高分一号WFV影像的深圳市水库COD<sub>Mn</sub>浓度时空分布特征

李俊; 张文志; 邓孺孺; 鲁志文; 梁业恒; 沈雪娇; 熊龙海; 刘永明

doi:10.11834/jrs.20219380

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高分一号WFV影像的深圳市水库COD_Mn浓度时空分布特征
Study of spatial—temporal characteristics for COD_Mn in Shenzhen reservoir based on GF-1 WFV
2022年26卷第8期页码：1562-1574
纸质出版日期： 2022-08-07 ，
DOI： 10.11834/jrs.20219380

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李俊，张文志，邓孺孺，鲁志文，梁业恒，沈雪娇，熊龙海，刘永明.2022.高分一号WFV影像的深圳市水库COD_Mn浓度时空分布特征.遥感学报，26（8）： 1562-1574

Li J，Zhang W Z，Deng R R，Lu Z W，Liang Y H，Shen X J，Xiong L H and Liu Y M. 2022. Study of spatial—temporal characteristics for COD_Mn in Shenzhen reservoir based on GF-1 WFV. National Remote Sensing Bulletin， 26（8）：1562-1574
李俊，张文志，邓孺孺，鲁志文，梁业恒，沈雪娇，熊龙海，刘永明.2022.高分一号WFV影像的深圳市水库COD_Mn浓度时空分布特征.遥感学报，26（8）： 1562-1574 DOI： 10.11834/jrs.20219380.

Li J，Zhang W Z，Deng R R，Lu Z W，Liang Y H，Shen X J，Xiong L H and Liu Y M. 2022. Study of spatial—temporal characteristics for COD_Mn in Shenzhen reservoir based on GF-1 WFV. National Remote Sensing Bulletin， 26（8）：1562-1574 DOI： 10.11834/jrs.20219380.

摘要

COD

是反映水体有机污染程度的一个重要水质参数。地表水有机污染遥感监测主要面临两个挑战：技术方法大多基于经验模型，依赖大量实测数据；有机污染评价的综合性指标，水质参数不明确。针对上述问题，本文从辐射传输机理出发，基于研究区水体特征，考虑悬浮泥沙、叶绿素、耗氧性有机物3大水质因子，提出一种反演机理清晰、参数意义明确的像元反射率与COD

浓度的物理模型。通过深圳市3大水库COD

浓度反演与验证，决定系数

达到0.832，模型方法可靠明。对3大水库COD

浓度时空分布特征研究，结果表明：（1）3大水库总体COD

浓度不高，受到轻度有机污染。（2）浓度高值区多在库角居民区附近，水库连接处未出现污染扩散。（3）2018年3月—2019年5月，库区水质改善，与2018年深圳市治水专项活动背景保持一致，建议水库水质保护核心是控制外源污染，避免丰水期污染源的输入。本文的模型方法是基于广东省典型水体光学参数，而水体固有光学特征具有季节差异，未来将进一步研究水体固光学特征变化模式，以提高模型的稳健性。此外，还可结合高分六号等谱段更多的卫星开展浅水区COD

浓度反演的研究。

Abstract

Permanganate index (COD

) is an important water quality parameter to reflect the degree of organic pollution. At present

the retrieval of organic pollution by remote sensing technology is mostly based on empirical models and requires considerable manpower for data collection. Meanwhile

it has time and space limitations because it cannot process each image under different imaging conditions adaptively. The integrated water quality index

CDOM

and DOC of the inverted parameters are not water quality indexes. Thus

they cannot be directly used for actual water quality evaluation. Therefore

a novel quantitative remote sensing technology method for the retrieval of water permanganate index with clear understanding on mechanism is proposed.

The method based on the radiation transmission process of electromagnetic waves and the characteristics of the water body in the study area consider the three major water quality factors of suspended sediment

chlorophyll

and oxygen-consuming organic

analyze the absorption and scattering coefficients of oxygen-consuming organic matter

and separate the contribution of the water column to the remote sensing signal from the effect of the bottom. The diffuse extinction coefficients (

) of water quality components are expressed as functions of in-water absorption (

) and scattering (

). Finally

the concentration of COD

was derived with the remote-sensing reflectance below the surface (

The experiment on the GF-1 Wide Field of View (WFV) imageries of the three major reservoirs in Shenzhen shows that the model method is reliable with overall accuracy of

