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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参考文献 38

1	HOU X, FENG L, DUAN H, et al. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sensing of Environment, 2017, 190, 107- 121. doi: 10.1016/j.rse.2016.12.006
2	PAERL H W. Assessing and managing nutrient-enhanced eutrophication in estuarine and coastal waters: Interactive effects of human and climatic perturbations. Ecological Engineering, 2006, 26 (1): 40- 54. doi: 10.1016/j.ecoleng.2005.09.006
3	ANDERSON D M, GLIBERT P M, BURKHOLDER J M. Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries, 2002, 25 (4): 704- 726. doi: 10.1007/BF02804901
4	2017年《中国生态环境状况公报》(摘录三)[J]. 环境保护, 2018, 46(13): 70-74.
5	MOSES W J, GITELSON A A, BERDNIKOV S, et al. Estimation of chlorophyll- a concentration in case II waters using MODIS and MERIS data—successes and challenges. Environmental Research Letters, 2009, 4 (4): 45005. doi: 10.1088/1748-9326/4/4/045005
6	金相灿, 朱萱. 我国主要湖泊和水库水体的营养特征及其变化. 环境科学研究, 1991, (1): 11- 20. doi: 10.3321/j.issn:1001-6929.1991.01.003
7	SEBASTIÁ-FRASQUET M, AGUILAR-MALDONADO J A, SANTAMARÍA-DEL-ÁNGEL E, et al. Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sensing, 2019, 11 (24): 2926. doi: 10.3390/rs11242926
8	段洪涛, 张柏, 宋开山, 等. 查干湖叶绿素a浓度高光谱定量模型研究. 环境科学, 2006, (3): 3503- 3507.
9	BILOTTA G S, BRAZIER R E. Understanding the influence of suspended solids on water quality and aquatic biota. Water Research, 2008, 42 (12): 2849- 2861. doi: 10.1016/j.watres.2008.03.018
10	ERFTEMEIJER P L A, ROBIN LEWIS R R. Environmental impacts of dredging on seagrasses: A review. Marine Pollution Bulletin, 2006, 52 (12): 1553- 1572. doi: 10.1016/j.marpolbul.2006.09.006
11	YANG Y, YAN B, SHEN W. Assessment of point and nonpoint sources pollution in Songhua River Basin, Northeast China by using revised water quality model. Chinese Geographical Science, 2010, 20 (1): 30- 36. doi: 10.1007/s11769-010-0030-3
12	GUAN Q, FENG L, HOU X, et al. Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations. Remote Sensing of Environment, 2020, 246, 111890. doi: 10.1016/j.rse.2020.111890
13	黄启会, 贺中华, 梁虹, 等. 基于高光谱数据的百花湖叶绿素a浓度估算. 环境科学与技术, 2019, 42 (1): 134- 141.
14	徐祎凡, 施勇, 李云梅. 基于环境一号卫星高光谱数据的太湖富营养化遥感评价模型. 长江流域资源与环境, 2014, 23 (8): 1111- 1118. doi: 10.11870/cjlyzyyhj201408010
15	DRUSCH M, DEL BELLO U, CARLIER S, et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 2012, 120, 25- 36. doi: 10.1016/j.rse.2011.11.026
16	阮仁良. 上海市水资源和水环境的可持续发展. 水资源保护, 2003, (1): 21- 24. doi: 10.3969/j.issn.1004-6933.2003.01.006
17	TIAN B, ZHOU Y, THOM R M, et al. Detecting wetland changes in Shanghai, China using FORMOSAT and Landsat TM imagery. Journal of Hydrology, 2015, 529, 1- 10. doi: 10.1016/j.jhydrol.2015.07.007
18	唐军武, 田国良, 汪小勇, 等. 水体光谱测量与分析Ⅰ: 水面以上测量法. 遥感学报, 2004, (1): 37- 44.
