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Journal of Tropical Oceanography >

2022 , Vol. 41 >Issue 3: 46 - 53

DOI: https://doi.org/10.11978/2021088

Oceanographic Research and Observation

Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image

  • MENG Miaomiao , 1, 2, 3 ,
  • ZHENG Xiangyang 1, 2, 3 ,
  • XING Qianguo , 1, 2, 3 ,
  • LIU Hailong 1, 2, 3
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  • 1. CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
  • 2. Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
XING Qianguo. email:

Copy editor: LIN Qiang

Received date: 2021-07-09

  Revised date: 2021-09-02

  Online published: 2021-09-19

Supported by

National Natural Science Foundation of China(42076188)

National Natural Science Foundation of China(41676171)

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA1906000)

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060203)

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060501)

The Instrument Developing Project of the Chinese Academy of Sciences(YJKYYQ20170048)

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Abstract

Satellite images are valuable data sources for monitoring floating green macroalgae on the sea surface. However, there are large errors in green macroalgae coverage derived on mixed pixels. It is thus important to solve the problem of sub-pixel coverage of green macroalgae, for precise monitoring of benthic green macroalgae in coastal area. In this paper, retrieval models were established to link sub-pixel coverage of green macroalgae with vegetation indexes and reflectance of characteristic bands by analyzing spectral characteristics of green macroalgae from the Landsat images, based on the results of green macroalgae coverage derived from unmanned aerial vehicle (UAV). The results show excellent linear relationships between the reflectance of blue, green, and red bands and the sub-pixel coverage of green macroalgae, and the reflectance decreases monotonically with increasing sub-pixel coverage. These three models were verified, and the results show that the model based on the reflectance of green band was more accuracy than the other indexes or index combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE) values of 0.92, 0.07, and 10.85%, respectively. Hence, we provide a model that can estimate the sub-pixel coverage of green macroalgae, and realize the precise monitoring of the coverage of green macroalgae extracted from Landsat images.

Key words： unmanned aerial vehicle (UAV); satellite image; green macroalgae; sub-pixel coverage

Cite this article

MENG Miaomiao , ZHENG Xiangyang , XING Qianguo , LIU Hailong . Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image[J]. Journal of Tropical Oceanography, 2022 , 41(3) : 46 -53 . DOI: 10.11978/2021088

近二十年来, 由大型藻类暴发而导致的海洋灾害在世界范围内呈增长趋势(Morand et al, 2005; Ye et al, 2011; Smetacek et al, 2013), 不仅危害海洋生态环境(Valiela et al, 1997), 还影响海上交通、沿海地区的观光旅游等活动(邢前国 等, 2011)。在中国,大型藻类灾害受到重视始自2008年夏季, 时值奥运会帆船比赛前, 青岛沿岸暴发了以漂浮大型绿藻浒苔(U. prolifera)为主的绿潮灾害。国外, 类似的海洋大型藻类灾害也在增加, 如法国Brittany的绿藻(Schreyers et al, 2021)、墨西哥湾与大西洋的漂浮马尾藻带(Wang et al, 2019; Gower et al, 2019)。
在我国近海与海岸带地区, 黄海夏季由紫菜养殖导致的大规模漂浮浒苔(Liu et al, 2009; Xing et al, 2019)以及冬春季黄海、东海的漂浮马尾藻铜藻(Xing et al, 2017; Qi et al, 2017)因规模较大受到了较多关注; 以底栖为主的大型藻也常在本地局部地区形成灾害或环境污染(Xing et al, 2016a; Han et al, 2019), 由于在沿海分布广泛, 其生态环境效应与资源管理问题不容忽视。
孔石莼(U. pertusa)是一种潮间带与浅海常见的绿色大型藻, 其出现与水体的高度富营养化密切相关。孔石莼在浅海以底栖为主, 能在非附着的条件下生长, 附着气泡较多时亦可在水面呈漂浮状。孔石莼生长迅速, 生物量大, 常因暴发、过度聚集而形成生态灾害或污染。孔石莼也有着正面的生态功能与资源价值, 在生长过程中可吸收大量营养盐净化水体、吸收二氧化碳, 也可作为食材、动物饲料, 或者深加工作为药物、保健品等原材料(刘岩 等, 2010)。
使用卫星影像能够大范围、同步监测绿藻在海面上的分布状态, 为绿潮的防控提供先决条件。微波影像可以根据粗糙度信息识别出漂浮在海面的绿藻(蒋兴伟 等, 2009; Shen et al, 2014), 而热红外影像可以根据不同的热发射率将绿藻从海水中识别出来(Xing et al, 2015)。监测绿藻最常用的是光学影像, 因为绿藻与陆上植被具有相似的光谱特性, 可见光波段反射率低, 红光波段和蓝光波段出现吸收谷, 近红外波段出现反射峰(樊彦国 等, 2015), 具有典型的“红边效应”(安德玉 等, 2018)。早期识别绿藻的遥感算法有差值植被指数(difference vegetation index, DVI)、比值植被指数(ratio vegetation index, RVI)、归一化植被指数(normalized difference vegetation index, NDVI)(Cui et al, 2012; Xing et al, 2018), 其中NDVI在光学影像绿藻提取中应用最为广泛(Li et al, 2018b)。由于植被指数具有一定局限性, 又有学者提出适用于绿藻提取的指数, 例如漂浮藻类指数(floating algae index, FAI)(Hu, 2009; Wang et al, 2016)和虚拟基线漂浮藻类指数(virtual-baseline floating macroalgae height, VB-FAH)(Xing et al, 2016a), 这些指数对太阳光、气溶胶变化等的影响敏感性较低, 更适用于海面绿藻的提取。
由于海面绿藻斑块的大小可以从几厘米到几公里不等(Li et al, 2018a), 传统的阈值提取方法属于“硬分类”, 由于混合像元的影响, 在估算绿藻面积时存在一定误差。针对混合像元分解的绿潮面积精细化提取, 有学者基于同步的高分辨率和低空间分辨率卫星影像间分析了混合像元分解(丁一 等, 2015), 还有学者通过实测绿藻光谱, 分析绿藻提取指数对绿藻表面覆盖度的响应, 并建立混合像元绿藻覆盖度估算模型(Qi et al, 2016; Li et al, 2018b)。
本文针对孔石莼展开研究, 利用实测同步厘米级分辨率无人机数据提取的绿藻面积, 建立基于Landsat-8数据计算的植被指数(NGRDI、NDVI和VB-FAH)以及单波段反射率对绿藻亚像素覆盖度的多个反演模型, 并对模型进行了验证与评价。