=0.832 and RMSE=46.4%. The spatial—temporal characteristics of the three major reservoirs in Shenzhen during 2018—2019 were investigated. The overall COD

concentration of the three major reservoirs is low with average COD

concentrations of less than 4 mg/L; it is affected by mild organic pollution. No pollution diffusion occurred at the junction of the reservoirs

and the peak concentration mostly appeared near the residential areas at the reservoir corner. The highest hotspot was observed in spring and autumn

whereas the lowest was in rainy summer From March 2018 to May 2019. The water quality improved

consistent with the background of Shenzhen’s special water treatment activities in 2018. The core of reservoir water quality protection is recommended to control external pollution and avoid the input of pollution sources during the flood season.

A distinct advantage of the models is broadly applicable due to their physical basis

which satisfied the application requirements. The model solving method is based on the inherent optical properties of typical water bodies in Guangdong Province

and these properties have seasonal variability. The seasonal variations of inherent optical properties of water bodies can improve the stability of the model. In addition

the spectrum of shallow waters is affected by the depth and the reflection at the bottom. COD

concentration inversion from satellite data with more spectrum bands remains underexplored. The RS scheme used in this study can not only provide support for inland water resource development and policy formulation in Shenzhen

but also a valuable reference for the evolution of inland water organic pollution in other regions.

关键词

CODMn有机污染吸收系数GF-1 WFV深圳水质遥感

Keywords

permanganate indexorganic pollutionabsorption coefficientGF-1 WFVShenzhenwater quality remote sensing

references

AiKen G R, McKnight D M, Wershaw R L and MacCarthy P. 1985. An introduction to humic substances in soil, sediment, and water. Humic Substances in Soil, Sediment, and Water: Geochemistry, Isolation, and Characterization. New York: John Wiley: 1-9

Alparslan E, Coskun H G and Alganci U. 2009. Water quality determination of Küçükçekmece Lake, turkey by using multispectral satellite data. The Scientific World Journal, 9: 425278 [DOI: 10.1100/tsw.2009.135http://dx.doi.org/10.1100/tsw.2009.135]

Bowers D G, Evans D, Thomas D N, Ellis K and Williams P J L B. 2004. Interpreting the colour of an estuary. Estuarine, Coastal and Shelf Science, 59(1): 13-20 [DOI: 10.1016/j.ecss.2003.06.001http://dx.doi.org/10.1016/j.ecss.2003.06.001]

Chen C Q, Tang S L, Pan Z L, Zhan H G, Larson M and Jönsson L. 2007. Remotely sensed assessment of water quality levels in the Pearl River Estuary, China. Marine Pollution Bulletin, 54(8): 1267-1272 [DOI: 10.1016/j.marpolbul.2007.03.010http://dx.doi.org/10.1016/j.marpolbul.2007.03.010]

Chen J S, He D W and Zhang Y. 2003. Is COD a suitable parameter to evaluate the water pollution in The Yellow River. Environmental Chemistry, 22(6): 611-614

陈静生, 何大伟, 张宇. 2003. 黄河水的COD值能够真实反映其污染状况吗. 环境化学, 2(6): 611-614 [DOI: 10.3321/j.issn:0254-6108.2003.06.018http://dx.doi.org/10.3321/j.issn:0254-6108.2003.06.018]

Chen L X. 2017. Design of COD concentration in the eutrophication of water body environment remote sensing monitoring system. Computer Measurement and Control, 25(7): 59-62

陈玲侠. 2017. 富营养化水体环境中COD浓度遥感监测系统设计. 计算机测量与控制, 25(7): 59-62 [DOI: 10.16526/j.cnki.11-4762/tp.2017.07.015http://dx.doi.org/10.16526/j.cnki.11-4762/tp.2017.07.015]

Deng R R, He Z J, Chen X X, Guan L J and Ke D. 2002a. Quantitative analysis on water pollution in the Pearl River estuary by remote sensing method. Acta Scientiarum Naturalium Universitatis Sunyatseni, 41(3): 99-103

邓孺孺, 何执兼, 陈晓翔, 关履基, 柯栋. 2002. 珠江口水域水污染遥感定量分析. 中山大学学报(自然科学版), 41(3): 99-103 [DOI: 10.3321/j.issn:0529-6579.2002.03.026http://dx.doi.org/10.3321/j.issn:0529-6579.2002.03.026]

