Impact of Surface Roughness on AMSR-E Sea Ice Products

Stroeve, Julienne; Markus, T.; Maslanik, James A; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, J.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew

doi:10.1109/tgrs.2006.880619

Cited by 56 publications

(68 citation statements)

References 18 publications

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“…Markus and Cavalieri, 1998). However, surface roughness variations introduce uncertainties to this method (Stroeve et al, 2006), and the method is only applicable to dry snow conditions and only to Antarctic sea ice and first-year ice in the Arctic, but fails over multi-year ice (Comiso et al, 2003). Thus, a method to estimate snow thickness over thick Arctic multi-year ice from SMOS brightness temperatures would improve monitoring of sea ice conditions in the Arctic from space.…”

Section: Introductionmentioning

confidence: 99%

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

et al. 2013

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Abstract. The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences Lband brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data.We find that the brightness temperatures above snowcovered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r 2 = 0.58.

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Section: Introductionmentioning

confidence: 99%

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

et al. 2013

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“…Extensive field work has been conducted in the Barrow area (e.g., George et al [13], Eicken et al [64], Druckenmiller et al [14]), including collection of snow depth data, laser profilometer data and other aerial observations as part of the NASA AMSR-E validation campaigns for sea ice products in March 2003 and 2006, AMSRIce03 and AMSRIce06 (see Herzfeld et al [31], Maslanik et al [63], Cavalieri et al [65], Sturmet al [66], Markus et al [67], Heinrichs et al [68], Rivas et al [69], Stroeve et al [70]). …”

Section: In-situ Observations and Related Data Analysis Resultant Fromentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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“…Sea ice roughness affects passive microwave emission differently depending on microwave frequency, polarization, and sensor-surface geometry. There is still a perplexing ambiguity in deciphering dielectric and surface roughness contributions from the MIZ to the passive microwave emissions detected at the satellite sensor due to insufficient in situ data suitable for such work (Stroeve et al, 2006;Hong, 2010). Helicopter-based laser profiling and LiDAR (Light Detection And Ranging) imaging of rough sea ice further aid these investigations (Rivas et al, 2006;Haas et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Various remote sensing approaches to understanding roughness in the marginal ice zone

Gupta

2015

Physics and Chemistry of the Earth, Parts A/B/C

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Impact of Surface Roughness on AMSR-E Sea Ice Products

Cited by 56 publications

References 18 publications

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Various remote sensing approaches to understanding roughness in the marginal ice zone

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