First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval

Junxin Yang; Lihua Zhong; Xinzhe Yuan; Xiaochen Wang; Bing Han; Yuxin Hu

doi:10.1007/s13131-023-2215-5

Volume 42 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2023 > 42(10): 84-96

Junxin Yang, Lihua Zhong, Xinzhe Yuan, Xiaochen Wang, Bing Han, Yuxin Hu. First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval[J]. Acta Oceanologica Sinica, 2023, 42(10): 84-96. doi: 10.1007/s13131-023-2215-5

Citation:

Junxin Yang, Lihua Zhong, Xinzhe Yuan, Xiaochen Wang, Bing Han, Yuxin Hu. First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval[J]. Acta Oceanologica Sinica, 2023, 42(10): 84-96. doi: 10.1007/s13131-023-2215-5

Citation:

Junxin Yang, Lihua Zhong, Xinzhe Yuan, Xiaochen Wang, Bing Han, Yuxin Hu. First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval[J]. Acta Oceanologica Sinica, 2023, 42(10): 84-96. doi: 10.1007/s13131-023-2215-5

PDF( 5593 KB)

First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval

doi: 10.1007/s13131-023-2215-5

Junxin Yang^{1, 2, 3},
Lihua Zhong^{1, 2
,
,},
Xinzhe Yuan⁴,
Xiaochen Wang^{1, 2},
Bing Han^{1, 2},
Yuxin Hu^{1, 2, 3}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
National Satellite Ocean Application Service, Beijing 100081, China

Funds: The National Natural Science Foundation of China under contract No. 41976169.

More Information

Corresponding author: E-mail: zhonglh@aircas.ac.cn
Received Date: 2022-11-06
Accepted Date: 2023-05-22

Available Online: 2023-12-13

Publish Date: 2023-10-01

Abstract

Abstract

Gaofen-3-02 (GF3-02) is the first C-band synthetic aperture radar (SAR) satellite with terrain observation with progressive scans of SAR (TOPSAR) imaging mode in China, which plays an essential role in marine environment monitoring. Given the weak scattering characteristics of the ocean, the system thermal noise superimposed on SAR images has significant interference, especially in cross-polarization channels. Noise-Equivalent Sigma-Zero (NESZ) is a measure of the sensitivity of the radar to areas of low backscatter. The NESZ is defined to be the scattering cross-section coefficient of an area which contributes a mean level in the image equal to the signal-independent additive noise level. For TOPSAR, NESZ exhibits the shape of the SAR scanning gain curve in the azimuth and the shape of the antenna pattern in the range. Therefore, the accurate measurement of NESZ plays a vital role in the application of spaceborne SAR sea surface cross-polarization data. This paper proposes a theoretical calculation method for the NESZ curve in GF3-02 TOPSAR mode based on SAR noise inner calibration data and the imaging algorithm. A method for correcting the error existing in the theoretical curve of NESZ is also proposed according to the relationship between sea surface backscattering and wind speed and the same characteristics of target scattering in the overlapping area of adjacent sub-swaths. According to assessment with wide-swath TOPSAR cross-polarization data, the GF3-02 TOPSAR mode has a very low thermal noise level, which is better than −33 dB at the edge of each beam, and controlled below −38 dB at the center of the beam. The two-dimensional reference curves of the NESZ of each beam are provided to the GF3-02 TOPSAR users. After discussing the relationship between normalized radar cross section (NRCS) and wind speed, we provide a formula for NRCS related to wind speed and radar incidence angle. Compared with the NRCS derived from this formula and the NESZ-subtracted NRCS of SAR images, the bias is −0.0048 dB, the Root Mean Square Error is 1.671 dB and the correlation coefficient is 0.939.
- GF3-02,
- TOPSAR,
- Noise-Equivalent Sigma-Zero (NESZ),
- cross-polarization,
- normalized radar cross section (NRCS),
- sea surface wind

FullText(HTML)

References(31)

References

Albright W, Nicoll J. 2002. Empirical determination of thermal noise levels in synthetic aperture radar. In: IEEE International Geoscience and Remote Sensing Symposium. Toronto, ON, Canada: IEEE, 2729–2731

Angelliaume S, Dubois-Femandez P, Jones C E, et al. 2018. Oil slick detection in the offshore domain: Evaluation of polarization-dependent SAR parameters. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE, 8096–8099

Curlander J C, McDonough R N. 1991. Synthetic Aperture Radar: Systems and Signal Processing. New York, USA: Wiley

Fan Shengren, Zhang Biao, Mouche A A, et al. 2020. Estimation of wind direction in tropical cyclones using C-band dual-polarization synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 58(2): 1450–1462. doi: 10.1109/TGRS.2019.2946885

Freeman A. 1992. SAR calibration: An overview. IEEE Transactions on Geoscience and Remote Sensing, 30(6): 1107–1121. doi: 10.1109/36.193786

Freeman A, Curlander J C. 1989. Radiometric correction and calibration of SAR images. Photogrammetric Engineering and Remote Sensing, 55(9): 1295–1301

Hajnsek I, Pottier E, Cloude S R. 2003. Inversion of surface parameters from polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 727–744. doi: 10.1109/TGRS.2003.810702

Han Bing, Ding Chibiao, Zhong Lihua, et al. 2018. The GF-3 SAR data processor. Sensors, 18(3): 835. doi: 10.3390/s18030835

