Optical remote sensing of <italic style="font-style: italic">Sargassum</italic> biomass in the Marginal Sea of China

LIU Yuru; QI Lin; ZHU Xiaobo; TANG Jun; LU Yingcheng

doi:10.11834/jrs.20254470

Atmosphere and Ocean | Views : 0 下载量: 339 CSCD: 0

R-PDF
PDF
Export
Share
Collection
Album

Optical remote sensing of Sargassum biomass in the Marginal Sea of China
Vol. 30, Issue 1, Pages: 118-131(2026)
Received：22 October 2024，

Published：07 January 2026
DOI： 10.11834/jrs.20254470
稿件说明：

移动端阅览

刘裕如，齐琳，朱小波，唐君，陆应诚.2026.中国近海马尾藻生物量的光学遥感研究.遥感学报，30（1）： 118-131 DOI： 10.11834/jrs.20254470.

Liu Y R，Qi L，Zhu X B，Tang J and Lu Y C. 2026. Optical remote sensing of Sargassum biomass in the Marginal Sea of China. National Remote Sensing Bulletin， 30（1）：118-131 DOI： 10.11834/jrs.20254470.

摘要

马尾藻（

Sargassum

）是一种在全球海洋广泛分布的漂浮大型褐藻，近年来由于其大量繁殖引发的“金潮”（Golden tides）现象，在以中国东海ECS（East China Sea）为主要区域的中国近海爆发愈加频发，对沿海生态环境和经济活动造成了不可忽视的影响。光学卫星遥感是海洋大型漂浮藻监测的有力支撑，能实现马尾藻的精确识别与定量估算。光学卫星遥感精细化监测中国东海马尾藻，尚缺乏马尾藻（中国东海主要藻种为铜藻）光学定量遥感模型，主要是因为缺失模型研究所需的实测光谱数据，制约了中国近海马尾藻的卫星光学定量遥感研究与应用。为实现其精准光学遥感估算，本研究在中国东海进行了马尾藻（铜藻）生物量控制实验，获取了反射率光谱数据，结合传感器光谱响应函数与遥感瑞利校正反射率

（Rayleigh-corrected Reflectance），建立了适用于多源光学传感器的中国东海马尾藻光学遥感估算模型，实现了多源光学卫星数据上马尾藻生物量有效估算，并对多源光学遥感数据的估算结果进行对比验证。结果表明相较于覆盖面积等定量参数，生物量能有效减少不同空间分辨率传感器因遥感尺度效应所带来的定量估算偏差，在多源卫星数据估算结果间具有较好的一致性，是不确定性最小的量化参数。基于多源光学卫星遥感数据对中国近海马尾藻开展定量遥感估算，为全面、准确掌握金潮爆发信息，促进中国近海漂浮藻类的精准量化和动态监测提供了重要参考。

Abstract

Sargassum

a type of floating macroalgae widely distributed in global oceans

is responsible for mass algae beaching events along the coastal areas of Caribbean Sea and other central western seas of the tropical Atlantic Ocean

causing many severe problems to the production and life of local residents. In recent years

Sargassum

has also triggered frequent occurrences of “golden tides” in the marginal sea of China

particularly the East China Sea (ECS)

due to its massive proliferation entangled with intricate climate and anthropogenic events. The emergence of golden tide events has had a significant impact on coastal ecosystems and economic activities. Optical satellite remote sensing has become a powerful tool for monitoring algae because of its advantages

such as wide monitoring coverage

rich spectral information

relatively uniform data formats

and easy accessibility. By combining accurate algae-identifying algorithms and algae-estimating quantitative models such as Biomass Per Area (BPA

kg/m

) estimation model

it will enable optical satellite remote sensing to precisely identify and quantitatively estimate

Sargassum

. Notably

Sargassum

in the ECS (

Sargassum

ECS

)

which is primarily composed of

S. horneri

exhibits different reflectance spectral characteristics due to species differences compared with

Sargassum

that inhabits other regions in the Atlantic Ocean

where

S. natans

and

S. fluitans

are the dominant species. Many studies that aimed to observe the

Sargassum

ECS

via remote sensing of optical satellites were mainly qualitative or not based on quantitative estimation models suitable for

