Journal of Tropical Oceanography >
Estimation of Chlorophyll-a in the western South China Sea based on hydro-meteorological parameters*
Copy editor: YIN Bo
Received date: 2024-04-26
Revised date: 2024-06-19
Online published: 2024-07-03
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)
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 (Rrs) 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
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 . DOI: 10.11978/2024095
表1 2012年至2023年12次巡航收集的信息概要Tab. 1 A summary of the information collected from 2012 to 2023 on 12 cruises |
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 |
图2 具有Chl-a (f)和Chl-a (aLH)测量数据的2023年航次的站点分布图底图基于自然资源部标准地图服务网站下载的审图号为GS(2023)2762号的标准地图制作, 底图无修改 Fig. 2 Location of stations for the 2023 cruise with Chl-a (f) and Chl-a (aLH) measurements |
图3 两种方法在同一站点得到的测量结果对比Fig. 3 The results of two types of measurements at the same stations |
图4 C057站点的两组数据在5~30m (a)和10~15m (b)的变化曲线对比Fig. 4 Comparison of the change curves of two measurements of data at 5~30 m (a) and 10~15 m (b) of the C057 station |
表2 本研究中利用的经验算法简介Tab. 2 Summary of empirical formulas used in this study |
算法名称 | 算法公式 | 参数值 | 文献来源 |
---|---|---|---|
OC4 | | ; ; a0=3.562; a1=-13.144; a2=15.409; a3=-5.997; a4=0.4 | O’Reilly等(2019) |
Aiken | | a0= -0.395; a1=0.803; a2=-0.158; a3=-0.118 | Aiken等(1996) |
Tassan | | a0=-1.012; a1=-0.439; a2=0.713; a3=-0.176 | Tassan (1994) |
注: 表示蓝光波段的遥感反射率, 表示绿光波段的遥感反射率。在本文中442nm对应蓝光波段, 532nm对应绿光波段。 表示442nm处的遥感反射率, 表示532nm处的遥感反射率, 表示448nm处的遥感反射率。 、 、 、 分别表示442nm、420nm、532nm、488nm处的水面之下遥感反射率。M表示不同经验算法中使用的波段比值变量, a0, a1, a2, a3, a4均为经验公式中的常数代称 |
表3 本研究中利用的随机森林算法的超参数Tab. 3 Hyper-parameters of RF algorithm used in this study |
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 |
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