Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation

doi:10.1080/01431161.2012.663112

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋物理与遥感研究室

	Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation
	Cui, Ting-Wei 1; Zhang, Jie 1; Sun, Li-E 1; Jia, Yong-Jun 1; Zhao, Wenjing 2,3; Wang, Zong-Ling 1; Meng, Jun-Min 1
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2012
卷号	33 期号:17 页码:5513-5527
ISSN号	0143-1161
DOI	10.1080/01431161.2012.663112
英文摘要	A massive floating green macroalgae bloom (GMB) has occurred for several years consecutively in the Yellow Sea since 2007. In view of the rapid growth of green macroalgae, early detection of its patches at first appearance by satellite imagery is of importance, and the central issue is the selection of appropriate satellite data. As a first step towards this goal, based on quasi-synchronous satellite images of HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting) charge-coupled devices (CCDs), Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) and TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), GMB monitoring abilities by these data were compared. The average percentage difference (APD) of the GMB areas derived by ASAR and CCD was less than 15%, which may be partly attributed to the inability of synthetic aperture radar (SAR) data to detect macroalgae suspended beneath the sea surface. The macroalgae area extracted by MODIS was over two times of that extracted by CCD, which was mainly explained by the difference in their spatial resolutions (250 vs 30 m). The effects of the configuration of sensor bands and the aerosol optical properties on the comparison result were found to be negligible, and the underlying reason is analysed by atmosphere radiative transfer modelling. With satellite images, the drifting velocity of macroalgae patches was estimated to be about 0.21 m s(-1), which was in agreement with the surface current field numerically simulated by the Hybrid Coordinate Ocean Model (HYCOM). It indicates that numerical modelling can aid in deduction of the situation of the patches when satellite data are not available, and on the other hand, satellite data can be used to estimate sea-surface currents through monitoring the movement of green algae. By a comprehensive comparison of available satellite data in operation, for the early detection of macroalgae patches and warning of a massive bloom, CCD data from the HJ-1A/B constellation was preferred, with 30 m spatial resolution, 700 km swath width and 2 day revisiting period. SAR data may be an effective supplement, which can avoid the effects of bad weather (cloud, fog and haze) on optical satellite monitoring.
电子版国际标准刊号	13665901
资助项目	ITF[ITS/058/09FP]
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000302169900012
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/4153]
专题	业务部门_海洋物理与遥感研究室
作者单位	1.State Ocean Adm, Inst Oceanog 1, Qingdao, Peoples R China; 2.Chinese Acad Sci, S China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China; 3.Chinese Acad Sci, Grad Univ, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cui, Ting-Wei,Zhang, Jie,Sun, Li-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.
APA	Cui, Ting-Wei.,Zhang, Jie.,Sun, Li-E.,Jia, Yong-Jun.,Zhao, Wenjing.,...&Meng, Jun-Min.(2012).Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation.INTERNATIONAL JOURNAL OF REMOTE SENSING,33(17),5513-5527.
MLA	Cui, Ting-Wei,et al."Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation".INTERNATIONAL JOURNAL OF REMOTE SENSING 33.17(2012):5513-5527.