基于水文气象参数的南海西部叶绿素a估算*

doi:10.11978/2024095

热带海洋学报 ›› 2025, Vol. 44 ›› Issue (2): 18-29.doi: 10.11978/2024095CSTR: 32234.14.2024095

基于水文气象参数的南海西部叶绿素a估算^*

郑媛宁¹^,²(), 李彩¹(), 周雯¹, 许占堂¹, 施震¹, 张现清¹^,², 刘聪¹^,², 赵金成¹^,²

1.热带海洋国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301
2.中国科学院大学, 北京 100049

收稿日期:2024-04-26 修回日期:2024-06-19 出版日期:2025-03-10 发布日期:2025-04-11
通讯作者: 李彩
作者简介:
郑媛宁(1999—), 女, 河南省邓州市人, 硕士研究生, 研究方向是海洋环境监测。email: zhengyuanning21@mails.ucas.ac.cn
*感谢国家自然科学基金共享航次, 感谢中国科学院南海海洋研究所海洋光学学科组全体成员对于本次实验数据获取做出的贡献。
基金资助:
广东省基础与应用基础研究基金项目(2023A1515240073); 广州市南沙区科技规划项目(2022ZD001); 国家重点研发计划项目(2016YFC1400603); 国家重点研发计划项目(2017YFC0506305)

Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters^*

ZHENG Yuanning¹^,²(), LI Cai¹(), ZHOU Wen¹, XU Zhantang¹, SHI Zhen¹, ZHANG Xianqing¹^,², LIU Cong¹^,², ZHAO Jincheng¹^,²

1. State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2024-04-26 Revised:2024-06-19 Online:2025-03-10 Published:2025-04-11
Contact: LI Cai
Supported by:
Guangdong Basic and Applied Basic Research Foundation(2023A1515240073); Science and Technology Planning Project of Science and Technology Planning Project of Nansha District, Guangzhou(2022ZD001); National Key Research and Development Program of China(2016YFC1400603); National Key Research and Development Program of China(2017YFC0506305)

摘要/Abstract

摘要：

文章以叶绿素a (Chlorophyll-a, Chl-a)的低成本、高精度估算为目标, 利用近十年南海西部调查航次数据, 基于随机森林(random forest, RF)算法, 以水文气象条件的变化对海洋生化过程的影响及贡献为基础, 以水文气象参数(hydro-meteorological parameters, HMPs)作为输入数据, 构建Chl-a的低成本估算模型对南海西部表层Chl-a进行了估算。为验证基于水文气象参数估算Chl-a的可靠性, 利用准分析算法(quasi-analytical algorithm, QAA)以实测固有光学特性参数为基础, 推导得到原位遥感反射率(remote sensing reflectance, R_rs)。在此基础上, 结合海洋颜色4 (ocean color 4, OC4)、Aiken和Tassan等经典水色产品经验算法对 Chl-a进行了估算及评价, 评价结果表明OC4算法的估算精度最高, R²可达0.438。与RF模型0.568的R²比较不难看出, 得益于HMPs的大数据量, 基于HMPs的RF模型其Chl-a估算结果表现出较为优秀的稳定性和泛化性, 与实测结果的空间分布一致性更好。通过对特征参数重要性进行研究发现, 盐度是基于HMPs估算Chl-a的机器学习模型中最重要的特征变量, 其次依次是温度、风与气压, 贡献率最低的是相对湿度。

关键词: 叶绿素a, 水文气象, 机器学习, 南海

Abstract:

With the goal of low-cost and high-accuracy estimation of Chlorophyll-a (Chl-a), a model for estimating Chl-a in the surface layer of the Western South China Sea (WSCS) was constructed in this study. Using the data from the WSCS cruises in the past ten years, and based on the influence of changes of hydro-meteorological conditions and their contribution to the oceanic biochemical processes, the hydro-meteorological parameters (HMPs) were used as the input data of the random forest (RF) algorithm. To evaluate the reliability of estimating Chl-a based on HMPs, the quasi-analytical algorithm (QAA) was used to derive the in-situ remote sensing reflectance (R_rs) based on the measured inherent optical property parameters. Then Chl-a was estimated and evaluated by the combination of classical empirical algorithms for water color products such as Ocean Color 4 (OC4), Aiken and Tassan, and the evaluation results showed that the OC4 algorithm had the highest estimation accuracy, with an R² of up to 0.438. The comparison with the R² of 0.568 of the RF-based model shows that, owing to the large data volume of HMPs, the Chl-a estimation results of the RF model based on HMPs show much better stability and generalization, and better spatial distribution consistency with the measured results. It was found in study of the importance of feature parameters that in the machine learning model for estimating Chl-a based on HMPs, salinity is the most important feature variable, followed in turn by temperature, wind and air pressure, and the lowest contributor is relative humidity.

