Forward and Inverse Radar Modeling of Terrestrial Snow Using SnowSAR Data

Zhu, Jiyue; Tan, Shurun; King, Joshua; Derksen, Chris; Lemmetyinen, Juha; Tsang, Leung

doi:10.1109/tgrs.2018.2848642

Cited by 46 publications

(117 citation statements)

References 26 publications

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“…The amount of attenuation depends on the optical thickness of the snow ( τ p ) and the radar incidence angle ( θ ). Commonly, and are neglected 29 and thus not further considered here.…”

Section: Resultsmentioning

confidence: 99%

Snow depth variability in the Northern Hemisphere mountains observed from space

et al. 2019

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Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

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“…The amount of attenuation depends on the optical thickness of the snow ( τ p ) and the radar incidence angle ( θ ). Commonly, and are neglected 29 and thus not further considered here.…”

Section: Resultsmentioning

confidence: 99%

Snow depth variability in the Northern Hemisphere mountains observed from space

et al. 2019

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“…accurate snowpack modelling at the tens of metres scale in tundra environments is challenging (Essery et al, 1999;Clark et al, 2011), snowpack layer correlation lengths could be used to parametrise models so variability of snowpack properties are reliably accounted for when modelling at coarser resolutions. This will become an important parameterisation for future linkages between physical snowpack models and one-dimensional radiative transfer models (Sandells et al, 2017), to better model surface and volume scattering in snow (Zhu et al, 2018), and in observing system simulation experiments (e.g. Garnaud et al, 2019) to assess the performance of future Ku-band radar satellite sensor configurations.…”

Section: Discussionmentioning

confidence: 99%

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Rutter¹,

Sandells²,

Derksen³

et al. 2019

Preprint

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Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of first guess information of snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths were sub-metre for all layers. Depth hoar was consistently ~ 30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and SSA of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks

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“…The satellite SAR SWE retrieval algorithms can be grouped into two categories: (a) physical inversion algorithms and (b) interferometry methods. By utilizing the frequency-dependent sensitivity to snow and underlying soil properties, combined multi-frequency SAR observations (e.g., X-and Ku-band) are capable of SWE retrievals as demonstrated by model simulations and field experiments [140][141][142][143]. The uncertainties related to snow density, ice microstructure, snow layer stratification, vegetation, and terrain effects are the main issues affecting the performance of both passive and active microwave snow retrieval algorithms [144][145][146].…”

Section: Snow Water Equivalentmentioning

confidence: 99%

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

Watts

Jiang

et al. 2019

Remote Sensing

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Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

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Forward and Inverse Radar Modeling of Terrestrial Snow Using SnowSAR Data

Cited by 46 publications

References 26 publications

Snow depth variability in the Northern Hemisphere mountains observed from space

Snow depth variability in the Northern Hemisphere mountains observed from space

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

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