Estimation of Phytoplankton Pigment Concentration in the South China Sea from Hyperspectral Absorption Data

Guifen Wang; Yinxue Zhang; Wenlong Xu; Wen Zhou; Hualian Wu; Zhantang Xu; Wenxi Cao

doi:10.3788/AOS202141.0601002

Acta Optica Sinica, Volume. 41, Issue 6, 0601002(2021)

Estimation of Phytoplankton Pigment Concentration in the South China Sea from Hyperspectral Absorption Data

Guifen Wang^1,2、*, Yinxue Zhang^1,2, Wenlong Xu^1,2, Wen Zhou³, Hualian Wu⁴, Zhantang Xu³, and Wenxi Cao³

¹Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, Jiangsu 210098, China

²College of Oceanography, Hohai University, Nanjing, Jiangsu 210098, China

³State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, Guangdong 510301, China

⁴CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, Guangdong 510301, China

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Figures & Tables(13)

Fig. 1. Regression relationship between concentration of TChl a and concentration of accessory pigments (AP)

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Fig. 2. Representative spectral absorption coefficients and their derivatives. (a) 4^th derivative spectrum; (b) 2^nd derivative spectrum; (c)1^st derivative spectrum; (d) absorption coefficient

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Fig. 3. Correlation coefficients between selected phytoplankton pigments concentration and derivative spectra. (a)(b) 1^st derivative spectra; (c)(d) 2^nd derivative spectra; (e)(f) 4^th derivative spectra

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Fig. 4. Comparison between the predicted value from the PLS model based on 2^nd derivative spectrum and the measured pigment concentrations for the validation data set, in which the 1∶1 ratio is shown as a solid line. (a) TChl a; (b) PSC; (c) PPC; (d) 19But; (e) Fuco; (f) 19Hex; (g) Diadino; (h) Zea

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Fig. 5. Comparison between the predicted value from the PLS model based on 4^th derivative spectrum and the measured pigment concentrations for the validation data set, in which the 1∶1 ratio is shown as a solid line. (a) TChl a; (b) PSC; (c) PPC; (d) 19But; (e) Fuco; (f) 19Hex; (g) Diadino; (h) Zea

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Fig. 6. Comparison of the validation results between the PLS regression model based on the 2^nd and 4^th derivative spectra and the empirical model based on total TChl a concentration. (a) Determination coefficient R² for linear regression between the predicted and measured pigment concentrations; (b) RMSE; (c) median percent difference; (d) mean percent difference

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Fig. 7. Validation results of the PLS regression model based on the 2^nd derivative spectrum and data collected in different months. (a) Determination coefficient R² for linear regression between the predicted and measured pigment concentrations; (b) RMSE; (c) median percent difference; (d) mean percent difference

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Table 1. Cruise information in the South China Sea between 2006—2015
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Table 1. Cruise information in the South China Sea between 2006—2015
Cruise Period Number of samples
200609 KFHC 2006.09 24
200708 KFHC 2007.08 27
200808 KFHC 2008.08 14
200909 KFHC 2009.09 48
201004 NSFC 2010.04 89
201212 NSFC 2012.12 97
201308 NSFC 2013.08 37
201506 NSFC 2015.06 95

Table 2. Used pigments and their abbreviations

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Table 2. Used pigments and their abbreviations

Pigment name	Abbreviation
Chlorophyll a	Chl a
Chlorophyll b	Chl b
Chlorophyll c₁,c₂	Chl c₁₊₂
Chlorophyll c₃	Chl c₃
Divinyl Chlorophyll a	DV Chla
Fucoxanthin	Fuco
Peridinin	Perid
19'-hexanoyloxyfucoxanthin	19Hex
Zeaxanthin	Zea
19'-butanoyloxyfucoxanthin	19But
Alloxanthin	Allo
Diadinoxanthin	Diadino
β-carotene	β caro

Table 3. Statistical distribution of different pigments, and the correlation coefficients R of log-transformed concentrations between accessory pigments and total Chlorophyll a (N=431)

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Table 3. Statistical distribution of different pigments, and the correlation coefficients R of log-transformed concentrations between accessory pigments and total Chlorophyll a (N=431)

Pigment	Range	Mean	Standard deviation	Correlation coefficient R
TChl a	[0.027,2.317]	0.262	0.261	1.00
PSC	[0.007,1.029]	0.087	0.115	0.87
PPC	[0.011,0.442]	0.091	0.047	0.58
19But	[0.001,0.205]	0.020	0.029	0.78
Fuco	[0.001,0.888]	0.027	0.072	0.78
19Hex	[0.004,0.209]	0.035	0.036	0.84
Diadino	[0.001,0.070]	0.007	0.008	0.72
Zea	[0.001,0.355]	0.062	0.034	0.03

Table 4. PLS parameters of 2^nd derivative spectrum and 4^th derivative spectrum models

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Table 4. PLS parameters of 2^nd derivative spectrum and 4^th derivative spectrum models