Key words: Chlorophyll-a, hydro-meteorology, machine learning, South China Sea

中图分类号:

P731.3

郑媛宁, 李彩, 周雯, 许占堂, 施震, 张现清, 刘聪, 赵金成. 基于水文气象参数的南海西部叶绿素a估算^*[J]. 热带海洋学报, 2025, 44(2): 18-29.

ZHENG Yuanning, LI Cai, ZHOU Wen, XU Zhantang, SHI Zhen, ZHANG Xianqing, LIU Cong, ZHAO Jincheng. Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters^*[J]. Journal of Tropical Oceanography, 2025, 44(2): 18-29.

图/表 15

图1

表1

图2

图3

图4

图5

图6

表2

本研究中利用的经验算法简介"

算法名称	算法公式	参数值	文献来源
OC4	$M = log 10 max R rs λ blue R rs λ green$ $M = log 10 max R rs λ blue R rs λ green$	$R rs λ blue = R rs 442 nm$ ; $R rs λ blue = R rs 442 nm$ ; a₀=3.562; a₁=-13.144; a₂=15.409; a₃=-5.997; a₄=0.4	O’Reilly等(2019)
Aiken	$M = log R rs 488 nm R rs 532 nm$ $M = log R rs 488 nm R rs 532 nm$	a₀= -0.395; a₁=0.803; a₂=-0.158; a₃=-0.118	Aiken等(1996)
Tassan	$M = r rs 442 nm r rs 532 nm r rs 420 nm r rs 488 nm a 0$ $M = r rs 442 nm r rs 532 nm r rs 420 nm r rs 488 nm a 0$	a₀=-1.012; a₁=-0.439; a₂=0.713; a₃=-0.176	Tassan (1994)