1 数据和方法

1.1 数据及数据预处理

1.1.1 卫星与无人机数据采集与处理

本文研究区域位于渤海湾沿岸, 渤海为陆表海, 平均水深18m (Xing et al, 2016b), 研究区域现场照片如图1a, 整个研究区海面分布有大量孔石莼, 孔石莼在海里浅水区呈现沉水非定生状态, 退潮时呈干出状态(图1b、c)。本文采用准同步的无人机影像和Landsat TM卫星影像开展研究, 无人机影像采集时间为2020年10月23日11时8分, Landsat卫星过境时间为10时49分。
图1 研究区现场照片(a)及沉水状态(b)和低潮干出状态(c)下的孔石莼

Fig. 1 Field photo of the study area (a) and Ulva pertusa under submerged condition (b), as well as under emerged condition during low tide (c)

图2 孔石莼与海水的光谱反射率

粗体黑线表示绿藻光谱曲线对应landsat-8第二到第五波段重采样的平均值, “┬”表示标准差

Fig. 2 Reflectance spectra of the Ulva pertusa and sea water, with the bold black line showing the mean reflectance of Ulva pertusa corresponding to the second to the fifth band of Landsat-8. “┬” is for standard deviation (S.D.)

无人机正射图像采集使用大疆御Mavic 2为搭载平台, 其搭载的传感器为1/2.3英寸CMOS, 有R、G、B三个波段。使用Pix4Dmapper软件对自带GPS定位信息的无人机影像进行快速校正、拼接处理, 对于无法拼接的无特征点图像进行手动拼接处理, 输出满足遥感解译需求的影像产品, 拼接后的无人机影像如图3c。对Landsat-8 OLI 数据首先进行辐射定标处理, 再使用FLAASH大气校正模型以获取反射率数据, 最后进行裁剪处理。为保证地理配准的精度, 将处理后的无人机影像与Landsat-8影像进行比较, 选取15个控制点, 发现配准结果较好, RMSE为0.51。
图3 研究区遥感影像及绿藻提取结果

a. Landsat-8图像; b. 无人机影像重采样; c. 无人机影像; d. 红波段灰度图; e. 无人机影像绿藻提取结果

Fig. 3 Remote sensing images of the study area and the results of green macroalgae extraction.

