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Comprehensive Vector Radiative Transfer Model for Estimating Sea Surface Salinity From L-Band Microwave Radiometry.

Authors :
Jin, Xuchen
He, Xianqiang
Shanmugam, Palanisamy
Bai, Yan
Gong, Fang
Yu, Shujie
Pan, Delu
Source :
IEEE Transactions on Geoscience & Remote Sensing. Jun2021, Vol. 59 Issue 6, p4888-4903. 16p.
Publication Year :
2021

Abstract

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<1%) for low wind speeds (<1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
59
Issue :
6
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
150448490
Full Text :
https://doi.org/10.1109/TGRS.2020.3007878