表2

表3

图7

图8

图9

图10

图11

图12

参考文献 45

[1]	李建鸿, 黄昌春, 查勇, 等, 2021. 长江干流表层水体悬浮物的空间变化特征及遥感反演[J]. 环境科学, 42(11): 5239-5249.
	LI JIANHONG, HUANG CHANGCHUN, ZHA YONG, et al, 2021. Spatial variation characteristics and remote sensing retrieval of total suspended matter in surface water of the yangtze river[J]. Environmental Science, 42(11): 5239-5249 (in Chinese with English abstract).
[2]	柳青青, 孟朔羽, 徐茗, 等, 2021. 随机森林反演卫星遥感海表面盐度研究[J]. 武汉大学学报(信息科学版), 48(9): 1538-1545.
	LIU QINGQING, MENG SHUOYU, XU MING, et al, 2021. Satellite sea surface salinity retrieval using random forest model[J]. Geomatics and Information Science of Wuhan University, 48(9): 1538-1545 (in Chinese with English abstract).
[3]	王春玲, 史锴源, 明星, 等, 2022. 基于机器学习的水体化学需氧量高光谱反演模型对比研究[J]. 光谱学与光谱分析, 42(8): 2353-2358.
	WANG CHUNLING, SHI KAIYUAN, MING XING, et al, 2022. A comparative study of the cod hyperspectral inversion models in water based on the maching learning[J]. Spectroscopy and Spectral Analysis, 42(8): 2353-2358 (in Chinese with English abstract).
[4]	张莹, 谢仕义, 邓伟彬, 等, 2019. 基于机器学习理论的海洋水质评价模型[J]. 物探化探计算技术, 41(6): 819-825.
	ZHANG YING, XIE SHIYI, DENG WEIBIN, et al, 2019. Ocean water quality evaluation model based on machine learning theory[J]. Computing Techniques for Geophysical and Geochemical Exploration, 41(6): 819-825 (in Chinese with English abstract).
[5]	AIKEN J, MOORE G F, TREES C C, et al, 1996. The SeaWiFS CZCS-type pigment algorithm[J]. Oceanographic Literature Review, 43(3): 315-316.
[6]	BINH N A, HOA P V, THAO G T P, et al, 2022. Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam[J]. International Journal of Applied Earth Observation and Geoinformation, 112: 102951.
[7]	BOSS E, PICHERAL M, LEEUW T, et al, 2013. The characteristics of particulate absorption, scattering and attenuation coefficients in the surface ocean; Contribution of the Tara Oceans expedition[J]. Methods in Oceanography, 7: 52-62.
[8]	BREIMAN L, 2001. Random forests[J]. Machine Learning, 45(1): 5-32.
[9]	CABALLERO I, ROMÁN A, TOVAR-SÁNCHEZ A, et al, 2022. Water quality monitoring with Sentinel-2 and Landsat-8 satellites during the 2021 volcanic eruption in La Palma (Canary Islands)[J]. Science of the Total Environment, 822: 153433.
[10]	CANNIZZARO J P, CARDER K L, 2006. Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters[J]. Remote Sensing of Environment, 101(1): 13-24.
[11]	CAPPELLI F, GRIMALDI S, 2023. Feature importance measures for hydrological applications: insights from a virtual experiment[J]. Stochastic Environmental Research and Risk Assessment, 37(12): 4921-4939.
[12]	CHEN BOTAO, MU XI, CHEN PENG, et al, 2021. Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data[J]. Ecological Indicators, 133: 108434.
[13]	DENG LIN, ZHOU WEN, CAO WENXI, et al, 2019. Retrieving phytoplankton size class from the absorption coefficient and chlorophyll a concentration based on support vector machine[J]. Remote Sensing, 11(9): 1054.
[14]	FU DONGYANG, HUANG YUYE, LIU DAZHAO, et al, 2020. Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function[J]. Acta Oceanologica Sinica, 39(7): 107-114.
[15]	GAO SHAN, WANG HUI, 2008. Seasonal and spatial distributions of phytoplankton biomass associated with monsoon and oceanic environments in the South China Sea[J]. Acta Oceanologica Sinica, 27(6): 17-32.
[16]	GARABAGHI F H, BENZER S, BENZER R, 2023. Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach[J]. Environmental Monitoring and Assessment, 195(7): 879. doi: 10.1007/s10661-023-11492-3 pmid: 37354319
[17]	GITELSON A, KARNIELI A, GOLDMAN N, et al, 1996. Chlorophyll estimation in the Southeastern Mediterranean using CZCS images: adaptation of an algorithm and its validation[J]. Journal of Marine Systems, 9(3-4): 283-290.
[18]	HE QINGYOU, ZHAN HAIGANG, XU JIE, et al, 2019. Eddy-induced chlorophyll anomalies in the Western South China Sea[J]. Journal of Geophysical Research: Oceans, 124(12): 9487-9506.
[19]	HUYNH H N T, ALVERA-AZCÁRATE A, BECKERS J M, 2020. Analysis of surface chlorophyll a associated with sea surface temperature and surface wind in the South China Sea[J]. Ocean Dynamics, 70(1): 139-161.
[20]	KAHRU M, GILLE S T, MURTUGUDDE R, et al, 2010. Global correlations between winds and ocean chlorophyll[J]. Journal of Geophysical Research: Oceans, 115(C12): C12040.
[21]	KOTTA D, KITSIOU D, 2019. Chlorophyll in the eastern Mediterranean sea: correlations with environmental factors and trends[J]. Environments, 6(8): 98.