(a) Landsat-8 image; (b) Unmanned aerial vehicle (UAV) image resampling; (c) UAV image; (d) gray image of red band; (e) green macroalgae information extracted from UAV image

1.1.2 高光谱数据采集与处理

为了解孔石莼的光谱特征, 帮助选择参与建模的指数, 于2020年11月11日测量了研究区域不同状态下孔石莼的反射率。采用的光谱仪为ASD HandHeld2, 波长范围为325~1075nm, 光谱分辨率为1nm; 先测参考板的辐射亮度值, 再测海水与不同状态下孔石莼的辐射亮度值, 每组重复测量3次取平均值, 反射率计算公式为
$R(\lambda )=\frac{L(\lambda )}{LP(\lambda )}*\rho (\lambda )$
式中 $R(\lambda )$代表被测物体的反射率, $L(\lambda )$代表地物辐射亮度值, $LP(\lambda )$代表参考板辐射亮度值, $\rho (\lambda )$是参考板的反射率。
所测得的海水与孔石莼的反射光谱曲线如图2所示。绿藻的光谱特征为在可见光波段反射率低, 在近红外波段反射率高, 与海水光谱的差异明显。将绿藻光谱按照Landsat-8 OLI传感器第二到四波段的光谱响应函数进行重采样处理, 发现其在蓝、绿、红、近红外波段的反射率低于海水, 在近红外波段反射率高于海水。

1.2 方法

1.2.1 无人机影像绿藻提取

在无人机影像上选取一块大小为417×435像素的样本区, 使用动态阈值法对样本区进行绿藻提取, 以人工目视解译结果为基准, 对比提取结果, 进行精度验证。
由于无人机影像只有红、绿、蓝三个波段, 因此选取4种典型植被指数: 归一化绿红差异指数(normalized green-red difference index, NGRDI)、归一化绿蓝差异指数(normalized green-blue difference index, NGBDI)、红绿比值指数(red green ratio index, RGRI)、过绿指数(excess green, ExG)做绿藻提取研究, 其计算公式如下:
$\text{NGRDI}=\frac{G-R}{G+R}$
$\text{NGBDI}=\frac{G-B}{G+B}$
$\text{RGDI}=\frac{R}{G}$
$\text{ExG}=2G-R-B$
式中R、G、B分别表示红波段、绿波段和蓝波段像素值。

1.2.2 卫星影像绿藻覆盖面积与植被指数关系建模

(1) 植被指数构建
将无人机影像上绿藻的提取结果以30m×30m的矩形为单位建立网格, 使之对应 Landsat-8 卫星影像的每个像素。统计每个像素内绿藻覆盖面积除以该像素的总面积(900m2), 作为每个混合像元的大型绿藻覆盖度(portion of macroalgae, POM)。根据研究区特点选取POM接近1的像素作为典型绿藻像素, POM接近0的像素作为典型海水像素, 端元所在位置如图3a彩色标注的像素(绿色表示典型的绿藻像素, 红色表示典型的海水像素)。分析其光谱曲线(图4)发现, 在可见光波段, 海水的反射率值高于绿藻, 在近红外波段, 海水反射率值降低, 而绿藻反射率值增高。有明显区分特征的波段是蓝、绿、红、近红外波段。
图4 典型绿藻像素反射率光谱曲线(a)与典型海水像素反射率光谱曲线(b)

Fig. 4 Reflectance spectral curve of typical green macroalgae pixels (a) and reflectance spectral curve of typical sea water pixels (b)

建立Landsat卫星影像混合像元POM与光谱值之间的线性关系: y=ax+b, y为POM, x选取7种指数参与建模分析, 分别为蓝波段、绿波段、红波段、近红外波段的反射率与NGRDI、NDVI、VB-FAH, 其计算公式如下:
$\text{NDVI}={\left( {{R}_{\text{NIR}}}-{{R}_{\text{RED}}} \right)}/{\left( {{R}_{\text{NIR}}}+{{R}_{\text{RED}}} \right)}\;$
$\begin{align} & \text{VB}-\text{FAN}=\left( {{R}_{\text{NIR}}}-{{R}_{\text{GREEN}}} \right)+\left( {{R}_{\text{GREEN}}}-{{R}_{\text{RED}}} \right) \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ {\left( {{\lambda }_{\text{NIR}}}-{{\lambda }_{\text{GREEN}}} \right)}/{\left( \text{2}{{\lambda }_{\text{NIR}}}-{{\lambda }_{\text{RED}}}-{{\lambda }_{\text{GREEN}}} \right)}\; \\ \end{align}$
式中R为反射率, 下标NIR、RED、GREEN分别表示近红外、红、绿波段。
(2) 模型建立与验证
在卫星图像上选取两块同样大小的15×12个像素组成的区域作为样本区与验证区, 如图3a, 为避免配准时的精度偏差, 以3×3个像素为窗口, 计算每个窗口内的平均POM与每个窗口内包含的9个像素的平均光谱反射率,在样本区域建立POM与NGRDI、NDVI、VB-FAH、蓝波段反射率、绿波段反射率、红波段反射率、近红波段反射率的回归模型, 选取有较好线性关系的模型公式在验证区域进行验证, 对反演模型拟合的POM结果与无人机影像提取结果拟合的POM进行对比分析, 选取最好的反演模型应用到整片无人机覆盖区域。

