基于水文气象参数的南海西部叶绿素a估算*

doi:10.11978/2024095

Abstract

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

CLC Number:

P731.3

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.

Figures/Tables 15

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

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	$M = log 10 max R rs λ blue R rs λ green$ $M = log 10 max R rs λ blue R rs λ green$	$R rs λ blue = R rs 442 nm$ ; $R rs λ blue = R rs 442 nm$ ; 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)

Tab. 2

Tab. 3

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

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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

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

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

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