Remote sensing inversion and time-series analysis of critical parameters for eutrophication assessment of urban waters in Shanghai

doi:10.3969/j.issn.1000-5641.2022.01.015

Abstract

Abstract:

Estuarine cities are heavily influenced by anthropogenic activities. In turn, their water bodies often face serious eutrophication and pollution problems, thereby exerting significant pressure on the urban production and living environment. This study focuses on the water bodies in the city of Shanghai, an important estuarine megacity in China. Using the Sentinel-2 satellite and in situ measured water spectrum data, we built an inversion model for rapid identification of two critical parameters for eutrophication assessment, namely chlorophyll-a concentration and turbidity. We subsequently analyzed the spatial and temporal variability of these two parameters using time-series satellite data. Our results showed that the correlation coefficient (R²) of turbidity and chlorophyll-a concentration inversion based on remote sensing was 0.95 and 0.87, respectively; the root mean square error (RMSE) was 4.33 μg/L and 8.93 NTU, respectively. Time-series analysis from 2019 showed that both chlorophyll-a concentration and turbidity in different urban water bodies were highest in the summer and lowest in the winter in Shanghai. Specifically, chlorophyll-a concentrations across water bodies decreased in the following sequence: aqua-culture/planting ponds, permanent freshwater lakes, reservoir ponds, permanent rivers, and canals/transportation rivers. In the case of turbidity, the water bodies ordered from highest to the lowest followed the sequence: aqua-culture/planting ponds, permanent rivers, canals/water delivery rivers, permanent freshwater lakes, and reservoir ponds. Time series analysis of chlorophyll-a concentrations and turbidity from 2019 showed that in water bodies with less human disturbance, the correlation between chlorophyll-a concentration and turbidity was stronger than those that were heavily influenced by anthropogenic activities. The use of Sentinel-2 satellite images to retrieve the chlorophyll-a concentration and turbidity in water bodies can generally provide information on the eutrophication status of water bodies in Shanghai; the data, moreover, can serve as a reference for aquatic environmental monitoring of inland water bodies in other cities.

Key words: Sentinel-2, eutrophication, chlorophyll-a, turbidity, temporal and spatial changes, Shanghai

CLC Number:

P407.1

Jiahao LI, Bo TIAN, Fang CAO, Yuekai HU, Yuanqiang DUAN, Zehao XIE, Ya PENG, Wenhao JIANG, Huifang FAN. Remote sensing inversion and time-series analysis of critical parameters for eutrophication assessment of urban waters in Shanghai[J]. Journal of East China Normal University(Natural Science), 2022, 2022(1): 135-147.

Figures/Tables 13

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水体指数	阈值	分类精度	Kappa 系数
NDWI	–0.2850	66.00%	0.1636
mNDWI	0.0195	98.60%	0.8814
CIWI	0.5112	60.10%	0.1289

序号	遥感因子	方程形式	参考模型
1	x=R_rs(705)/R_rs(665)	y =3.2998x ^3.8235	Neil等^[27]
2	x= R_rs(705)/R_rs(665)	y=56.183x– 49.535	Gilerson等^[26]
3	x= (R_rs(705)–R_rs(665))/ (R_rs(705) + R_rs(665))	y=308.23x ² + 78.224x + 4.6854	Mishra等^[29]
4	R_rs(705)、R_rs(740)、R_rs(783)、R_rs(842)	多元线性回归	Maeda等^[28]
5	x=R^–1_rs(740)·(R^–1_rs(665)–R^–1_rs (705))	y= 72.632x + 4.9605	Dall等^[30]

序号	遥感因子	方程形式	参考模型
1	x = R_rs(665)/R_rs(490)	y = 2.5797e^(2.1653x)	Kratzer等^[34]
2	x =R_rs(665)/R_rs(560)	y = 401.25x ²–579.43x + 240.59	Hou等^[1]
3	x =R_rs(842)/R_rs(665)	y=62.167x^0.65	Doxaran等^[33]
4	x =R_rs(842)	y = –117312 x ² + 7073x – 9.1356	Petus等^[32]
5	R_rs(665)、R_rs(705)、R_rs(740)、R_rs(783)、R_rs(865)	多元线性回归	Maeda等^[28]
6	x = R_rs(783)	y = 4904.6x ^1.0668	本文的方法

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