2 结果与讨论

2.1 基于无人机影像的绿藻提取结果

在无人机影像上均匀选取一定数量的绿藻与海水样本点, 其地表像元亮度值(digital number, DN)均值曲线如图5a所示。发现绿藻在红波段的DN值明显低于海水, 因此使用动态阈值法提取绿藻时选用红波段DN值与4种植被指数。为了确定基于此研究区域的无人机影像提取绿藻的最佳方法, 选取一块样本区, 以目视解译的结果作为绿藻提取的真实结果, 对比分析5种指数的动态阈值法提取的结果。
图5 海面上不同地物光谱曲线(a)与样本区绿藻监测结果(b)

Fig. 5 Spectral curve of different features on the sea (a) and monitoring results of green macroalgae of the sample area (b)

不同提取方法的提取结果如图5b所示, 从图中可以看出, 使用ExG指数提取的结果噪点最多。从表1可以看出, 监测精度最高的是红波段DN值, 提取一致性达到94%, Kappa为0.88, F1 score为0.92。且红波段灰度图(图3d)可以反映出无人机影像单个像素的POM, 为便于之后进行更精细的提取, 使用红波段DN值对无人机影像进行绿藻提取, 最终提取结果如图3e所示。
表1 不同绿藻提取方法精度对比

Tab. 1 Comparation on accuracy of green macroalgae extraction with different methods

动态阈值法 提取一致性 Kappa F1 score
NGRDI 89% 0.72 0.83
NGBDI 83% 0.68 0.79
RGRI 79% 0.62 0.74
ExG 77% 0.38 0.65
红波段DN值 94% 0.88 0.92

2.2 卫星影像建模结果讨论

样本区域的建模结果表明, POM与可见光三个波段的反射率线性关系最好, 其R2都大于0.9, 与近红外波段的线性关系最差。3个指数中, POM与NDVI、VB-FAH的线性关系较好, 与NGRDI的关系较差(图6)。
图6 样本区不同指数与特征波段反演绿藻覆盖度

Fig. 6 Green macroalgae coverage retrieved from indexes and characteristic bands of the sample area

选用可见光三个波段所建立的模型在验证区域进行验证。以模型的反演结果作为估测的POM, 以无人机影像提取的结果作为真实的POM, 分析拟合结果(图7)。从表2可知, 使用绿波段进行反演, R2最大, 为0.92; 均方根误差最小, 平均相对误差也最小, 表明使用绿波段所建立的模型效果最好。
图7 验证区蓝(a)、绿(b)、红(c)波段反演模型散点图

Fig. 7 Scatter plots for retrieval models of blue (a), green (b) and red (c) bands of the verification area

表2 蓝、绿、红波段反演模型结果比较

Tab. 2 Comparison of retrieval results for blue, green and red bands' retrieval models

输入值 公式 R2 均方根误差(RMSE) 平均相对误差(MRE)
RRED
RGREEN
RBLUE		 y=-20.08x+1.31
y=-22.73x+1.6
y=-33.86x+1.53		 0.87
0.92
0.86		 0.09
0.07
0.09		 17.64%
10.85%
18.11%

2.3 模型适用性分析

对Landsat卫星影像的陆地部分进行掩膜处理, 将绿波段反演公式应用于研究区域, 计算每个像素的POM, 卫星影像反演的绿藻覆盖度如图8a所示, 无人机影像提取结果反演的绿藻覆盖度如图8b所示。
图8 Landsat绿波段反射率反演绿藻覆盖度(a)与无人机影像提取真实绿藻覆盖度(b)

