柘林湾近岸水产养殖区水域叶绿素a浓度反演

doi:10.11978/2019110

Abstract

Abstract:

Mariculture has become a major source of pollution in offshore waters. Chlorophyll-a, as a parameter of primary productivity, is an important indicator of water quality evaluation. We took Zhelin Bay of Guangdong Province as our study area. Using Sentinel-2 spectral image on September 4, 2018 and in-situ measured chlorophyll-a concentration, we constructed an estimation model of chlorophyll-a concentration to obtain the spatial distribution of chlorophyll-a concentration. In the chlorophyll-a concentration inversion model, we selected a linear regression model, a three-band model and the Normalized Difference Chlorophyll Index (NDCI) for comparative analysis. Through comparison and evaluation, a model with high inversion accuracy was used to estimate the chlorophyll-a concentration in multiple months of 2018 and analyze its distribution characteristics. The results showed that the inversion accuracy of the NDCI model was significantly higher than that of the other models. The decision coefficient R² of the NDCI model was 0.8, the root mean square error (RMSE) was 9.7, and the mean absolute percentage error (MAPE) was 0.99. The time applicability of the NDCI model was tested by the measured data, which showed that the NDCI model could more accurately and effectively estimated the spatial distribution characteristics of chlorophyll-a concentration. The chlorophyll-a concentration showed a trend of decreasing from nearshore to the outside of the bay. The overall trend of chlorophyll-a concentration in the aquaculture area was as follows: pond breeding area > tidal flat breeding area > cage culture area > floating raft breeding area. Under the influence of water exchange, rainfall and culture activities, the concentration of chlorophyll-a in the culture area of the fish pond was the lowest in February when the fish was in the seedling stage, and its change trend was February < April < June < December. This study provides a valuable reference for environmental monitoring of marine aquaculture waters in Zhelin Bay.

Key words: marine aquaculture, chlorophyll-a concentration, back analysis, Sentinel-2, NDCI model

CLC Number:

P714.11

PAN Cuihong, XIA Lihua, WU Zhifeng, WANG Meng, XIE Xuetong, WANG Fang. Remote sensing retrieval of chlorophyll-a concentration in coastal aquaculture area of Zhelin Bay[J].Journal of Tropical Oceanography, 2021, 40(1): 142-153.

Figures/Tables 14

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养殖类型	样本	浓度范围/(μg·L^-1)	平均浓度/(μg·L^-1)
池塘养殖区	A20, A21, A22, A23, A25, A26, A39, A27, A30, A31, A29, A35, A45, A34, A24, A46, A37, A38, A43	15.63~115.78	77.67
牡蛎养殖区	A9, A28, A33, A40, A42, A44, A36, A47	5.56~56.6	24.92
网箱养殖区	A2, A3, A8, A11, A12, A13, A14, A16	3.66~38.62	16.55
浮筏养殖区	A4, A5, A6, A17, A18, A19	3.18~26.90	15.94
非养殖区	A1, A7, A10, A15, A32, A41	3.22~14.25	7.87

Sentinel-2波段	中心波长/nm
波段1(海岸线气溶胶)	443
波段2(蓝)	490
波段3(绿)	556
波段4(红)	665
波段5(植被红边)	705
波段6(植被红边)	740
波段7(植被红边)	783
波段8(近红外)	842
波段8a(植被红边)	865
波段9(水汽)	945
波段10(短波-卷云)	1375
波段11(短波1)	1610
波段12(短波2)	2190

序号	数据类型	成像时间	备注
1	S2B_MSIL1C	2018-04-02	0.74%, 无云
2	S2B_MSIL1C	2018-06-11	4.44%, 无云
3	S2A_MSIL1C	2018-09-04	8.644%, 少量云
4	S2B_MSIL2A	2018-12-18	0.0%, 无云
5	S2B_MSIL2A	2019-02-06	0.0%, 无云
6	S2B_MSIL2A	2019-08-15	26.42%, 少量云

模型	线性表达式	x变量	R²	RMSE	MAPE	MAE
单波段模型	y=0.1151x-11.744	$ R_{(945 \mathrm{nm})}$	0.58	15.55	1.06	12.24
比值模型	y=58.071x-3.9114	$ \frac{R_{(945 \mathrm{nm})}}{R_{(495 \mathrm{nm})}}$	0.77	10.70	0.75	7.98
三波段模型	y=92.307x+14.176	$ \left[\frac{1}{R_{(665 \mathrm{nm})}}-\frac{1}{R_{(705 \mathrm{nm})}}\right] \times R_{(740 \mathrm{nm})}$	0.76	9.95	1.27	5.70
NDCI模型	y= 238.32x+10.586	$ \frac{R_{(705 \mathrm{nm})}-R_{(665 \mathrm{nm})}}{R_{(665 \mathrm{nm})}+R_{(705 \mathrm{nm})}}$	0.80	9.70	0.99	7.75

模型	RMSE	MAPE	MAE	SD
单波段模型	42.79	14.14	42.33	8.11
比值模型	16.51	5.93	16.38	3.41
三波段模型	6.92	2.62	6.46	2.92
NDCI模型	3.29	0.52	2.19	4.85

Remote sensing retrieval of chlorophyll-a concentration in coastal aquaculture area of Zhelin Bay

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时间	鱼塘养殖区平均浓度/(μg·L^-1)	牡蛎养殖区平均浓度/(μg·L^-1)	网箱养殖区平均浓度/(μg·L^-1)	浮筏养殖区平均浓度/(μg·L^-1)	非养殖水体平均浓度/(μg·L^-1)
2018年4月	45.65	18.10	3.81	5.39	4.12
2018年6月	40.19	17.61	12.65	14.96	14.08
2018年9月	65.52	31.17	19.38	16.25	14.26
2018年12月	75.54	17.08	18.54	17.12	12.54
2019年2月	34.49	10.28	9.71	9.11	5.50