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| Correction Method of Multispectral Satellite Images Based on Spaceborne Synchronous Atmospheric Parameters |
| XU Ling-ling1, 2, XIONG Wei2, YI Wei-ning2, QIU Zhen-wei2, LIU Xiao2, CUI Wen-yu2* |
1. University of Science and Technology of China, Hefei 230026, China
2. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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Abstract The atmospheric state varies significantly in terms of the temporal and spatial scales. The atmospheric correction of remote sensing satellite images is limited because it is difficult to dynamically obtain atmospheric parameters matching with the images to be corrected. As the civil optical remote sensing satellite with the highest spatial resolution in China, the Gao Fen Duo Mo satellite is equipped with the first civilian Synchronization Monitoring Atmospheric Corrector (SMAC). The SMAC onboard the GFDM satellite platform is capable of multispectral and polarization detection and can offer time-synchronized, and field-of-view overlapped atmospheric measurements to obtain atmospheric parameters synchronized with the main sensor. This study proposed a synchronous atmospheric correction method for high-spatial resolution image based on the atmospheric parameters retrieved from SMAC. Firstly, based on the principle of time synchronization, the original data of SMAC was processed to form the SMAC-Level1 product, combining with the auxiliary data of the main camera. Then, according to the SMAC-Level1 data, the SMAC pixels covered with cloud were discriminated, and the aerosol and water vapor parameters of the pixels without cloud coverage were retrieved to form the SMAC-L2 product. Finally, based on the 6SV radiative transfer model, the atmospheric radiometric correction and proximity effect correction were carried out on the remote sensing image from the GFDM satellite (Level1), and the surface reflectance product of the main camera (Level2) was obtained. In the experimental part, Syn-AC was applied to the remote sensing image from the GFDM satellite, and the image quality before and after the atmospheric correction was evaluated. Furthermore, the surface reflectance after the correction was compared with the ground-measured value to discuss the accuracy of the synchronous atmospheric correction method. In addition, the classical correction method FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method.The results show that the reflectance obtained from the corrected image of Syn-AC is in good agreement with the ground-measured value, and the mean absolute error is 0.012 2 (the mean absolute error of FLAASH is 0.027 4). The atmospheric correction method based on synchronous atmospheric parameters retrieved from SMAC has great potential in improving satellite image quality and remote sensing quantitative applications.
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Received: 2022-11-16
Accepted: 2023-05-06
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Corresponding Authors:
CUI Wen-yu
E-mail: cuiwenyu@aiofm.ac.cn
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[1] XU Xi-ru(徐希孺). Physics for Remote Sensing(遥感物理). Beijing: Peking University Press(北京: 北京大学出版社), 2006. 328.
[2] Wang Tao, Du Lili, Yi Weining, et al. Remote Sens. Environment, 2021，259: 112412.
[3] Kaufman Y J, Karnieli A, Tanré D. IEEE Transactions on, Geoscience and Remote Sensing, 2000, 38(1): 525.
[4] LI Zheng-qiang, CHEN Xing-feng, MA Yan, et al(李正强, 陈兴峰, 马 䶮, 等). Journal of Nanjing University of Information Science & Technology: Natural Science Edition(南京信息工程大学学报：自然科学版), 2018, 10(1): 6.
[5] Smirnov A, Holben B N, Eck T F, et al. Geophysical Research Letters, 2002, 29(23): 30-1.
[6] ZHA Yong, HE Jun-liang, CHENG Feng, et al(查 勇，贺军亮，程 峰，等). Atmospheric Aerosol Inversion and Remote Sensing for Pollution Environment Monitoring(大气气溶胶反演及其污染环境监测遥感). Beijing: Science Press(北京：科学出版社)，2019.
[7] Kaufman Y J, Sendra C. International Journal of Remote Sensing, 1988, 9：1357.
[8] Barazzetti L, Roncoroni F, Brumana R, et al. In Proceedings of the XXIII ISPRS Congress, Prague, Czech Republic，12-19 July, 2016. 429.
[9] XIA Tian, ZHANG Wen-hui(夏 天, 张文慧). Satellite Applications(卫星应用), 2020，(8): 70.
[10] HU Ya-dong, HU Qiao-yun, SUN Bin, et al(胡亚东, 胡巧云, 孙 斌, 等). Optics and Precision Engineering(光学精密工程), 2015, 23(3): 652.
[11] Li Zhengqiang, Hou Weizhen, Qiu Zhenwei, et al. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1.
[12] KANG Qing, YUAN Yin-lin, LI Jian-jun, et al(康 晴, 袁银麟, 李健军, 等). Acta Optica Sinica(光学学报), 2017, 37(3): 0312003.
[13] Jethva H, Satheesh S K, Srinivasan J，et al. Atmos. Chem. Phys., 2010, 10(21): 10399.
[14] Ciren P，Kondragunta S. J. Geophys. Res., Atmos., 2014, 119(8): 4770.
[15] Hall D K, Riggs G A, Salomonson V V. Remote Sens. Environment, 1995, 54(2): 127.
[16] Zheng F X，Li Z Q，Hou W Z，et al. J. Appl. Remote Sens., 2020, 14(1): 014516.
[17] Dubovik O，Herman M, Holdak A, et al. Atmospheric Meas. Techn., 2011, 4(5): 975.
[18] ZHAI Mo, YUAN Shuai(翟 墨, 原 帅). Heilongjiang Meteorology(黑龙江气象), 2016, 33(3): 20.
[19] Vermote E F, Kotchenova S. Journal of Geophysical Research: Atmospheres, 2008, 113(D23): D23S90.
[20] Ralf Bennartz，Jürgen Fischer. Remote Sensing of Environment, 2001, 78(3): 274.
[21] WANG Tao, ZHOU Nan, YI Wei-ning, et al(王 涛, 周 楠, 易维宁, 等). Acta Physica Sinica(物理学报), 2021, 70(13): 139101.
[22] WANG Qian, CHEN Xue, MA Jian-wen, et al(王 倩, 陈 雪, 马建文, 等). Acta Optica Sinica(光学学报), 2010, 30(11): 3342.
[23] WANG Tao, ZHOU Chuan-jie, YI Wei-ning, et al(王 涛, 周川杰, 易维宁, 等). Acta Optica Sinica(光学学报), 2021, 41(11): 1101002.
[24] TANG Xing, YI Wei-ning, DU Li-li, et al(汤 兴，易维宁，杜丽丽, 等). Acta Optica Sinica(光学学报), 2016, 36(2): 0228003.
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