Geophysical and atmospheric controls on Ku-, X- and C-band backscatter evolution from a saline snow cover on first-year sea ice from late-winter to pre-early melt

Nandan, Vishnu; Scharien, Randall K.; Geldsetzer, Torsten; Mahmud, Mallik; Yackel, John J.; Islam, Tanvir; Gill, Jagvijay P. S.; Fuller, M. Christopher; Gunn, Grant E.; Duguay, Claude R.

doi:10.1016/j.rse.2017.06.029

Cited by 16 publications

(18 citation statements)

References 41 publications

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“…The highest brine volumes are found at the snow/sea ice interface, decreasing with height into the snow cover. These observations are consistent with the upward brine wicking mechanism expected to occur within the bottom 6-8 cm of the snow cover overlaying FYI (Barber et al, 1995;Crocker, 1992;Drinkwater & Crocker, 1988;Geldsetzer et al, 2009;Nandan et al, 2017). For snow thicknesses ≤8 cm, the snow is usually found to be completely brine-wetted.…”

Section: Empirical Distribution Of Brine Volume In Snow-covered Fyisupporting

confidence: 83%

“…For 10 cm ≥ H S ≤ 24 cm, Δ S ≈ 7 cm, and for H S ≥ 26 cm, Δ S ≈ 6 cm. The consistency of Δ S for snow thicknesses ≥10 cm is supported by observations of brine‐wetted snow typically being confined within the bottom 6–8 cm of the snow cover overlaying FYI (Barber et al, ; Drinkwater & Crocker, ; Geldsetzer et al, ; Nandan et al, ). Therefore, a consistent value of Δ S = 7 cm could reasonably be used for snow thicknesses >8 cm.…”

Section: Resultsmentioning

confidence: 99%

“…In thicker snow covers, strong salinity gradients are commonly observed in the bottommost layers, with significantly lower brine volumes, or brine‐free conditions, in the uppermost snow layers (Drobot & Barber, ; Fuller et al, ). The brine within the snow alters the dielectric and microwave scattering properties of the snow (Barber & Nghiem, ; Drinkwater & Crocker, ; Geldsetzer et al, ), leading to strong microwave attenuation within the snow volume, with significantly reduced radar penetration (e.g., Nandan et al, ). These factors likely also affect the location of the CS‐2 main scattering horizon.…”

Section: Introductionmentioning

confidence: 99%

“…However, no study has investigated the impact of snow salinity on CS-2 signal penetration. dielectric and microwave scattering properties of the snow (Barber & Nghiem, 1999;Drinkwater & Crocker, 1988;Geldsetzer et al, 2009), leading to strong microwave attenuation within the snow volume, with significantly reduced radar penetration (e.g., Nandan et al, 2017). These factors likely also affect the location of the CS-2 main scattering horizon.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Snow Salinity on CryoSat‐2 Arctic First‐Year Sea Ice Freeboard Measurements

Nandan

Geldsetzer

Yackel

et al. 2017

Geophysical Research Letters

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117

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The European Space Agency's CryoSat‐2 satellite mission provides radar altimeter data that are used to derive estimates of sea ice thickness and volume. These data are crucial to understanding recent variability and changes in Arctic sea ice. Sea ice thickness retrievals at the CryoSat‐2 frequency require accurate measurements of sea ice freeboard, assumed to be attainable when the main radar scattering horizon is at the snow/sea ice interface. Using an extensive snow thermophysical property dataset from late winter conditions in the Canadian Arctic, we examine the role of saline snow on first‐year sea ice (FYI), with respect to its effect on the location of the main radar scattering horizon, its ability to decrease radar penetration depth, and its impact on FYI thickness estimates. Based on the dielectric properties of saline snow commonly found on FYI, we quantify the vertical shift in the main scattering horizon. This is found to be approximately 0.07 m. We propose a thickness‐dependent snow salinity correction factor for FYI freeboard estimates. This significantly reduces CryoSat‐2 FYI retrieval error. Relative error reductions of ~11% are found for an ice thickness of 0.95 m and ~25% for 0.7 m. Our method also helps to close the uncertainty gap between SMOS and CryoSat‐2 thin ice thickness retrievals. Our results indicate that snow salinity should be considered for FYI freeboard estimates.

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Section: Empirical Distribution Of Brine Volume In Snow-covered Fyisupporting

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Snow Salinity on CryoSat‐2 Arctic First‐Year Sea Ice Freeboard Measurements

Nandan

Geldsetzer

Yackel

et al. 2017

Geophysical Research Letters

Self Cite

117

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“…Given that late winter σ 0 VV and σ 0 HV exhibit negative correlation with melt season f p (Table 4; e.g., Figures 4 and 5), and that f p is inversely correlated with late winter snow thickness [7], we infer that σ 0 VV , and σ 0 HV increase with snow thickness in a manner similar to previous studies [2,39].R VV HH exhibits substantial negative correlation with f p at MR (e.g., Figure 6). R VV HH pixel values are primarily negative, indicating that σ 0 HH > σ 0 VV , suggesting that Fresnel reflection effects associated with a smooth dielectric interface are responsible [70]. R VV HH tends toward 0 dB as f p decreases, pointing to reduced Fresnel reflection, which, in turn, may be associated with rougher FYI and thicker snow.…”

Section: Relationship Between Winter Sar Backscatter and Pond Fractionmentioning

confidence: 99%

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

et al. 2018

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Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover.

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Snow on sea ice

Mallett,

Nandan,

Macfarlane

et al. 2024

Reference Module in Earth Systems and Environmental Sciences

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Geophysical and atmospheric controls on Ku-, X- and C-band backscatter evolution from a saline snow cover on first-year sea ice from late-winter to pre-early melt

Cited by 16 publications

References 41 publications

Effect of Snow Salinity on CryoSat‐2 Arctic First‐Year Sea Ice Freeboard Measurements

Effect of Snow Salinity on CryoSat‐2 Arctic First‐Year Sea Ice Freeboard Measurements

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

Snow on sea ice

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