上海城市水体富营养化关键参量的遥感反演与时序分析

doi:10.3969/j.issn.1000-5641.2022.01.015

摘要/Abstract

摘要：

河口城市受到流域、海洋和局地人类活动的强烈影响, 其水体面临污染和富营养化问题, 对城市生态、生产、生活等造成极大压力. 本项研究针对上海这一特大河口城市的不同水体类型, 利用Sentinel-2遥感影像及水体实测光谱数据, 构建了叶绿素a浓度和浊度两个富营养化的关键水质参量遥感快速反演模型, 并分析了这两个关键参量的时序变化. 结果表明: 基于遥感的叶绿素a浓度、浊度, 反演模型精度良好, 相关系数 (R²) 分别为0.87、0.95, 均方根误差 (RMSE) 分别为4.33 μg/L、8.93 NTU. 2019年时序分析表明, 上海城市水体叶绿素a浓度和浊度均为夏季最高, 冬季最低. 从水体类型上看, 叶绿素a浓度从高到低为: 养殖场/种植塘、永久性淡水湖、库塘、永久性河流、运河/输水河水体, 浊度从高到低为: 养殖场/种植塘、永久性河流、运河/输水河、永久性淡水湖、库塘. 对2019年叶绿素a浓度和浊度的时序变化分析发现, 在人类活动干扰较小的水体中, 叶绿素a浓度和浊度的相关性较强; 而人类活动影响较大的水体中, 两者的相关性较弱. 研究表明, 利用Sentinel-2卫星影像可有效反演城市水体叶绿素a浓度和浊度, 准确跟踪水体富营养化时序变化, 可为其他城市内陆水体的水环境监测提供参考和借鉴.

关键词: Sentinel-2, 富营养化, 叶绿素a, 浊度, 时空变化, 上海

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

中图分类号:

P407.1

李嘉皓, 田波, 曹芳, 胡越凯, 段元强, 谢泽昊, 彭亚, 姜文浩, 范惠芳. 上海城市水体富营养化关键参量的遥感反演与时序分析[J]. 华东师范大学学报（自然科学版）, 2022, 2022(1): 135-147.

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.

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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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