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Comparative Analysis of GF-1 and GF-6 WFV Images in Suspended
Matter Concentration Inversion in Dianchi Lake |
ZHAO Ran1, YANG Feng-yun1*, MENG Qing-yan2, 3, KANG Yu-peng2, 4, ZHENG Jia-yuan1, HU Xin-li2, YANG Hang2 |
1. School of Civil Engineering,University of Science and Technology Liaoning,Anshan 114051,China
2. The Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101,China
3. College of Resources and Environment, University of Chinese Academy of Sciences,Beijing 100049,China
4. School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China
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Abstract Total suspended matter (TSM) is one of the important parameters of water environment assessment and an important index of remote sensing water retrieval. GF-1/WFV and GF-6/WFV are free and open satellite data of the gaofen series, which are widely used in remote sensing monitoring. However, there are few studies on the applicability of the new bands of GF-6/WFV in water quality parameter inversion. This study takes Dianchi Lake in Yunnan province as the research area, based on the testing data synchronization with water transit (or similar) of the phase of GF-1/WFV and GF-6/WFV remote sensing image using statistics analysis method to the same band (blue, green, red and near-infrared) consistency analysis, regression method based on using the experience of the TSM inversion models of the two kinds of data, respectively, The model with GF-6/WFV added bands were compared with the model constructed by GF-1/WFV. The optimal model was applied to six GF-6/WFV images in 2020 to obtain the TSM distribution map of Dianchi Lake. The results show that the correlation coefficients of GF-1/WFV and GF-6/WFV in blue, green, red and near infrared bands are 0.98, 0.98, 0.97 and 0.99, respectively. The apparent reflectance of the two kinds of data is highly consistent. The inversion accuracy of GF-1/WFV difference model “B2+B4-B1” based on blue, green and near-infrared red bands is high, and the root means square error of model inversion is 6.35 mg·L-1, and the average absolute percentage error is 23.60%. The ratio model “1/B5+B6” constructed by GF-6/WFV based on near-infrared, red-edge 1 and red-edge 2 bands has a high inversion accuracy. Model inversion’s root mean square error (RMSE) is 3.07 mg·L-1, and the mean absolute percentage error (MAPE) is 20.65%. By comparing the difference model “B1-B4” constructed by GF-1/WFV with “B5-B4” constructed by GF-6/WFV, it is found that the root means square error of the latter is reduced by 2.61 mg·L-1, and the average absolute percentage is reduced by 32.33%. The experiment shows that the inversion effect of the model with the red-edge band is better than other models. The TSM distribution map of Dianchi Lake in 2020 was obtained using the modeling formula. The TSM in Dianchi Lake varied from 4 to 45 mg·L-1, with an average value of 18.23 mg·L-1. The overall spatial distribution showed a trend of heavy distribution in the north and light distribution in the south, and the time distribution of TSM in Dianchi Lake showed an upward and downward trend. This study can not only provide a reference for the sensor band setting of lake water quality monitoring but also provide technical support for water quality remote sensing monitoring by the water resources supervision department of Dianchi Lake.
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Received: 2021-12-03
Accepted: 2022-03-24
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Corresponding Authors:
YANG Feng-yun
E-mail: yfy196507@163.com
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[1] Liqiao Tian, Onyx W H Wai, Xiaoling Chen, et al. International Journal of Remote Sensing, 2016, 37(14): 3400.
[2] Shi K, Zhang Y, Zhu G, et al. Remote Sensing of Environment, 2015, 164: 43.
[3] Kuhn Catherine, Aline de M V, Nick W, et al. Remote Sensing of Environment: An Interdisciplinary Journal, 2019, 224: 104.
[4] Molkov A A, Fedorov S V, Pelevin V V, et al. Remote Sensing. 2019; 11(10): 1215.
[5] da Rocha Júnior Rodrigo Lins, David Duarte Cavalcante Pinto, Fabricio Daniel dos Santos Silva, et al. Water, 2021, 13(12): 1613.
[6] Feng Lian, Hou Xuejiao, Zheng Yi. Remote Sensing of Environment, 2019, 221: 675.
[7] ZHANG Yi-bo, ZHANG Yun-lin, ZHA Yong, et al(张毅博,张运林,查 勇,等). Environmental Science(环境科学), 2015, 36(1): 56.
[8] Cao Hongye, Ling Han, Wei Li, et al. Water Environment Research: a Research Publication of the Water Environment Federation, 2020, 93(4): 582.
[9] Liu Jia, Liu Jiahang, He Xianqiang, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(7): 2170.
[10] LI Jian-hong, HUANG Chang-chun, ZHA Yong, et al(李建鸿,黄昌春,查 勇,等). Environmental Science(环境科学), 2021, 42(11): 5239.
[11] Na Z L, Yao H M, Chen H Q, et al. Sustainability, 2021, 13(9): 4649.
[12] Qiong Chen, Zhou Bin, Yu Zhifeng, et al. Remote Sensing, 2021, 13(7): 1339.
[13] LI Hong-zhe, WANG Shi-jie, LI Cheng-ming(李弘哲,王世杰,李成名). Environmental Engineering(环境工程), 2022, 40(4): 196.
[14] Guo L, Liu Y, He H, et al. Optik, 2021, 231: 166414.
[15] YIN Zi-yao, LI Jun-sheng, FAN Hai-sheng, et al(殷子瑶,李俊生,范海生,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(2): 494.
[16] DING Bo, LI Wei, HU Ke(丁 波, 李 伟, 胡 克). Remote Sensing for Natural Resources(国土资源遥感), 2022, 34(1): 10.
[17] WANG Zi-yan, REN Chao, LIANG Yue-ji, et al(王子彦,任 超,梁月吉,等). Bulletin of Surveying and Mapping(测绘通报), 2021,(6): 6.
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