Fig. 8 Retrieval green macroalgae coverage of Landsat green band reflectance (a) and real green macroalgae coverage extracted from UAV image (b)

计算得到研究区域的卫星反演绿藻覆盖面积为0.4883km2, 无人机影像提取的真实绿藻覆盖面积为0.5729km2, 反演面积与真实面积的比值为0.85。反演结果虽然对部分像素绿藻覆盖度有所低估, 但对于整体绿藻的分布有较好的反映, 可以应用于此区域绿藻覆盖面积的估测。

3 结论

结合无人机低空航拍影像和Landsat-8卫星影像, 提出了基于NGRDI、NDVI、VB-FAH和蓝、绿、红、近红外波段反射率的绿藻亚像素覆盖度估算线性模型。
在参与新模型建模的众多参数中, 蓝、绿、红波段反射率与绿藻亚像素覆盖度呈现出更好的线性关系, 其R2分别为0.95、0.96、0.93。在验证区域对基于此三种模型拟合的POM结果进行验证, 其中绿波段反射率所建立的模型拟合结果最好, 其R2为0.92, 均方根误差为0.07, 平均相对误差为10.85%。使用绿波段反射率所建立的模型应用于整个研究区域所反演的绿藻覆盖面积与真实绿藻覆盖面积(无人机图像所提取的绿藻)比值为0.85。此模型可以较好地反映此研究区域的绿藻分布情况, 而对于更广泛的海水区域, 则需要进一步研究。

References

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安德玉, 邢前国, 魏振宁, 等, 2018. 黄海典型漂浮大型藻类光谱特征分析[J]. 海洋与湖沼, 49(5): 1054-1060.

AN DEYU, XING QIANGUO, WEI ZHENNING, et al, 2018. Spectral features and analysis of typical floating macroalgae in the yellow sea[J]. Oceanologia et Limnologia Sinica, 49(5): 1054-1060. (in Chinese with English abstract)

[2]
丁一, 黄娟, 崔廷伟, 等, 2015. 基于NDVI与丰度关系的MODIS影像浒苔混合像元分解方法[J]. 海洋学报, 37(7): 123-131.

DING YI, HUANG JUAN, CUI TINGWEI, et al, 2015. The decomposition method of MODIS images Enteromorpha mixed pixels based on the relation of NDVI to abundance[J]. Acta Oceanologica Sinica, 37(7): 123-131. (in Chinese with English abstract)

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[3]
樊彦国, 白羽, 陈潘潘, 等, 2015. 青岛近海浒苔光谱特征研究[J]. 海洋科学, 39(4): 87-91.

FAN YANGUO, BAI YU, CHEN PANPAN, et al, 2015. Research of spectrum characteristics of enteromorpha in Qingdao offshore[J]. Marine Sciences, 39(4): 87-91. (in Chinese with English abstract)

[4]
蒋兴伟, 刘建强, 邹斌, 等, 2009. 浒苔灾害卫星遥感应急监视监测系统及其应用[J]. 海洋学报, 31(1): 52-64.

JIANG XINGWEI, LIU JIANQIANG, ZOU BIN, et al, 2009. The satellite remote sensing system used in emergency response monitoring for Entermorpha prolifera disaster and its application[J]. Acta Oceanologica Sinica, 31(1): 52-64. (in Chinese with English abstract)

[5]
刘岩, 刘涛, 于丹, 等, 2010. 常见石莼科绿藻的生物学特征及分子系统学分析[J]. 中国海洋大学学报, 40(12): 71-80.

LIU YAN, LIU TAO, YU DAN, et al, 2010. Biological characteristics and molecular systematics studies on common green algae of Ulvaceae[J]. Periodical Of Ocean University Of China, 40(12): 71-80. (in Chinese with English abstract)

[6]
邢前国, 郑向阳, 施平, 等, 2011. 基于多源、多时相遥感影像的黄、东海绿潮影响区检测[J]. 光谱学与光谱分析, 31(6): 1644-1647.

XING QIANGUO, ZHENG XIANGYANG, SHI PING, et al, 2011. Monitoring “green tide” in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images[J]. Spectroscopy and Spectral Analysis, 31(6): 1644-1647. (in Chinese with English abstract)

[7]
CUI TINGWEI, ZHANG JIE, SUN LIE, et al, 2012. 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, 33(17): 5513-5527.

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[8]
GOWER J, KING S, 2019. Seaweed, seaweed everywhere[J]. Science, 365(6448): 27.

