountain catchment of the central Chilean Andes, applying a recently updated methodology for spaceborne photogrammetry. Regional weather station observations revealed an 80% reduction in precipitation for 2019 (the second driest winter since 1950) relative to 2017, though only a 10% reduction in total snow-covered area is seen in the satellite imagery. We threshold surface height changes based upon uncertainty of stable (snow-free) terrain differences for topographic characteristics of the catchment (slope, aspect, roughness etc). For a conservative analysis of change, outside of the topographically-derived confidence intervals, we calculate a mean 0.48 ± 0.28 m reduction of snow depth and a 39 ± 15% reduction in snow volume for 2019, relative to 2017 (for 23% of the total catchment area). Our findings therefore quantify, for the first time in the Andes, the relationship of high-resolution mountain snow depth observations with low elevation precipitation records and characterise its inter-annual variability over high elevation, complex terrain. The practical use of such detailed snow depth information at high elevations is of great value to lowland communities and our findings highlight the clear need to relate the high spatial (Pléiades) and temporal (in-situ) scales within the available datasets in order to improve estimates of region-wide snow volumes.

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Section: Digital Elevation Model Generationmentioning

confidence: 99%

Section: Snow Depth Vs Meteorology Of the Central Andesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

et al. 2020

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“…The benefits of remote sensing techniques to characterise snow have been long-established (e.g. Dietz et al, 2012;Dozier and Painter, 2004;König et al, 2001;Kokhanovsky et al, 2019;Nolin, 2010). Commonly, methods to derive information about the snow physical properties from optical satellite observations are based on surface reflectance products (e.g.…”

Section: Introductionmentioning

confidence: 99%

Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain

et al. 2020

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Abstract. The monitoring of snow-covered surfaces on Earth is largely facilitated by the wealth of satellite data available, with increasing spatial resolution and temporal coverage over the last few years. Yet to date, retrievals of snow physical properties still remain complicated in mountainous areas, owing to the complex interactions of solar radiation with terrain features such as multiple scattering between slopes, exacerbated over bright surfaces. Existing physically based models of solar radiation across rough scenes are either too complex and resource-demanding for the implementation of systematic satellite image processing, not designed for highly reflective surfaces such as snow, or tied to a specific satellite sensor. This study proposes a new formulation, combining a forward model of solar radiation over rugged terrain with dedicated snow optics into a flexible multi-sensor tool that bridges a gap in the optical remote sensing of snow-covered surfaces in mountainous regions. The model presented here allows one to perform rapid calculations over large snow-covered areas. Good results are obtained even for extreme cases, such as steep shadowed slopes or, on the contrary, strongly illuminated sun-facing slopes. Simulations of Sentinel-3 OLCI (Ocean and Land Colour Instrument) scenes performed over a mountainous region in the French Alps allow us to reduce the bias by up to a factor of 6 in the visible wavelengths compared to methods that account for slope inclination only. Furthermore, the study underlines the contribution of the individual fluxes to the total top-of-atmosphere radiance, highlighting the importance of reflected radiation from surrounding slopes which, in midwinter after a recent snowfall (13 February 2018), accounts on average for 7 % of the signal at 400 nm and 16 % at 1020 nm (on 13 February 2018), as well as of coupled diffuse radiation scattered by the neighbourhood, which contributes to 18 % at 400 nm and 4 % at 1020 nm. Given the importance of these contributions, accounting for slopes and reflected radiation between terrain features is a requirement for improving the accuracy of satellite retrievals of snow properties over snow-covered rugged terrain. The forward formulation presented here is the first step towards this goal, paving the way for future retrievals.

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Snow in the Mountains

García-Ruiz,

Arnáez,

Lasanta

et al. 2024

Earth and Environmental Sciences Library

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Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

Cited by 86 publications

References 45 publications

Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain

Snow in the Mountains

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