The influence of snow microstructure on dual-frequency radar measurements in a tundra environment

King, Joshua; Derksen, Chris; Toose, Peter; Langlois, Alexandre; Larsen, C. F.; Lemmetyinen, Juha; Marsh, P.; Montpetit, Benoît; Roy, Alexandre; Rutter, Nick; Sturm, Matthew

doi:10.1016/j.rse.2018.05.028

Cited by 59 publications

(97 citation statements)

References 52 publications

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“…Tundra snow is typically composed of fine-grained wind slab layers overlying depth hoar composed of large faceted grains. Under these conditions, it is likely that two layer snow simulations would be necessary [44]. However, also in the case of NoSREx data, early season radar observations, and thus retrievals of both p active ex,eff and SWE, were assumed to be influenced by melt-refreeze crusts in the base of the snowpack; causing the high backscattering values seen in the early season (Figure 1).…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

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Section: Discussionmentioning

confidence: 99%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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“…1; Derksen et al, 2009). As depth hoar and wind slab have strongly diverging microwave scattering properties (Hall et al, 1991), the relative proportion of each strongly influences Ku-band radar backscatter (Yueh et al, 2009;King et al, 2015King et al, , 2018. Knowledge of how layers vary within Arctic snowpacks is therefore critical to the assessment of uncertainty in radar-based retrievals of snow water equivalent (SWE) and forward models of snow radiative transfer.…”

Section: Introductionmentioning

confidence: 99%

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

Rutter

Sandells²,

Derksen

et al. 2019

The Cryosphere

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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 nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown 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 in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a 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 specific surface area (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 <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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“…A minimal difference in backscatter was observed between the forested (S20) and non-forested (S6) sites with snow in Figure 6a, while a greater difference, up to 4.5 dB and 6 dB for co-and cross-polarized backscatter, was observed between the forested (S21) and non-forested (S9) sites without snow in Figure 6b; the larger cross-polarized difference was a result of enhanced volume scattering in the forest canopy. Because the SWE at S20 was 48 mm greater than that at S6, greater backscatter would be expected at the forested site, especially given the larger proportion of wind slab present in the non-forested site, which would have reduced sensitivity to the SWE [56], but this was not the case. This indicated that the forest canopy partially attenuated the signal from the snow beneath.…”

Section: Angular Backscatter Response From the Forested And Non-foresmentioning

confidence: 98%

Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

Thompson

Kelly

2018

Remote Sensing

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UWScat, a ground-based Ku-and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman-Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman-Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment. of radar response [7][8][9][10]. Synthetic aperture radar (SAR) imaging can provide wintertime landscape observations at spatial resolutions <100 m, which makes it an attractive technique for snow mapping.Current methods for estimating the SWE at Ku-and X-band frequencies focus on moderate-to-shallow snow and include that proposed by [7] for the CoReH20 mission and that proposed by [11], which uses an interferometric approach; other recent work has been done by [12][13][14]. However, these studies have excluded forested regions and, therefore, have neglected the sub-canopy SWE. This is unsurprising, because forested regions are among the most challenging environments for remote sensing of snow [15]. Of particular relevance in Canada, the boreal forest, dominated by coniferous species and situated in the circumpolar northern high latitudes, covers 270 million ha, or about 30% of the landscape [16]. Globally, boreal forest covers 1.1 billion ha and is the Earth's largest terrestrial ecosystem [15]. Given the northern locale and the intersection of the boreal zone with snow-covered landscapes [17], it is important that the estimation of the SWE accounts for forest attenuation of the snow backscatter signal.Recent studies on SWE retrieval from borea...

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The influence of snow microstructure on dual-frequency radar measurements in a tundra environment

Cited by 59 publications

References 52 publications

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

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

Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

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