Open AccessA rapid atmospheric correction model for L-band microwave radiometer under the cloudless condition
Author(s) -
Yan Du,
Wentao Ma,
Yang Xiao-feng,
Guihong Liu,
Y. E. Yang,
Ziwei Li
Publication year - 2015
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.64.079501
Subject(s) - environmental science , radiometer , downwelling , atmospheric correction , remote sensing , atmospheric model , brightness temperature , radiative transfer , microwave radiometer , radiation , satellite , water vapor , atmospheric radiative transfer codes , meteorology , atmospheric sciences , brightness , physics , optics , geology , upwelling , oceanography , astronomy
Atmospheric correction is very important to the accurate retrieval of geophysical parameters from spaceborne L-band radiometers. In this paper, the L-band upwelling and downwelling radiation brightness temperature and transmittance above sea surface are calculated using the atmospheric radiation transfer model based on NCEP temperature and humidity profile data. A regression model, i.e., radiation-vapor model, is established to describe the relationship between the three atmospheric radiation parameters and the atmospheric water vapor content as well as the sea surface pressure. Using this model, the atmospheric radiation parameters can be calculated and used to correct the atmospheric effects in L-band microwave radiometer observation. In order to test the proposed model, the atmospheric radiation parameters are calculated by this model and compared with the SSM/I water vapor content data and the NCEP surface pressure data. Finally, the model outputs are compared with the Aquarius satellite data. Results indicate that the radiation brightness temperature calculated by the proposed model is lower than the Aquarius data about 0.335 K and the root-mean-square error between them is about 0.086 K after correcting the systematic errors. The atmospheric transmittance calculated by the proposed model agrees well with the Aquarius data. Besides, the proposed model uses fewer input data and is faster and more stable than other existing models.