基于高分四号卫星的黄海绿潮漂移速度提取研究

陈晓英; 张杰; 崔廷伟; 宋平舰

doi:10.3969/j.issn.0253-4193.2018.01.004

基于高分四号卫星的黄海绿潮漂移速度提取研究

doi: 10.3969/j.issn.0253-4193.2018.01.004

国家海洋局第一海洋研究所, 山东青岛 266061

基金项目: 国家自然科学基金（41476159，41506203，41706209，41476101）；中韩海洋科学共同研究中心项目（PI-2017-3）；海洋公益性行业科研专项（2013418025-2）。

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出版历程
- 收稿日期: 2017-04-10

Extraction of the green tide drift velocity in the Yellow Sea based on GF-4

First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

摘要

摘要: 静止轨道卫星高分四号（GF-4）具有高时间分辨率（20 s）和高空间分辨率（50 m）的独特优势。为了挖掘GF-4卫星在海洋灾害监测中的应用潜力，本文基于2016年6月25日1天4景的GF-4卫星影像，利用最大相关系数法（MCC），开展了黄海绿潮漂移速度提取研究，分析了海面风场、潮汐等对绿潮漂移的影响。研究发现：（1）MCC方法可高精度自动追踪GF-4影像中绿潮的分钟级（8~9 min）位置变化，绿潮漂移速率和方向的相对偏差分别为11%和5%；当2景GF-4影像的成像时间间隔增大至小时级（如6 h）时，随着绿潮斑块形状的改变，MCC方法绿潮自动追踪的准确性下降。（2）绿潮在1天之中的漂移速率和方向可发生显著变化，当日上午9时黄海绿潮漂移速率均值为（0.36±0.13）m/s，方向以东南向为主，至15时，绿潮漂移速率显著增加至（0.69±0.12）m/s，方向变为东北偏北。（3）绿潮漂移速度与海面风速的相关系数为0.74，绿潮漂移方向为风向偏右；绿潮的向岸、离岸运动与相应时刻的涨、落潮具有较好的对应关系。GF-4卫星数据可为绿潮快速漂移的高精度监测提供数据支撑。
- 高分四号(GF-4) /
- 绿潮 /
- 遥感 /
- 最大相关系数(MCC) /
- 漂移速度
Abstract: The geostationary optical satellite GF-4 has the unique advantages of high temporal resolution (20s) and high spatial resolution (50m). In order to explore GF-4's potential in ocean disaster monitoring, the maximum cross correlation method (MCC) was applied to four GF-4 satellite images on June 25^th, 2016 to extract the drifting velocity of the green tide in the Yellow Sea, and the influences of wind and tide on the green tide movement were analyzed. (1) The MCC method is shown to be capable of automatically tracking the green tide patches movement on the minute scale (8-9 min) in the GF-4 images with a high accuracy, with the absolute percentage difference (APD) of velocity magnitude and direction of 11% and 5%, respectively. When the temporal interval between the two GF-4 images increased to several hours (e.g., 6 h), the accuracy of MCC tracking decreased due to the obvious shape changes of green tide patch. (2) The drifting velocity of the green tide during the daytime can change significantly. At 9:00 am, the green tide patches had an average velocity of (0.36±0.13) m/s and moved southeast. But at 3:00 pm, the velocity increased to (0.69±0.12) m/s and the patches moved toward northwest direction. (3) The drifting velocity of green tide had a close correlation with wind speed (r=0.74); and green tide patches' movement direction was also in good accordance with local tide cycle. The GF-4 data can provide data support for accurate monitoring of green tide short-term movement.
- GF-4 /
- green tide /
- remote sensing /
- MCC /
- drift velocity

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参考文献(17)

卢健, 张启龙, 李安春. 苏北沿岸流对浒苔暴发及漂移过程的影响[J]. 海洋科学, 2014, 38(10):83-89. Lu Jian, Zhang Qilong, Li Anchun. The influence of Subei coastal current on the outbreak and drift of Enteromorpha prolifera[J]. Marine Sciences, 2014, 38(10):83-89.

Cui T W, Zhang J, Sun L E, et al. Satellite monitoring of massive green macroalgae bloom (GMB):imaging ability comparison of multi-source data and drifting velocity estimation[J]. International Journal of Remote Sensing, 2012, 33(17):5513-5527.