S. horneri

whose key parameters have not been scientifically and objectively determined due to the lack of measured spectral data from spectrum collecting experiment. Thus

to reali

ze the accurate quantification of

Sargassum

ECS

via optical remote sensing

this study conducted controlled biomass experiments on

S. horneri

in the marginal sea of China

acquiring its hyperspectral reflectance dataset

then integrating spectral response functions of multiple optical sensors to simulate the corresponding Rayleigh-corrected reflectance (

)

such as the Multispectral Imager (MSI) onboard Sentinel-2A/B

the Coastal Zone Imager onboard HaiYang-1C/D (CZI on HY-1C/D)

the Moderate Resolution Imaging Spectroradiometer onboard Aqua/Terra (MODIS on Aqua/Terra)

the Operational Land Imager onboard Landsat-8

the Ocean and Land Color Instrument onboard Sentinel-3A/B

the Visible Infrared Imaging Radiometer onboard National Polar-Orbiting Partnership

and the Geostationary Ocean Color Imager-II on board GEO-KOMPSAT-2B

to develop remote sensing estimation models applicable to multisource sensors. In the comparison and validation step of the quantitative models of multisource remote sensing data

CZI and MODIS multispectral images with quantitative estimation results was selected as samples to be compared with the quasi-synchronous MSI quantitative estimation results as reference value. The comparison and validation result showed that the biomass from BPA estimation models that integrate the spatial resolution of pixel

as the most robust quantitative parameter among algae-containing pixels area or algae coverage area

effectively reduces the remote sensing scale effect discrepancies among data from multiple optical sensors

offering higher consistency and less uncertainty in multisource data estimation results. This study

which is based on multisource optical satellite remote sensing data

analyzes

Sargassum

in the marginal sea of China

providing crucial references for comprehensively and precisely understanding the dynamics of golden tide outbreaks and enhancing the precise quantification and monitoring of floating

algae in these areas

关键词

Keywords

references

Hu C M . 2009 . A novel ocean color index to detect floating algae in the global oceans . Remote Sensing of Environment , 113 ( 10 ): 2118 - 2129 [ DOI: 10.1016/j.rse.2009.05.012 http://dx.doi.org/10.1016/j.rse.2009.05.012 ]

Hu C M . 2021 . Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: challenges and potentials . Remote Sensing of Environment , 259 : 112414 [ DOI: 10.1016/j.rse.2021.112414 http://dx.doi.org/10.1016/j.rse.2021.112414 ]

Hu C M . 2022 . Remote detection of marine debris using Sentinel-2 imagery: a cautious note on spectral interpretations . Marine Pollution Bulletin , 183 : 114082 [ DOI: 10.1016/j.marpolbul.2022.114082 http://dx.doi.org/10.1016/j.marpolbul.2022.114082 ]

Hu C M and He M X . 2008 . Origin and offshore extent of floating algae in Olympic sailing area . Eos, Transactions American Geophysical Union , 89 ( 33 ): 302 - 303 [ DOI: 10.1029/2008EO330002 http://dx.doi.org/10.1029/2008EO330002 ]

Hu C M , Murch B , Barnes B B , Wang M Q , Maréchal J P , Franks J , Johnson D , Lapointe B , Goodwin D S , Schell J and Siuda A N S . 2016 . Sargassum watch warns of incoming seaweed . Eos , 97 [ DOI: 10.1029/2016EO058355 http://dx.doi.org/10.1029/2016EO058355 ]

Hu C M , Qi L , Hu L B , Cui T W , Xing Q G , He M X , Wang N , Xiao Y F , Sun D Y , Lu Y C , Yuan C , Wu M Q , Wang C Y , Chen Y L , Xu H P , Sun L , Guo M H and Wang M H . 2023 . Mapping Ulva prolifera green tides from space: a revisit on algorithm design and data products . International Journal of Applied Earth Observation and Geoinformation , 116 : 103173 [ DOI: 10.1016/j.jag.2022.103173 http://dx.doi.org/10.1016/j.jag.2022.103173 ]

Hu L B , Hu C M and He M X . 2017 . Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea . Remote Sensing of Environment , 192 : 217 - 227 [ DOI: 10.1016/j.rse.2017.01.037 http://dx.doi.org/10.1016/j.rse.2017.01.037 ]

Huete A R , Justice C . 1999 . MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document (Version 3)