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[9]
HAN HONGBIN, SONG WEI, WANG ZONGLING, et al, 2019. Distribution of green algae micro-propagules and their function in the formation of the green tides in the coast of Qinhuangdao, the Bohai Sea, China[J]. Acta Oceanologica Sinica, 38(8): 72-77.

[10]
HU CHUANMIN, 2009. A novel ocean color index to detect floating algae in the global oceans[J]. Remote Sensing of Environment, 113(10): 2118-2129.

DOI

[11]
LI LIN, XING QIANGUO, LI XUERONG, et al, 2018a. Assessment of the impacts from the world's largest floating macroalgae blooms on the water clarity at the West Yellow Sea using MODIS data (2002-2016)[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1397-1402.

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[12]
LI LIN, ZHENG XIANGYANG, WEI ZHENNING, et al, 2018b. A spectral-mixing model for estimating sub-pixel coverage of sea-surface floating macroalgae[J]. Atmosphere-Ocean, 56(4): 296-302.

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[13]
LIU DONGYAN, KEESING J K, XING QIANGUO, et al, 2009. World's largest macroalgal bloom caused by expansion of seaweed aquaculture in China[J]. Marine Pollution Bulletin, 58(6): 888-895.

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[14]
MORAND P, MERCERON M, 2005. Macroalgal population and sustainability[J]. Journal of Coastal Research, 21(5): 1009-1020.

[15]
QI LIN, HU CHUANMIN, XING QIANGUO, et al, 2016. Long-term trend of Ulva prolifera blooms in the western Yellow Sea[J]. Harmful Algae, 58: 35-44.

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[16]
QI LIN, HU CHUANMIN, WANG MENGQIU, et al, 2017. Floating algae blooms in the East China Sea[J]. Geophysical Research Letters, 44(22): 11501-11509.

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[17]
SCHREYERS L, VAN EMMERIK T, BIERMANN L, et al, 2021. Spotting green tides over Brittany from space: three decades of monitoring with Landsat imagery[J]. Remote Sensing, 13(8): 1408.

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[18]
SHEN HUI, PERRIE W, LIU QINGRONG, et al, 2014. Detection of macroalgae blooms by complex SAR imagery[J]. Marine Pollution Bulletin, 78(1-2): 190-195.

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[19]
SMETACEK V, ZINGONE A, 2013. Green and golden seaweed tides on the rise[J]. Nature, 504(7478): 84-88.

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[20]
VALIELA I, MCCLELLAND J, HAUXWELL J, et al, 1997. Macroalgal blooms in shallow estuaries: controls and ecophysiological and ecosystem consequences[J]. Limnology and Oceanography, 42(5): 1105-1118.

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[21]
WANG MENGQIU, HU CHUANMIN, 2016. Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations[J]. Remote Sensing of Environment, 183: 350-367.

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[22]
WANG MENGQIU, HU CHUANMIN, BARNES B B, et al, 2019. The great Atlantic Sargassum belt[J]. Science, 365(6448): 83-87.

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[23]
XING QIANGUO, TOSI L G, BRAGA F, et al, 2015. Interpreting the progressive eutrophication behind the world's largest macroalgal blooms with water quality and ocean color data[J]. Natural Hazards, 78(1): 7-21.

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[24]
XING QIANGUO, HU CHUANMIN, 2016a. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique[J]. Remote Sensing of Environment, 178: 113-126.

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[25]
XING QIANGUO, BRAGA F, TOSI L, et al, 2016b. Detection of low salinity groundwater seeping into the eastern Laizhou Bay (China) with the aid of Landsat thermal data[J]. Journal of Coastal Research, 74(S1): 149-156.

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[26]
XING QIANGUO, GUO RUIHONG, WU LINGLING, et al, 2017. High-resolution satellite observations of a new hazard of golden tides caused by floating sargassum in winter in the Yellow Sea[J]. IEEE Geoscience and Remote Sensing Letters, 14(10): 1815-1819.

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[27]
XING QIANGUO, WU LINGLING, TIAN LIQIAO, et al, 2018. Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign[J]. Marine Pollution Bulletin, 133: 150-156.

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[28]
XING QIANGUO, AN DEYU, ZHENG XIANGYANG, et al, 2019. Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms[J]. Remote Sensing of Environment, 231: 111279.

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[29]
YE NAIHAO, ZHANG XIAOWEN, MAO YUZE, et al, 2011. ‘Green tides' are overwhelming the coastline of our blue planet: taking the world's largest example[J]. Ecological Research, 26(3): 477-485.

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