Korosov A, Demchev D, Miranda N, et al. 2022. Thermal denoising of cross-polarized Sentinel-1 data in interferometric and extra wide swath modes. IEEE Transactions on Geoscience and Remote Sensing, 60: 5218411

Lu Liao, Yang Jie. 2013. Analysis of the influence of water body on polarimetric SAR calibration. In: IET International Radar Conference 2013. Xi’an, China: IET

Martone M, Bräutigam B, Rizzoli P, et al. 2013. Impact of SAR data quantization on TanDEM-X performance. In: 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne, VIC, Australia: IEEE, 4487–4490

Moon W M, Staples G, Kim D J, et al. 2010. Radarsat-2 and coastal applications: Surface wind, waterline, and intertidal flat roughness. Proceedings of the IEEE, 98(5): 800–815. doi: 10.1109/JPROC.2010.2043331

Park J W, Korosov A A, Babiker M, et al. 2018. Efficient thermal noise removal for Sentinel-1 TOPSAR cross-polarization channel. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1555–1565. doi: 10.1109/TGRS.2017.2765248

Park J W, Won J S, Korosov A A, et al. 2019. Textural noise correction for Sentinel-1 TOPSAR cross-polarization channel images. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 4040–4049. doi: 10.1109/TGRS.2018.2889381

Ren Lin, Yang Jingsong, Mouche A, et al. 2017. Preliminary analysis of Chinese GF-3 SAR quad-polarization measurements to extract winds in each polarization. Remote Sensing, 9(12): 1215. doi: 10.3390/rs9121215

Scheuchl B, Cumming I. 2005. Analysis of the influence of NESZ variations on cross-polarized signatures of sea ice. In: Proceedings 2005 IEEE International Geoscience and Remote Sensing Symposium. Seoul, Korea(South): IEEE, 5157–5160

Shi Lei, Yang Le, Zhao Lingli, et al. 2021. NESZ estimation and calibration for Gaofen-3 polarimetric products by the minimum noise envelope estimator. IEEE Transactions on Geoscience and Remote Sensing, 59(9): 7517–7534. doi: 10.1109/TGRS.2020.3033534

Torres R, Navas-Traver I, Bibby D, et al. 2017. Sentinel-1 SAR system and mission. In: 2017 IEEE Radar Conference (RadarConf). Seattle, WA, USA: IEEE, 1582–1585

Vachon P W, Wolfe J. 2011. C-band cross-polarization wind speed retrieval. IEEE Geoscience and Remote Sensing Letters, 8(3): 456–459. doi: 10.1109/LGRS.2010.2085417

Valenzuela G R. 1978. Theories for the interaction of electromagnetic and oceanic waves−A review. Boundary-Layer Meteorology, 13(1): 61–85

Van Zadelhoff G J, Stoffelen A, Vachon P W, et al. 2014. Retrieving hurricane wind speeds using cross-polarization C-band measurements. Atmospheric Measurement Techniques, 7(2): 437–449. doi: 10.5194/amt-7-437-2014

Verhoef A, Portabella M, Stoffelen A, et al. 2008. CMOD5. N-the CMOD5 GMF for neutral winds. https://digital.csic.es/bitstream/10261/156198/1/Verhoef_et_al_2008.pdf [2021-12-29/2023-04-03

Wang Lei, Han Bing, Yuan Xinzhe, et al. 2018. A preliminary analysis of wind retrieval, based on GF-3 wave mode data. Sensors, 18(5): 1604. doi: 10.3390/s18051604

Yang Junxin, Han Bing, Zhong Lihua, et al. 2022. First assessment of GF3–02 SAR ocean wind retrieval. Remote Sensing, 14(8): 1880. doi: 10.3390/rs14081880

Yin Di, Han Bing, Sun Jili, et al. 2021. Calibration method to the impact of antenna scanning on TOPSAR. Journal of University of Chinese Academy of Sciences (in Chinese), 38(6): 791–799

Zhang Qingjun. 2017. System design and key technologies of the GF-3 satellite. Acta Geodaetica et Cartographica Sinica (in Chinese), 46(3): 269–277

Zhang Kangyu, Huang Jingfeng, Mansaray L R, et al. 2019a. Developing a subswath-based wind speed retrieval model for Sentinel-1 VH-Polarized SAR data over the ocean surface. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1561–1572. doi: 10.1109/TGRS.2018.2867438

Zhang Biao, Mouche A, Lu Yiru, et al. 2019b. A geophysical model function for wind speed retrieval from C-band HH-polarized synthetic aperture radar. IEEE Geoscience and Remote Sensing Letters, 16(10): 1521–1525. doi: 10.1109/LGRS.2019.2905578

Zhang Guosheng, Perrie W, Li Xiaofeng, et al. 2017. A hurricane morphology and sea surface wind vector estimation model based on C-band cross-polarization SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1743–1751. doi: 10.1109/TGRS.2016.2631663

Zhang Biao, Perrie W, Vachon P W, et al. 2012. Ocean vector winds retrieval from C-band fully polarimetric SAR measurements. IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4252–4261. doi: 10.1109/TGRS.2012.2194157

Zhong Lihua, Qiu Xiaolan, Han Bing, et al. 2019. ScanSAR radiometric correction and analysis of GaoFen-3. In: 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). Xiamen, China: IEEE, 1–4

Relative Articles

Supplements(0)

Cited By

Proportional views