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The polarization instrument of Fengyun-3 precipitation satellite is the first Polarization and Multi-Angle Imager (PMAI) with short-wave infrared channel in China, aiming to accurately measure the radiation characteristics of clouds and aerosols in the atmosphere. The accuracy of radiometric measurement is an important technical index of instrument performance, which is of great significance for the inversion of high-precision quantitative parameters of satellite remote sensing. For the non-polarization channel of a polarization imager, polarization is a kind of interference information, and the polarization sensitivity of the instrument needs to be inverted and quantitatively removed to improve the accuracy of radiation calibration. A method of least squares fitting response value of complete linear polarization incident light based on different polarization angles is proposed to measure the polarization sensitivity of the non-polarization channel full field of view. According to the measured polarization rate of each channel and the polarization characteristics of the incident light, the polarization sensitivity of each channel is calibrated based on the polarization calibration model. The results show that the polarization sensitivity of the non-polarization channel shows obvious edge effect, gradually increasing from the center of the focal plane to the edge, and has obvious spectral differences, the smaller the wavelength having the higher the polarization sensitivity. The maximum polarization sensitivity occurs in the edge field of view of the non-polarization channel in the 1030nm band, close to 1.6%, which has a great impact on the accuracy of radiometric calibration. After polarization sensitivity calibration, the polarization sensitivity of the edge field of view is within 0.5%. The results show that by calibrating the polarization sensitivity of the full field of view of the non-polarization channel, the radiometric calibration can be effectively improved, which provides strong support for high-precision quantitative remote sensing.
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