Keesing J K , Liu D Y , Fearns P and Garcia R . 2011 . Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007 - 2009 , their origin and relationship to the expansion of coastal seaweed aquaculture in China. Marine Pollution Bulletin , 62 ( 6 ): 1169 - 1182 [ DOI: 10.1016/j.marpolbul.03.040 http://dx.doi.org/10.1016/j.marpolbul.03.040 ]

Komatsu T , Mizuno S , Natheer A , Kantachumpoo A , Tanaka K , Morimoto A , Hsiao S T , Rothäusler E A , Shishidou H , Aoki M and Ajisaka T . 2014 . Unusual distribution of floating seaweeds in the East China Sea in the early spring of 2012 . Journal of Applied Phycology , 26 ( 2 ): 1169 - 1179 [ DOI: 10.1007/s10811-013-0152-y http://dx.doi.org/10.1007/s10811-013-0152-y ]

Liu J C , Lu J Q , Ding J and Lu Y C . 2022 . A refined imagery algorithm to extract green tide in the Yellow Sea from HY-1C satellite CZI measurements . Haiyang Xuebao , 44 ( 5 ): 1 - 11

刘锦超 , 刘建强 , 丁静 , 陆应诚 . 2022 . HY-1C卫星CZI载荷的黄海绿潮提取研究 . 海洋学报 , 44 ( 5 ): 1 - 11 [ DOI: 10.12284/hyxb2022097 http://dx.doi.org/10.12284/hyxb2022097 ]

Lu Y C , Li L H , Hu C M , Li L , Zhang M W , Sun S J and Lv C G . 2016 . Sunlight induced chlorophyll fluorescence in the near-infrared spectral region in natural waters: interpretation of the narrow re-flectance peak around 761 nm . Journal of Geophysical Research : Oceans , 121 ( 7 ): 5017 – 5029 [ DOI: 10.1002/2016JC011797 http://dx.doi.org/10.1002/2016JC011797 ]

Lu T , Lu Y C , Hu L B , Jiao J N , Zhang M W and Liu Y X . 2019 . Uncertainty in the optical remote estimation of the biomass of Ulva prolifera macroalgae using MODIS imagery in the Yellow Sea . Optics Express , 27 ( 13 ): 18620 - 18627 [ DOI: 10.1364/OE.27.018620 http://dx.doi.org/10.1364/OE.27.018620 ]

Qi L , Hu C M , Mikelsons K , Wang M H , Lance V , Sun S J , Barnes B , Zhao J , and Van der Zande D . 2020 . In search of floating algae and other organisms in global oceans and lakes . Remote Sensing of Environment . 239 : 111659 [ DOI: 10.1016/j.rse.2020.111659 http://dx.doi.org/10.1016/j.rse.2020.111659 ]

Qi L and Hu C M . 2021 . To what extent can Ulva and Sargassum be detected and separated in satellite imagery? . Harmful Algae , 103 : 102001 [ DOI: 10.1016/j.hal.2021.102001 http://dx.doi.org/10.1016/j.hal.2021.102001 ]

Qi L , Hu C M , Barnes B B , Lapointe B E , Chen Y L , Xie Y Y and Wang M H . 2022 . Climate and anthropogenic controls of seaweed expansions in the East China Sea and Yellow Sea . Geophysical Research Letters , 49 ( 19 ): e2022 GL 098185 [ DOI: 10.1029/2022GL098185 http://dx.doi.org/10.1029/2022GL098185 ]

Qi L , Hu C M , Lu Y C and Ma R H . 2023 . Spectral analysis and identification of floating algal blooms in oceans and lakes based on HY-1C/D CZI observations . National Remote Sensing Bulletin , 27 ( 1 ): 157 - 170

齐琳 , 胡传民 , 陆应诚 , 马荣华 . 2023 . 基于HY-1C/D卫星CZI的海洋、湖泊中漂浮藻藻华的光谱分析与识别 . 遥感学报 , 27 ( 1 ): 157 - 170 [ DOI: 10.11834/jrs.20235009 http://dx.doi.org/10.11834/jrs.20235009 ]

Qi L , Wang M H and Hu C M . 2023 . Uncertainties in MODIS-derived Ulva prolifera amounts in the Yellow Sea: a systematic evaluation using Sentinel-2/MSI observations . IEEE Geoscience and Remote Sensing Letters , 20 : 1501805 [ DOI: 10.1109/LGRS.2023.3272889 http://dx.doi.org/10.1109/LGRS.2023.3272889 ]