19	MUELLER J L, MOREL A, FROUIN R, et al. Ocean optics protocols for satellite ocean color sensor validation, revision 4. Volume III: Radiometric measurements and data analysis protocols [R]. Goddard Space Flight Space Center, 2003.
20	RUESCAS A B, PEREIRA-SANDOVAL M, TENJO C, et al. Sentinel-2 atmospheric correction inter-comparison over two lakes in Spain and Peru-Bolivia [C/OL]. [2021-10-08]. https://isp.uv.es/papers/Ruescas16CLEO.pdf.
21	SÒRIA-PERPINYÀ X, VICENTE E, URREGO P, et al. Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images. Science of the Total Environment, 2020, 698, 134305. doi: 10.1016/j.scitotenv.2019.134305
22	HEDLEY J D, HARBORNE A R, MUMBY P J. Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos. International Journal of Remote Sensing, 2005, 26 (10): 2107- 2112. doi: 10.1080/01431160500034086
23	徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究. 遥感学报, 2005, (5): 589- 595.
24	MEHMET S, BÜLENT S. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13 (1): 146- 165. doi: 10.1117/1.1631315
25	O'REILLY J E , MARITORENA S , SIEGEL D A , et al. Ocean color chlorophyll-a algorithms for SeaWiFS, OC2, and OC4: Version 4 [R]. Seawifs Post Launch Calibration & Validation Analyses, 2000: 9-23.
26	GILERSON A A, GITELSON A A, ZHOU J, et al. Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Opt Express, 2010, 18 (23): 24109- 24125. doi: 10.1364/OE.18.024109
27	NEIL C, SPYRAKOS E, HUNTER P D, et al. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sensing of Environment, 2019, 229, 159- 178. doi: 10.1016/j.rse.2019.04.027
28	MAEDA E E, LISBOA F, KAIKKONEN L, et al. Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data. Remote Sensing of Environment, 2019, 221, 609- 620. doi: 10.1016/j.rse.2018.12.006
29	MISHRA S, MISHRA D R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 2012, 117, 394- 406. doi: 10.1016/j.rse.2011.10.016
30	DALL OLMO G, GITELSON A A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: Experimental results. Applied Optics, 2005, 44 (3): 412- 422. doi: 10.1364/AO.44.000412
31	SALAMA M S, VERHOEF W. Two-stream remote sensing model for water quality mapping: 2SeaColor. Remote Sensing of Environment, 2015, 157, 111- 122. doi: 10.1016/j.rse.2014.07.022
32	PETUS C, CHUST G, GOHIN F, et al. Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery. Continental Shelf Research, 2010, 30 (5): 379- 392. doi: 10.1016/j.csr.2009.12.007
33	DOXARAN D, FROIDEFOND J, CASTAING P, et al. Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data. Estuarine, Coastal and Shelf Science, 2009, 81 (3): 321- 332. doi: 10.1016/j.ecss.2008.11.013
34	KRATZER S, KYRYLIUK D, EDMAN M, et al. Synergy of satellite, in situ and modelled data for addressing the scarcity of water quality information for eutrophication assessment and monitoring of Swedish Coastal Waters. Remote Sensing, 2019, 11 (17): 2051. doi: 10.3390/rs11172051
35	BOWERS D G, BRAITHWAITE K M, NIMMO-SMITH W A M, et al. Light scattering by particles suspended in the sea: The role of particle size and density. Continental Shelf Research, 2009, 29 (14): 1748- 1755. doi: 10.1016/j.csr.2009.06.004
36	赵雷, 廖晓斌, 周真明, 等. 泉州某水库水质季节变化原因及对策探究. 中国给水排水, 2020, 36 (1): 36- 42.
37	李志波, 季丽, 李丹丹, 等. 罗非鱼精养池塘水质变化规律和沉积物产污系数研究. 环境科学与技术, 2015, 38 (5): 168- 174.
38	VANHELLEMONT Q, RUDDICK K. Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. Remote Sensing of Environment, 2018, 216, 586- 597. doi: 10.1016/j.rse.2018.07.015

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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