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Influences of Two-Scale Roughness Parameters on the Ocean Surface Emissivity From Satellite Passive Microwave Measurements

Cited 2 time in Web of Science Cited 2 time in Scopus
Authors

Lee, Sang-Moo; Gasiewski, Albin J.; Sohn, Byung-Ju

Issue Date
2022
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Geoscience and Remote Sensing, Vol.60, p. 4204112
Abstract
In this study, a method for estimating two-scale roughness influences on the ocean surface emissivity is developed by solving a simplified two-scale ocean emissivity model equation. In this model, scatterings by small-scale roughness are described by the Kirchhoff approximation. For large-scale roughness, the mean local incidence angle (LIA) is introduced to describe slanted surface slope deviation from flat surface. This study focuses on the ocean state under low/moderate wind conditions in order to preclude foam and anisotropic influences within the model. Consequently, a unique pair of two-scale roughness parameters are estimated from the equation using observed ocean emissivities from AMSR2-measured radiances. The results show that the estimated small-scale roughness at 6.925 and 10.65 GHz is linearly correlated with the 10-m height wind speed U-10. As the frequency reaches 36.5 GHz, however, the scatters between small-scale roughness and U-10 are increased, which suggests that the Kirchhoff bistatic scattering function is not fully suitable to describe the small-scale roughness at this frequency. The linear relationships between mean LIA and U-10 are found with high correlation coefficients. In addition, the estimated mean LIA corresponds well with associated roughness calculated from both observed and modeled ocean wave height spectra. This evidence demonstrates that the proposed large-scale roughness parameterization is physically meaningful and, therefore, the mean LIA has a physical basis in large-scale roughness. In addition, the strong correlations between the roughness parameters and U-10 demonstrate the possibility to estimate U-10 from the AMSR2 data using intermediate parameters that are physically based on ocean surface characteristics.
ISSN
0196-2892
URI
https://hdl.handle.net/10371/201040
DOI
https://doi.org/10.1109/TGRS.2021.3105915
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Data Assimilation for Numerical Weather Prediction, Radiative Transfer Modeling, Satellite Remote Sensing

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