|
|
|
|
|
|
Research on Remote Sensing Retrieval of Bohai Sea Transparency
Based on Sentinel-3 OLCI Image |
JIANG Ling-ling1, WANG Long-xiao1, 2 , WANG Lin2*, GAO Si-wen1, YUE Jian-quan1 |
1. College of Environment Science and Engineering, Dalian Maritime University,Dalian 116026, China
2. National Marine Environmental Monitoring Center, Dalian 116023, China
|
|
|
Abstract Transparency is one of the critical indicators for marine ecological environment monitoring, and it plays a vital role in the military, navigation, fishery, and other fields. Compared with other traditional ocean monitoring technologies, remote sensing technology has the advantages of long time sequence, extensive range, and near real-time acquisition of ocean information. Therefore, it is of great significance to the rational development and utilization of marine resources by using satellites to observe ocean transparency. This study used the in situ transparency data and the equivalent remote sensing reflectance data of the Sentinel-3 OLCI sensor to build the Bohai Sea transparency inversion model, which mainly included the single band method, the band ratio method, and the mixed band method. The model’s accuracy was verified with the in situ-satellite match-ups. It found that the best inversion model was the mixed band model with B6 (560 nm) and B7 (620 nm) as the sensitive factors. The coefficient of determination R2 was 0.68, the average relative error (MRE) was 15.93%, and the root means square error (RMSE) was 0.48 m. On this basis, combined with Sentinel-3 OLCI time-series images, we got the monthlyremote sensing products of Bohai transparency in 2020 and found that thetransparency showed obvious regional and seasonal characteristics. The transparency ranged from 0 m to 10 m. It was higher in July and August. The value of some areas could be deeper than 9 m. While it was relatively low in winter, the value was less than 2 m in January and February. At the same time, we also found that the higher transparency appeared in the central Bohai Sea and the coastal waters of Qinhuangdao, while the transparency was lower in Bohai Bay, Liaodong Bay, and Laizhou Bay throughout the year. The characteristic trend of transparency is inseparable from Bohai Sea coastal geological properties, the distribution of surrounding rivers, and coastal urban agglomerations and industrial ports. This research provided a reliable theoretical basis for remote sensing transparency estimation, and it was of great significance for monitoring the marine environment of the Bohai Sea.
|
Received: 2021-07-19
Accepted: 2021-10-27
|
|
Corresponding Authors:
WANG Lin
E-mail: lwang@nmemc.org.cn
|
|
[1] Tyler J E. Limnology and oceanography, 1968, 13(1): 1.
[2] XUE Yu-huan, XIONG Xue-jun, LIU Yan-qing(薛宇欢, 熊学军, 刘衍庆). Advances in Marine Science(海洋科学进展), 2015, 33(1): 38.
[3] GAO Lei, YAO Hai-yan, ZHANG Meng-meng, et al(高 磊, 姚海燕, 张蒙蒙, 等). Journal of Marine Sciences(海洋学研究), 2017, 35(3): 79.
[4] Zeng S, Lei S, Li Y, et al. Remote Sensing, 2020, 12(9): 1516.
[5] ZHOU Yi, LIU Yao, TIAN Shu-fang(周 毅, 刘 瑶, 田淑芳). Spacecraft Engineering(航天器工程), 2020, 29(6): 155.
[6] Lee Z, Shang S, Qi L, et al. Remote Sensing of Environment, 2016, 177: 101.
[7] YU Ding-feng, ZHOU Yan, XING Qian-guo, et al(禹定峰, 周 燕, 邢前国, 等). Marine Environmental Science(海洋环境科学), 2016, 35(5): 774.
[8] ZHANG Chun-gui, ZENG Yin-dong(张春桂, 曾银东). Journal of Meteorology and Environment(气象与环境学报), 2015, 31(2): 73.
[9] WU Ding, YE Fa-wang, QIU Jun-ting, et al(武 鼎, 叶发旺, 邱骏挺, 等). Technology and Economic Guide(科技经济导刊), 2019, 27(15): 6.
[10] Mograne M, Jamet C, Loisel H, et al. Remote Sensing, 2019, 11(6): 668.
[11] Kyryliuk D, Kratzer S. Sensors, 2019, 19(16): 3609.
[12] Jerlov N G. ICES Journal of Marine Science, 1977, 37(3): 281.
[13] CONG Pi-fu, QU Li-mei, HAN Geng-chen, et al(丛丕福, 曲丽梅, 韩庚辰, 等). Advances in Earth Science(地球科学进展), 2011, 26(3): 295.
|
[1] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[2] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[3] |
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2. Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3269-3274. |
[4] |
WANG Ge1, YU Qiang1*, Yang Di2, NIU Teng1, LONG Qian-qian1. Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2572-2578. |
[5] |
FENG Tian-shi1, 2, 3, PANG Zhi-guo1, 2, 3*, JIANG Wei1, 2, 3. Remote Sensing Retrieval of Chlorophyll-a Concentration in Lake Chaohu Based on Zhuhai-1 Hyperspectral Satellite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2642-2648. |
[6] |
YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min*. Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 866-872. |
[7] |
NIU Teng1, 3, LU Jie1, 2*, YU Jia-xin4, WU Ying-da5, LONG Qian-qian3, YU Qiang3. Research on Inversion of Water Conservation Distribution of Forest Ecosystem in Alpine Mountain Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 530-536. |
[8] |
LONG Qian-qian1, ZHOU Ren-hao2, YUE De-peng1, NIU Teng1, MAO Xue-qing1, WANG Peng-chong3, YU Qiang1*. Research on Inversion of Water Conservation Capacity of Forest Litter in Yarlung Zangbo Grand Canyon Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 229-235. |
[9] |
ZHANG Zi-han1, YAN Lei1,2, LIU Si-yuan1, FU Yu1, JIANG Kai-wen1, YANG Bin3, LIU Sui-hua4, ZHANG Fei-zhou1*. Leaf Nitrogen Concentration Retrieval Based on Polarization Reflectance Model and Random Forest Regression[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2911-2917. |
[10] |
PANG Shu-na1, ZHU Wei-ning1*, CHEN Jiang2, SUN Nan3, HUANG Li-tong1, ZHANG Yu-sen1, ZHANG Ze-liang1. Using Landsat-8 to Remotely Estimate and Observe Spatio-Temporal Variations of Total Suspended Matter in Zhoushan Coastal Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3826-3832. |
[11] |
ZOU Bin, TU Yu-long, JIANG Xiao-lu, TAO Chao, ZHOU Mo, XIONG Li-wei. Estimation of Cd Content in Soil Using Combined Laboratory and Field DS Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3223-3231. |
[12] |
FENG Hai-ying1, FENG Zhong-ke1*, FENG Hai-xia2. One New Method of PM2.5 Concentration Inversion Based on Difference Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3012-3016. |
[13] |
YANG Liu1,2, FENG Zhong-ke1*, YUE De-peng1, SUN Jin-hua3. Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2140-2145. |
[14] |
MA Wei-wei1, GONG Cai-lan1*, HU Yong1, WEI Yong-lin2, LI Long3, LIU Feng-yi1, MENG Peng1 . Hyperspectral Remote Sensing Estimation Models for Pasture Quality [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2851-2855. |
[15] |
HUANG Jue1, CHEN Xiao-ling1, CHEN Li-qiong1*, ZHANG Li1, 2 . Particles Size Distribution and Its Influence on Remote Sensing Retrieval of Turbid Poyang Lake [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(11): 3085-3089. |
|
|
|
|