Richardson A J and Wiegand C L . 1977 . Distinguishing vegetation from soil background information . Photogrammetric Engineering and Remote Sensing , 43 ( 12 ): 1541 - 1552

Smetacek V and Zingone A . 2013 . Green and golden seaweed tides on the rise . Nature , 504 ( 7478 ): 84 - 88 [ DOI: 10.1038/nature12860 http://dx.doi.org/10.1038/nature12860 ]

Tang J , Lu Y C , Jiao J N , Liu J Q , Hu L B , Ding J , Xing Q G , Wang F T , Song Q J , Chen Y L , Tian L Q , Wang X Y and Liu J C . 2023 . High-precision monitoring of green tide biomass in the Yellow Sea of China through optical remote sensing . National Remote Sensing Bulletin , 27 ( 11 ): 2484 - 2498

唐君 , 陆应诚 , 焦俊男 , 刘建强 , 胡连波 , 丁静 , 邢前国 , 王福涛 , 宋庆君 , 陈艳拢 , 田礼乔 , 王心源 , 刘锦超 . 2023 . 中国近海绿潮生物量的卫星光学遥感估算 . 遥感学报 , 27 ( 11 ): 2484 - 2498 [ DOI: 10.11834/jrs.20232535 http://dx.doi.org/10.11834/jrs.20232535 ]

Wang M Q and Hu C M . 2016 . Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations . Remote Sensing of Environment , 183 : 350 - 367 [ DOI: 10.1016/j.rse.2016.04.019 http://dx.doi.org/10.1016/j.rse.2016.04.019 ]

Wang M Q , Hu C M , Barnes B B , Mitchum G , Lapointe B and Montoya J P . 2019 . The great Atlantic Sargassum belt . Science , 365 ( 6448 ): 83 - 87 [ DOI: 10.1126/science.aaw7912 http://dx.doi.org/10.1126/science.aaw7912 ]

Wang M Q , Hu C M , Cannizzaro J , English D , Han X X , Naar D , Lapointe B , Brewton R and Hernandez F . 2018 . Remote sensing of Sargassum biomass, nutrients, and pigments . Geophysical Research Letters , 45 ( 22 ): 12359 - 12367 [ DOI: 10.1029/2018GL078858 http://dx.doi.org/10.1029/2018GL078858 ]

Webster R K and Linton T . 2013 . Development and implementation of Sargassum Early Advisory System (SEAS) . Shore and Bench , 81 ( 3 ): 1 - 6

Wen Y S , Deng J M , Mao Z H , Shi J , Zhou Y , Lu T and Lu Y C . 2018 . Effect of the Fresnel reflection of a water surface on Chlorophyll Fluorescence line height at approximately 761 nm . Journal of Remote Sensing (in Chinese) , 22 ( 3 ): 424 - 431

温颜沙 , 邓建明 , 毛志华 , 石静 , 周杨 , 鲁婷 , 陆应诚 . 2018 . 水面菲涅尔反射对~761 nm叶绿素荧光估算的影响 . 遥感学报 , 22 ( 3 ): 424 - 431 [ DOI: 10.11834/jrs.20187251 http://dx.doi.org/10.11834/jrs.20187251 ]

Xing Q G , Guo R H , Wu L L , An D Y , Cong M , Qin S and Li X R . 2017 . High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in winter in the Yellow Sea . IEEE Geoscience and Remote Sensing Letters , 14 ( 10 ): 1815 - 1819 [ DOI: 10.1109/LGRS.2017.2737079 http://dx.doi.org/10.1109/LGRS.2017.2737079 ]

Xing Q G and Hu C M . 2016 . 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 . Remote Sensing of Environment , 178 : 113 - 126 [ DOI: 10.1016/j.rse.2016.02.065 http://dx.doi.org/10.1016/j.rse.2016.02.065 ]

Alert me when the article has been cited

提交

Common mistakes in remote sensing studies of green tides and golden tides in the Yellow Sea and East China Sea

Spatial reconstruction-aided spatio-temporal fusion of NDVI images

Reconstruction of high spatiotemporal resolution snow cover remote sensing range data from multiple sources based on deep learning model： A case study in the Qilian Mountains

Monitoring chilling damage of corn in the Northeast China based on MODIS LST and meteorological data

Optical remote sensing of Sargassum biomass in the Marginal Sea of China

DOI： 10.11834/jrs.20254470

摘要

Abstract

关键词

Keywords

references