[22]	KWONG I H Y, WONG F K K, FUNG T, 2022. Automatic mapping and monitoring of marine water quality parameters in Hong Kong using sentinel-2 image time-series and google earth engine cloud computing[J]. Frontiers in Marine Science, 9: 871470.
[23]	LEE Z P, DU KEPING, VOSS K J, et al, 2011. An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance[J]. Applied Optics, 50(19): 3155-3167.
[24]	LI JUNYI, LI MIN, WANG CHAO, et al, 2023. Multiple mechanisms for chlorophyll a concentration variations in coastal upwelling regions: a case study east of Hainan Island in the South China Sea[J]. Ocean Science, 19(2): 469-484.
[25]	LOISEL H, VANTREPOTTE V, OUILLON S, et al, 2017. Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002-2012)[J]. Remote Sensing of Environment, 190: 217-232.
[26]	MA CHUNLEI, ZHAO JUN, AI BIN, et al, 2021. Two-decade variability of sea surface temperature and chlorophyll-a in the Northern South China Sea as revealed by reconstructed cloud-free satellite data[J]. IEEE Transactions on Geoscience and Remote Sensing, 59(11): 9033-9046.
[27]	MATSUMOTO J, OLAGUERA L M P, NGUYEN-LE D, et al, 2020. Climatological seasonal changes of wind and rainfall in the Philippines[J]. International Journal of Climatology, 40(11): 4843-4857.
[28]	O’REILLY J E, MARITORENA S, MITCHELL B G, et al, 1998. Ocean color chlorophyll algorithms for SeaWiFS[J]. Journal of Geophysical Research: Oceans, 103(C11): 24937-24953.
[29]	O’REILLY J E, WERDELL P J, 2019. Chlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6[J]. Remote Sensing of Environment, 229: 32-47.
[30]	POPE R M, FRY E S, 1997. Absorption spectrum (380-700 nm) of pure water. Ⅱ. Integrating cavity measurements[J]. Applied Optics, 36(33): 8710-8723.
[31]	RAHMANI F, LAWSON K, OUYANG WENYU, et al, 2021. Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data[J]. Environmental Research Letters, 16(2): 024025.
[32]	ROESLER C S, BARNARD A H, 2013. Optical proxy for phytoplankton biomass in the absence of photophysiology: rethinking the absorption line height[J]. Methods in Oceanography, 7: 79-94.
[33]	SHENG CHONG, JIAO J J, LUO XIN, et al, 2023. Offshore freshened groundwater in the Pearl River estuary and shelf as a significant water resource[J]. Nature Communications, 14(1): 3781. doi: 10.1038/s41467-023-39507-0 pmid: 37355684
[34]	SISWANTO E, TANG JUNWU, YAMAGUCHI H, et al, 2011. Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas[J]. Journal of Oceanography, 67(5): 627-650.
[35]	TASSAN S, 1994. Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters[J]. Applied Optics, 33(12): 2369-2378. doi: 10.1364/AO.33.002369 pmid: 20885588
[36]	WANG FEIER, WANG YIXU, ZHANG KAI, et al, 2021. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation[J]. Environmental Research, 202: 111660.
[37]	WANG TIANHAO, SUN YU, SU HUA, et al, 2023. Declined trends of chlorophyll a in the South China Sea over 2005-2019 from remote sensing reconstruction[J]. Acta Oceanologica Sinica, 42(1): 12-24.
[38]	WU MEILIN, WANG YOUSHAO, DONG JUNDE, et al, 2017. Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China[J]. Frontiers of Earth Science, 11(1): 114-126. doi: 10.1007/s11707-016-0585-0
[39]	XIAN TAO, SUN LIANG, YANG YUANJIAN, et al, 2012. Monsoon and eddy forcing of chlorophyll-a variation in the northeast South China Sea[J]. International Journal of Remote Sensing, 33(23): 7431-7443.
[40]	YUAN XIAOGUANG, LIU SHIRUO, FENG WEI, et al, 2023. Feature importance ranking of random forest-based end-to-end learning algorithm[J]. Remote Sensing, 15(21): 5203.
[41]	ZHANG XIANQING, LI CAI, ZHENG YUANNING, et al, 2023. Approach for estimating the vertical distribution of the diffuse attenuation coefficient in the South China Sea[J]. Optics Express, 31(26): 43771-43789. doi: 10.1364/OE.503850 pmid: 38178466
[42]	ZHANG YIFAN, FITCH P, VILAS M P, et al, 2019. Applying multi-layer artificial neural network and mutual information to the prediction of trends in dissolved oxygen[J]. Frontiers in Environmental Science, 7: 46.
[43]	ZHAO YANPING, SONG YUMEI, CUI JINLI, et al, 2020. Assessment of water quality evolution in the Pearl River estuary (South Guangzhou) from 2008 to 2017[J]. Water, 12( 1): 59.
[44]	ZHENG YUANNING, LI CAI, ZHANG XIANQING, et al, 2024. Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island[J]. Ecological Indicators, 159: 111693.
[45]	ZHI WEI, APPLING A P, GOLDEN H E, et al, 2024. Deep learning for water quality[J]. Nature Water, 2(3): 228-241.

