多平台海洋激光雷达的海洋上表层生物光学参数反演及验证

杨帆; 张洪玮; 李子旺; 朱培志; 孟凡谦; 唐军武; 刘秉义; 吴松华

doi:10.12000/JR25064

多平台海洋激光雷达的海洋上表层生物光学参数反演及验证

DOI: 10.12000/JR25064 CSTR: 32380.14.JR25064

杨帆¹,
张洪玮¹,
李子旺^{1, 2},
朱培志¹,
孟凡谦¹,
唐军武^{1, 3},
刘秉义³,
吴松华^{1, 3, ,}

1.
中国海洋大学信息科学与工程学部海洋技术学院青岛 266100
2.
中国海洋大学三亚海洋研究院三亚 572000
3.
崂山国家实验室青岛 266237

基金项目: 国家自然科学基金(U2106210, 42106182)，国家重点研发计划(2022YFB3901705, 2022YFB3901702)，崂山实验室科技创新项目(LSKJ202201405, LSKJ202201202)

详细信息

作者简介:
杨　帆，硕士生，主要研究方向为海洋激光雷达数据反演及系统调试

张洪玮，博士，副教授，主要研究方向为基于相干激光雷达的低空风切变遥感及其在航空安全方面的应用，以及海洋激光雷达在悬浮颗粒物探测方面的设计和应用

李子旺，博士生，主要研究方向为沙氏海洋激光雷达的研制以及在悬浮颗粒物探测方面的应用

朱培志，博士生，主要研究方向为海洋激光雷达仿真及数据反演

孟凡谦，博士生，主要研究方向为星载激光雷达定标检验及数据处理

唐军武，博士，研究员，主要研究方向为海洋激光雷达的开发和应用、海洋光学和水色遥感

刘秉义，博士，副教授，主要研究方向为海洋和大气激光探测技术及算法

吴松华，博士，教授，主要研究方向为激光雷达技术与大气海洋遥感

通讯作者:
吴松华 wush@ouc.edu.cn

责任主编：狄慧鸽 Corresponding Editor: DI Huige
中图分类号: TN958
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出版历程
- 收稿日期: 2025-04-03
- 修回日期: 2025-05-20
- 网络出版日期: 2025-05-29

Inversion and Validation of Ocean Surface Bio-optical Parameters Using Multiplatform Ocean LiDAR

YANG Fan¹,
ZHANG Hongwei¹,
LI Ziwang^{1, 2},
ZHU Peizhi¹,
MENG Fanqian¹,
TANG Junwu^{1, 3},
LIU Bingyi³,
WU Songhua^{1, 3
, ,}

1.
College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2.
Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China
3.
Laoshan National Laboratory, Qingdao 266237, China

Funds: The National Natural Science Foundation of China (U2106210, 42106182), National Key Research and Development Program (2022YFB3901705, 2022YFB3901702), Laoshan Laboratory Science and Technology Innovation Projects (LSKJ202201405, LSKJ202201202)

More Information

Corresponding author: WU Songhua, wush@ouc.edu.cn

摘要

摘要: 海洋上表层生物光学参数的垂直特征对评估海洋初级生产力和碳循环至关重要。虽然激光雷达技术能够有效探测这些参数，但受限于经验模型在不同区域的适应性差异，反演结果通常存在较大偏差。针对该问题，该研究基于2023—2024年中国南海海域多平台激光雷达联合观测数据，结合区域适应性生物光学模型，实现了该海域生物光学参数剖面的高精度反演。其中，叶绿素a浓度剖面反演结果与原位数据一致性较高，决定系数(R²)为0.84，平均均方根误差(RMSE)为0.14 μg·L^–1。通过误差传递模型量化分析表明，不同波段的光学敏感性差异显著影响误差分布，蓝光波段有效探测深度为70 m，显著高于绿光波段的58 m。蓝光波段在次表层叶绿素最大值层(SCML)的自身反演偏差较绿光波段低0.18 μg·L^–1，进而阐明了各波段光学特性与自身偏差的内在关联，为提升复杂水体生物光学参数剖面反演的可靠性和开展误差分析提供了有效方法。
- 海洋激光雷达 /
- 激光探测 /
- 生物光学参数 /
- 蓝绿波段 /
- 误差分析
Abstract: The vertical characteristics of biological optical parameters in the upper ocean are essential for evaluating marine primary productivity and the carbon cycle. Although ocean lidar can effectively detect these parameters, the inversion results are usually highly biased due to the regional differences in the adaptability of empirical models. This study uses multiplatform lidar observations collected in the South China Sea (2023–2024), combined with a region-adaptive bio-optical model, to achieve high-precision profiling of bio-optical parameters in the region. The derived vertical profiles of chlorophyll-a concentration showed strong agreement with in-situ measurements, with a coefficient of determination (R²) of 0.84 and an average root mean square error of 0.14 μg·L^–1. Further quantitative analysis using an error transfer model revealed that differences in band-specific optical sensitivity considerably affected error distribution. The effective detection depth in the blue band was 70 m, notably higher than the 58 m depth in the green band. In addition, at the subsurface chlorophyll maximum layer, the inversion bias in the blue band was 0.18 μg·L^–1 lower than that in the green band, highlighting the intrinsic relationship between the optical characteristics of each wavelength and its associated bias. This result provides an effective method for improving the reliability of profile inversion of bio-optical parameters in complex waters and performing error analysis.
- Ocean LiDAR /
- Laser detection /
- Bio-optical parameters /
- Blue-green wave band /
- Error analysis