PLS model	Pigment	N	${R_{MSE}}_{}$ /(mg·m^-3)	${η_{x}}_{}$ /%	${η_{y}}_{}$ /%	R²	a	b
Based on 2^ndderivative spectrum	TChl a	7	0.129	87.92	79.27	0.72	0.761	0.060
	PSC	4	0.054	82.69	79.11	0.74	0.748	0.019
	PPC	4	0.034	82.23	47.33	0.38	0.413	0.052
	19But	3	0.017	80.15	66.52	0.63	0.646	0.006
	Fuco	5	0.035	84.62	83.76	0.72	0.725	0.006
	19Hex	3	0.023	79.49	63.27	0.57	0.617	0.013
	Diadino	8	0.004	89.86	80.58	0.72	0.745	0.002
	Zea	4	0.027	82.22	39.18	0.28	0.315	0.044
Based on 4^thderivative spectrum	TChl a	6	0.139	75.27	75.94	0.68	0.716	0.071
	PSC	5	0.057	72.36	77.62	0.70	0.718	0.022
	PPC	12	0.036	89.42	52.55	0.30	0.392	0.054
	19But	5	0.018	73.88	66.35	0.59	0.626	0.006
	Fuco	5	0.040	69.85	78.20	0.64	0.647	0.008
	19Hex	2	0.022	58.22	61.50	0.58	0.604	0.013
	Diadino	6	0.004	75.04	76.45	0.67	0.711	0.002
	Zea	12	0.028	89.44	43.45	0.20	0.274	0.046

Table 5. Validation results for the PLS model based on the 2^nd and 4^th derivative spectra

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Table 5. Validation results for the PLS model based on the 2^nd and 4^th derivative spectra

PLS model	Pigment	R²	a	b	${R_{MSE}}_{}$ /(mg·m^-3)	N	${Δ_{MDPD}}_{}$ /%	${Δ_{MPD}}_{}$ /%
PLS model based on 2^ndderivative spectrum	TChl a	0.75	0.615	0.074	0.169	0	24.99	37.83
	PSC	0.97	0.714	0.011	0.049	0	25.83	36.00
	PPC	0.54	0.296	0.056	0.045	0	21.85	31.41
	19But	0.83	0.704	0.007	0.014	0	30.95	62.03
	Fuco	0.88	0.558	0.006	0.045	31	75.01	118.31
	19Hex	0.82	0.699	0.012	0.017	0	27.03	42.87
	Diadino	0.73	0.543	0.002	0.006	1	30.72	37.83
	Zea	0.59	0.283	0.040	0.033	0	28.69	114.26
PLS model based on 4^thderivative spectrum	TChl a	0.72	0.563	0.089	0.180	1	27.29	43.65
	PSC	0.89	0.636	0.020	0.063	3	33.22	51.14
	PPC	0.38	0.289	0.056	0.048	0	23.79	37.18
	19But	0.80	0.688	0.007	0.015	1	32.13	70.45
	Fuco	0.67	0.415	0.010	0.060	33	80.94	153.48
	19Hex	0.83	0.669	0.009	0.018	1	21.65	33.50
	Diadino	0.71	0.461	0.003	0.006	0	24.08	35.88
	Zea	0.43	0.290	0.038	0.034	2	35.64	103.47

Table 6. Empirical model's coefficients(A,B) based on TChl a and the validation results

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Table 6. Empirical model's coefficients(A,B) based on TChl a and the validation results

Pigment	A	B	R²	a	b	${R_{MSE}}_{}$ /(mg·m^-3)	${Δ_{MDPD}}_{}$ /%	${Δ_{MPD}}_{}$ /%
PSC	0.372	1.144	0.83	0.808	0.013	0.057	34.91	41.82
PPC	0.149	0.310	0.28	0.236	0.075	0.049	20.62	42.17
19But	0.063	0.805	0.44	0.366	0.013	0.025	63.85	118.41
Fuco	0.172	1.912	0.59	0.656	0.009	0.058	48.25	63.89
19Hex	0.099	0.701	0.75	0.583	0.016	0.021	33.35	56.19
Diadino	0.026	0.977	0.84	0.742	0.002	0.004	24.17	28.44
Zea	0.079	0.128	0.00	0.009	0.066	0.044	26.78	212.20

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Guifen Wang, Yinxue Zhang, Wenlong Xu, Wen Zhou, Hualian Wu, Zhantang Xu, Wenxi Cao. Estimation of Phytoplankton Pigment Concentration in the South China Sea from Hyperspectral Absorption Data[J]. Acta Optica Sinica, 2021, 41(6): 0601002

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Paper Information

Category: Atmospheric Optics and Oceanic Optics

Received: Sep. 14, 2020

Accepted: Nov. 9, 2020

Published Online: Apr. 7, 2021

The Author Email: Wang Guifen (guifenwang@hhu.edu.cn)

DOI:10.3788/AOS202141.0601002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Cruise information in the South China Sea between 2006—2015

Table 1. Cruise information in the South China Sea between 2006—2015

Table 2. Used pigments and their abbreviations

Table 2. Used pigments and their abbreviations

Table 3. Statistical distribution of different pigments, and the correlation coefficients R of log-transformed concentrations between accessory pigments and total Chlorophyll a (N=431)

Table 3. Statistical distribution of different pigments, and the correlation coefficients R of log-transformed concentrations between accessory pigments and total Chlorophyll a (N=431)

Table 4. PLS parameters of 2nd derivative spectrum and 4th derivative spectrum models

Table 4. PLS parameters of 2nd derivative spectrum and 4th derivative spectrum models

Table 5. Validation results for the PLS model based on the 2nd and 4th derivative spectra

Table 5. Validation results for the PLS model based on the 2nd and 4th derivative spectra

Table 6. Empirical model's coefficients(A,B) based on TChl a and the validation results

Table 6. Empirical model's coefficients(A,B) based on TChl a and the validation results

Table 4. PLS parameters of 2^nd derivative spectrum and 4^th derivative spectrum models

Table 4. PLS parameters of 2^nd derivative spectrum and 4^th derivative spectrum models

Table 5. Validation results for the PLS model based on the 2^nd and 4^th derivative spectra

Table 5. Validation results for the PLS model based on the 2^nd and 4^th derivative spectra