年份	数据采集时间	覆盖范围	站点数
2012	9月30日—10月25日	14°00′—22°12′N, 110°12′—119°00′E	37
2013	8月9日—9月2日	10°00′—20°06′N, 110°30′—114°00′E	61
2015	6月21日—7月17日	17°24′—22°30′N, 109°30′—119°00′E	43
2016	9月3日—9月23日	10°00′—21°00′N, 111°30′—119°00′E	23
2017	10月1日—10月23日	18°24′—21°54′N, 110°18′—114°54′E	4
2018	6月12日—6月22日	21°30′—23°06′N, 113°12′—116°54′E	7
	8月18日—8月25日	17°54′—21°18′N, 110°36′—115°18′E	1
2019	9月27日—10月5日	17°42′—23°00′N, 110°30′—117°06′E	3
2020	8月28日—9月4日	18°42′—22°00′N, 107°24′—117°00′E	32
	6月1日—6月29日	17°24′—23°06′N, 110°00′—117°36′E	22
2022	8月14日—6月22 日	14°00′—22°24′N, 110°12′—119°00′E	27
2023	6月23日—7月20日	10°00′—20°06′N, 110°30′—114°00′E	129

算法	超参数	含义	设置值
RF	n_estimators	决策树个数	3
	max_depth	决策数的最大深度	7
	random_state	随机种子	90

基于水文气象参数的南海西部叶绿素a估算^*

Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters^*

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 45

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	刘慧, 张辉贤, 刘馨蔓, 林强, 沈萍萍. 南海小叶海蛞蝓(Phyllidiella nanhaiensis sp. nov.)线粒体基因组特征与系统进化[J]. 热带海洋学报, 2025, 44(1): 1-8.
[2]	刘杰, 闫桐, 经志友. 南海西北部连续台风激发的近惯性内波观测研究[J]. 热带海洋学报, 2025, 44(1): 66-81.
[3]	徐超, 龙丽娟, 李莎, 袁丽, 徐晓璐. 南海及其附属岛礁海洋科学考察历史资料系统整编3. 数据共享服务及应用[J]. 热带海洋学报, 2024, 43(5): 158-165.
[4]	徐超, 龙丽娟, 李莎, 何云开, 袁丽, 徐晓璐. 南海及其附属岛礁海洋科学考察历史资料系统整编1. 资料整编技术及应用[J]. 热带海洋学报, 2024, 43(5): 143-149.
[5]	徐超, 龙丽娟, 李莎, 徐晓璐, 袁丽. 南海及其附属岛礁海洋科学考察历史资料系统整编2. 数据治理技术与应用[J]. 热带海洋学报, 2024, 43(5): 150-157.
[6]	齐焕东, 朱程, 李序春, 景昕蒂, 宋德瑞. 基于规则集和多层感知机的Argo温度数据质量控制方法^*[J]. 热带海洋学报, 2024, 43(5): 190-202.
[7]	柳原, 柯志新, 李开枝, 谭烨辉, 梁竣策, 周伟华. 人类活动和沿岸流影响下的粤东近海浮游动物群落特征[J]. 热带海洋学报, 2024, 43(4): 98-111.
[8]	刘玓玓, 张喜洋, 孙富林, 王明壮, 谭飞, 施祺, 王冠, 杨红强. 南海海滩岩微生物群落结构和特定菌株对其成因机制的启示*[J]. 热带海洋学报, 2024, 43(4): 112-122.
[9]	江绿苗, 陈天然, 赵宽, 张婷, 许莉佳. 南海北部涠洲岛边缘珊瑚礁的生物侵蚀实验研究[J]. 热带海洋学报, 2024, 43(3): 155-165.
[10]	许莉佳, 廖芝衡, 陈辉, 王永智, 黄柏强, 林巧云, 甘健锋, 杨静. 南海北部珊瑚群落结构特征及其对海洋热浪事件的响应[J]. 热带海洋学报, 2024, 43(3): 58-71.
[11]	邱燕, 鞠东, 黄文凯, 王英民, 聂鑫. 南海中央海盆海底初始扩张时间的重新认定[J]. 热带海洋学报, 2024, 43(2): 154-165.
[12]	赵明辉, 袁野, 张佳政, 张翠梅, 高金尉, 王强, 孙珍, 程锦辉. 南海北部被动陆缘洋陆转换带张裂-破裂研究新进展[J]. 热带海洋学报, 2024, 43(2): 173-183.
[13]	黄谕, 王琳, 麦志茂, 李洁, 张偲. 南海热带岛礁生物土壤结皮中细菌的分离及其固砂特性初步研究[J]. 热带海洋学报, 2023, 42(6): 101-110.
[14]	王辰燕, 史敬文, 颜安南, 康亚茹, 王煜轩, 覃素丽, 韩民伟, 张瑞杰, 余克服. 有机磷酸酯在南海长棘海星中的生物富集特征及来源解析[J]. 热带海洋学报, 2023, 42(5): 30-37.
[15]	李牛, 邸鹏飞, 冯东, 陈多福. 冷泉渗漏对海洋沉积物氧化还原环境地球化学识别的影响——以南海东北部F站位活动冷泉为例^*[J]. 热带海洋学报, 2023, 42(5): 144-153.

基于水文气象参数的南海西部叶绿素a估算*

Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters*

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 45

相关文章 15

Metrics

本文评价

推荐阅读 0

基于水文气象参数的南海西部叶绿素a估算^*

Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters^*