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Research Letter

Preliminary analysis of calibration for Chinese civilian 1mC-SAR

, ORCID Icon, , &
Pages 709-718 | Received 07 Dec 2023, Accepted 15 Jun 2024, Published online: 24 Jun 2024
 

ABSTRACT

The main purpose of this letter is to investigate the performance of the calibration of the Chinese civilian satellite carrying synthetic aperture radar (SAR), denoted as 1mC-SAR, which is a successor of the previous Gaofen-3 (GF-3) satellite. In total, more than 500 1mC-SAR images in co-polarization modes (vertical–vertical [VV] and horizontal–horizontal [HH]) are collocated with the wind products from Haiyang-2 (HY-2B/2C/2D) scatterometers in April – May 2023. The well-developed geophysical model function (GMF) CMOD7 and polarization ratio PR2011 in the C-band are used to simulate the normalized radar cross section (NRCS) employing the wind vector from HY-2. There are more than 2000 matchups for VV-polarization and more than 1000 matchups for HH-polarization. Comparison of the simulations and 1mC-SAR measurements yields a root mean squared error (RMSE) of 3.03 dB, a correlation coefficient (r) of 0.93, a scatter index (SI) of −0.14, and a bias of −2.22 dB for the VV-polarized NRCS, which is slightly better than the 3.68 dB RMSE, 0.90 r, −0.15 SI, and −3.08 dB bias of the HH-polarized NRCS. The variations in the difference in the VV-polarized NRCS (1mC-SAR measurements minus simulations) with respect to the wind speed and incidence angle are analysed. Despite the difference in the VV-polarization (i.e. <2 dB) at wind speeds of greater than 3 m s−1, the difference in the VV-polarization approaches 3 dB at several incidence angles, which needs to be further improved.

Acknowledgments

We appreciate the provision of the 1mC-SAR image and wind product from HY-2 scatterometers by the National Satellite Ocean Application Service through an authorized account via https://osdds.nsoas.org.cn.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the National Key Research and Development Program of China [2023YFE0102400], the National Natural Science Foundation of China [42076238 and 42376174], and the Natural Science Foundation of Shanghai [23ZR1426900].

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