HTML全文

图 1 船载海洋激光雷达系统图

Figure 1. Shipboard LiDAR system diagram

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图 2 机载海洋激光雷达系统图

Figure 2. System diagram of airborne LiDAR

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图 3 中国南海试验区域两个航次的站点位置图

Figure 3. Site location map of two voyages in the South China Sea experimental area

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图 4 数据反演流程图

Figure 4. Data inversion flowchart

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图 5 船载海洋激光雷达T4站点部分测量数据反演结果示例

Figure 5. Example of inversion results of some measurements at the T4 site of a shipborne ocean LiDAR

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图 6 使用通用参数和拟合系数拟合后，激光雷达反演结果与RBR测量结果对比图

Figure 6. Comparison chart of laser LiDAR inversion results and RBR measurement results after fitting with general parameters and fitting coefficients

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图 7 利用拟合系数反演出的一些典型站点的叶绿素a浓度剖面与利用原位RBR测量结果比较

Figure 7. Comparison of chlorophyll-a concentration profiles of some typical sites using fitting coefficients and in-situ RBR measurements

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图 8 典型站点的船载与机载海洋激光雷达反演的叶绿素a浓度剖面与原位RBR测量结果比较、标准差比较以及相应的信噪比剖面

Figure 8. Comparison of chlorophyll-a concentration profiles inverted by shipborne and airborne ocean LiDAR at typical sites with in-situ RBR measurement results, standard deviation comparison, and corresponding signal-to-noise ratio profiles

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图 9 A1站点和B2站点的船载与机载海洋激光雷达反演的颗粒有机碳浓度剖面与元素分析仪测量结果比较

Figure 9. Comparison of POC concentration profiles retrieved from shipborne and airborne ocean LiDAR at A1 and B2 stations with water sampling measurement results

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图 10 A1, B2站点理论计算叶绿素a浓度误差

Figure 10. Theoretical calculation error of chlorophyll-a concentration at A1 and B2 stations

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表 1 船载雷达系统参数表

Table 1. Parameter table of shipboard LiDAR system

分系统	参数	描述
发射系统	单脉冲能量	200 mJ @ 532 nm; 120 mJ @ 355 nm;
	单脉冲能量	400 mJ @ 1064 nm
	重复频率	20 Hz
	脉宽	≤ 5 ns
	发散角	≤ 0.5 mrad
	光束直径	≈ 6.5 mm
接收系统	望远镜直径	60 mm
	视场	16～60 mrad
	滤色片	1 nm @ 532 nm; 3 nm @ 355 nm, 405 nm; 10 nm @ 680 nm
	探测器	Photomultiplier tube
采集系统	AD分辨率	14 bits
采集系统	采样频率	PC @ 1 GHz

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表 2 机载雷达系统参数表

Table 2. Parameter table of airborne LiDAR system

分系统	参数	描述
发射系统	单脉冲能量	2 mJ @ 486.1 nm; 5 mJ @ 532.2 nm
	重复频率	100 Hz
	脉宽	2.5 ns @ 486.1 nm; 6 ns @ 532.2 nm
	发散角	5 mrad
接收系统	望远镜直径	200 mm
	视场	28 mrad
	接收光学带宽	0.44 nm @ 486.1 nm; 0.60 nm @ 532.2 nm
	探测器	Photomultiplier tube
采集系统	采样频率	1 ns

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表 3 辅助参数说明

Table 3. Auxiliary parameter description

数据来源	参数	说明
RBR水质监测仪 (RBR XR-420, RBR, Canada)	Chl_a	叶绿素a浓度剖面
Global Ocean Biogeochemistry Analysis and Forecast	Chl_a	叶绿素a浓度
元素分析仪(UNICUBE, Elementar, germany)	POC	颗粒有机碳浓度

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