High-precision monitoring of green tide biomass in the Yellow Sea of China through optical remote sensing

TANG Jun; LU Yingcheng; JIAO Junnan; LIU Jianqiang; HU Lianbo; DING Jing; XING Qianguo; WANG Futao; SONG Qingjun; CHEN Yanlong; TIAN Liqiao; WANG Xinyuan; LIU Jinchao

doi:10.11834/jrs.20232535

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High-precision monitoring of green tide biomass in the Yellow Sea of China through optical remote sensing
Vol. 27, Issue 11, Pages: 2484-2498(2023)
Published： 07 November 2023 ，
DOI： 10.11834/jrs.20232535

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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

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.

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 DOI： 10.11834/jrs.20232535.

摘要

生物量是精准量化海洋大型漂浮藻类的关键参数，是反映海洋生态环境变化的有效指标。光学遥感卫星能为绿潮的精细化监测与评估提供数据支持，实现绿潮的精准识别与量化估算。针对中国海洋一号C/D卫星（HY-1C/D）海岸带成像仪CZI（Coastal Zone Imager）、美国中分辨率成像光谱仪MODIS（Moderate-resolution Imaging Spectroradiometer）、欧洲空间局哨兵2号卫星多光谱成像仪MSI （Multi Spectral Instrument）等光学遥感数据特点，基于绿潮生物量变化模拟与观测验证数据，本研究提出了适用于不同光学卫星数据的绿潮生物量估算模型与计算方法，开展了中国近海绿潮生物量光学遥感估算与交叉验证。结果表明：相较于绿潮像元面积和覆盖面积，绿潮生物量估算结果的不确定性最小，该参数能有效减少面积参数所内含的尺度效应差异，能更准确地用于海洋绿潮的量化与评估。此外，基于CZI和MODIS数据开展2021年中国近海绿潮生物量协同监测应用，有效提高了绿潮生物量监测的精度，详细量化了2021年中国近海绿潮生物量的年内变化，展现了绿潮生物量的精细空间分布格局与变化趋势。多源光学遥感数据开展绿潮生物量遥感估算，对中国近海漂浮藻类的精准、定量、动态监测，具有重要的方法与数据参考意义。

Abstract

Large-scale green tides occurring in the Yellow Sea (YS) of China have become a critical eco-environmental problem

causing serious damage to marine and the coastal ecological environment

aquaculture

and tourism since 2007. Green tide biomass is a key parameter for accurate quantification of floating macroalgae

serving as an effective indicator for monitoring changes in the marine ecological environment. Satellite remote sensing technology plays a pivotal role in supporting the monitoring and assessment of green tide. Spaceborne optical sensors

in particular

offer a wealth of data that is indispensable for the fine-scale quantitative monitoring and assessment of green tide. In this study

we have established robust statistical relationships between Biomass Per Area (BPA) and various optical remote sensing indices by modeling the laboratory measurements of

U. prolifera

biomass (wet weight) per unit area and the corresponding spectral reflectance data. The computational methods of BPA have been carefully designed and validated for different optical data

including Moderate Resolution Imaging Spectroradiometer (MODIS)

the Multispectral Instrument (MSI) onboard Sentinel-2 satellites

and the Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites. These results indicate that BPA can serve as a highly effective parameter in quantifying green tide using remote sensing data. Unlike common parameters such as pixel area or coverage area

BPA can mitigate the scale effects of spatial resolution differences from various observations

minimizing the uncertainty especially when integrating multiple remote sensing data. With the coordinated utilization of CZI and MODIS data in 2021 and the developed BPA models

the detailed intra-annual variations in green tide biomass in the YS of China were quantified. This analysis has revealed the intricate spatial distribution patterns and trends inherent in green tide biomass fluctuations. The utilization of multiple optical remote sensing data sources for the estimation of green tide biomass carries important methodological significance and serves as an accurate data reference for the precise

quantitative

and dynamic monitoring of green tide in the YS of China.

关键词

绿潮生物量光学遥感HY-1C/DCZIMODIS

Keywords

green tide biomassoptical remote sensingHY-1C/DCZIMODIS

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