夏深圳. 基于遥感的黄海浒苔漂移速度与驱动机制研究[D]. 南京:南京大学, 2015. Xia Shenzhen. Distribution and driving mechanism of the drift velocity of Ulva prolifera in the Yellow Sea based on remote sensing[D]. Nanjing:Nanjing University, 2015.

Turiel A, Nieves V, Garcia-Ladona E, et al. The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines[J]. Ocean Science, 2009, 5(4):447-460.

Crocker R I, Matthews D K, Emery W J, et al. Computing coastal ocean surface currents from infrared and ocean color satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(2):435-447.

Girard-Ardhuin F, Ezraty R. Enhanced Arctic sea ice drift estimation merging radiometer and scatterometer data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7):2639-2648.

Lavergne T, Eastwood S, Teffah Z, et al. Sea ice motion from low-resolution satellite sensors:An alternative method and its validation in the Arctic[J]. Journal of Geophysical Research:Oceans, 2010, 115(C10):C005958.

Ciappa A, Pietranera L, Coletta A, et al. Surface transport detected by pairs of COSMO-SkyMed ScanSAR images in the Qingdao region (Yellow Sea) during a macro-algal bloom in July 2008[J]. Journal of Marine Systems, 2010, 80(1/2):135-142.

蔡晓晴. 基于静止轨道海洋水色卫星数据的绿潮遥感探测方法和逐时变化特征研究[D]. 青岛:中国海洋大学, 2014. Cai Xiaoqing. Remote sensing detection and diurnal variation research of green macro-algae bloom by geostationary ocean color imager[D]. Qingdao:Ocean University of China, 2014.

李永祺. 中国区域海洋学——海洋环境生态学[M]. 北京:海洋出版社, 2012. Li Yongqi. Regional Oceanography of China Seas-Marine Environmental Ecology[M]. Beijing:China Ocean Press, 2012.

孙湘平. 中国近海及毗邻海域水文概况[M]. 北京:海洋出版社, 2016. Sun Xiangping. Hydrology Situation in China Offshore and Adjacent Sea Areas[M]. Beijing:China Ocean Press, 2016.

刘亚豪. 黄海冬季环流动力学研究及中国近海水色遥感大气校正改进[D]. 青岛:中国海洋大学, 2011. Liu Yahao. Study on winter circulation dynamics of the Yellow Sea and improvement in remote sensing atmospheric correction in China coastal area[D]. Qingdao:Ocean University of China, 2011.

马艳, 郭丽娜, 黄容, 等. 2008-2010年青岛近海浒苔暴发气象条件及其漂移特征[J]. 气象与环境学报, 2015, 31(4):89-96. Ma Yan, Guo Lina, Huang Rong, et al. Meteorological conditions of Enteromorpha prolifera outbreak and its movement in Qingdao seashore from 2008 to 2010[J]. Journal of Meteorology and Environment, 2015, 31(4):89-96.

Lee J H, Pang I C, Moon I J, et al. On physical factors that controlled the massive green tide occurrence along the southern coast of the Shandong Peninsula in 2008:A numerical study using a particle-tracking experiment[J]. Journal of Geophysical Research:Oceans, 2011, 116(C12):C12036.

乔方利, 王关锁, 吕新刚, 等. 2008与2010年黄海浒苔漂移输运特征对比[J]. 科学通报, 2011, 56(18):1470-1476. Qiao Fangli, Wang Guansuo, Lv Xingang, et al. Drift characteristics of green macroalgae in the Yellow Sea in 2008 and 2010[J]. Chinese Science Bulletin, 2011, 56(21):2236-2242.

Ekman V W. On the influence of the earth's rotation on ocean currents[J]. Arkiv Mater N. Astr. Fysik Bd, 1905, 2:1-53.

马洪余, 乔方利, 戴德君. 不同垂向分布特征的垂直黏性系数对经典定常Ekman螺旋结构的影响[J]. 中国科学:地球科学, 2014, 44(2):367-376. Ma Hongyu, Qiao Fangli, Dai Dejun. The effects of vertical viscosity coefficients with different distribution characteristics on classical Ekman spiral structure[J]. Chinese Science:Earth Science, 2014, 57(4):693-702.

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