Abstract
Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.
Highlights
In 2006, Markus et al [25] found that using lowfrequency channels of AMSR-E or Advanced Microwave Scanning Radiometer 2 (AMSR2) might improve the capability of snow depth observation because low-frequency signals are more sensitive to deep snow and are less affected by weather, ice, and white frost inside the snow
This is the scattering of microwave signals at high frequency at low frequency, the mainly because, as the particle size is ofgreater snow becomes larger, thethan scattering of microwave signal attenuation becomes very fast as which the particle and becomes at signals by snow gradually increases, leadssize to increases faster changes in TBs saturated with snow a certain snow depth, which will result in GRs that no the snowthan depth depth
Starting from the physical process of microwave signal transmission in ice, snow, and the atmosphere, the influence of snow depth variations on surface observed TBs was analyzed, along with their correlation based on radiative transfer theory
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In 2006, Markus et al [25] found that using lowfrequency channels of AMSR-E or AMSR2 might improve the capability of snow depth observation because low-frequency signals are more sensitive to deep snow and are less affected by weather, ice, and white frost inside the snow According to this inference, Rostosky et al [26] derived new retrieval coefficients based on a regression analysis using five years of Operation IceBridge (OIB) airborne snow depth measurements (see Section 2.1.2 for a definition of OIB data) and extended the algorithm to take advantage of the lower frequency channel at 7 GHz. The gradient ratios of the 18.7 and 6.9 GHz vertical TB were used for statistical regression. The bias and RMSE between the obtained snow data and the IMB data were 0.1 and 9.8 cm, respectively In addition to these empirical algorithms, Maaß et al proposed an algorithm for snow depth retrieval on thick ice based on radiative transfer theory using L-band TBs from SMOS [30]. We verified the retrieval snow depths using 2011 OIB snow depth data
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