Deng R R, He Y Q, Qin Y, Chen Q D and Chen L. 2012b. Pure water absorption coefficient measurement after eliminating the impact of suspended substance in spectrum from 400 nm to 900 nm. Journal of Remote Sensing, 16(1): 174-191

邓孺孺, 何颖清, 秦雁, 陈启东, 陈蕾. 2012. 分离悬浮质影响的光学波段(400—900NM)水吸收系数测量. 遥感学报, 16(1): 174-191 [DOI: 10.11834/jrs.2012183http://dx.doi.org/10.11834/jrs.2012183]

Deng R R, He Y Q, Qin Y, Chen Q D and Chen L. 2012b. Measuring pure water absorption coefficient in the near-infrared spectrum (900-2500 nm). Journal of Remote Sensing, 16(1): 192-206

邓孺孺, 何颖清, 秦雁, 陈启东, 陈蕾. 2012. 近红外波段(900—2500 nm)水吸收系数测量. 遥感学报, 16(1): 192-206 [DOI: 10.11834/jrs.2012188http://dx.doi.org/10.11834/jrs.2012188]

Deng R R, Qin Y, Liang Y H, He Y Q, Chen Q D, Xiong L H, Liu X L, Liu Y F, Lu S J, Liu Y M and Lin L. 2015. Method for simultaneously inverting turbidity, COD and chlorophyll concentration of inland water bodies. CN, 201510513928.4

邓孺孺, 秦雁, 梁业恒, 何颖清, 陈启东, 熊龙海, 刘旭拢, 刘英飞, 卢世军, 刘永明, 林梨. 2015. 同时反演内陆水体混浊度、COD和叶绿素浓度的方法. 中国, 201510513928.4

Fichot C G, Downing B D, Bergamaschi B A, Windham-Myers L, Marvin-DiPasquale M, Thompson D R and Gierach M M. 2016. High-resolution remote sensing of water quality in the San Francisco bay-delta estuary. Environmental Science and Technology, 50(2): 573-583 [DOI: 10.1021/acs.est.5b03518http://dx.doi.org/10.1021/acs.est.5b03518]

Fu J, Ji G S, Yang J and Chen Y Q. 1993. Correlative analysis between the reflection spectrum and pollution coefficients of water in the grand canal through Southern Jiangsu. Environmental Science, 14(5): 13-18

傅江, 季耿善, 杨静, 陈玉泉. 1993. 苏南大运河水体反射光谱与污染参数的相关分析. 环境科学, 14(5): 13-18 [DOI: 10.13227/j.hjkx.1993.05.006http://dx.doi.org/10.13227/j.hjkx.1993.05.006]

Gholizadeh M H, Melesse A M and Reddi L. 2016. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16(8): 1298 [DOI: 10.3390/s16081298http://dx.doi.org/10.3390/s16081298]

Gitelson A, Garbuzov G, Szilagyi F, Mittenzwey K H, Karnieli A and Kaiser A. 1993. Quantitative remote sensing methods for real-time monitoring of inland waters quality. International Journal of Remote Sensing, 14(7): 1269-1295 [DOI: 10.1080/01431169308953956http://dx.doi.org/10.1080/01431169308953956]

Guo L F, Gao X H, Lin C, Liu D W and Guo Y. 2010. Remote sensing information model for water organic pollution based on expert classifier. Yellow River, 32(12): 108-111

郭丽峰, 高小红, 林超, 刘德文, 郭勇. 2010. 基于专家分类器的水体有机污染遥感信息模型. 人民黄河, 32(12): 108-111 [DOI: 10.3969/j.‍issn.‍1000-1379.2010.12.045http://dx.doi.org/10.3969/j.‍issn.‍1000-1379.2010.12.045]

Huang M F, Song Q J, Mao Z H, Xing X F, Bai Z A, Gu P and Zhao Z L. 2011. The retrieval model for COD in waters using optical absorption properties of CDOM-a case study at the Shuangtaizi River and the Liaodong Gulf. Acta Oceanologica Sinica, 33(3): 47-54

黄妙芬, 宋庆君, 毛志华, 邢旭峰, 白贞爱, 古平, 赵祖龙. 2011. 应用CDOM光学特性估算水体COD——以辽宁省盘锦市双台子河和辽东湾为例. 海洋学报, 33(3): 47-54

Kutser T. 2012. The possibility of using the Landsat image archive for monitoring long time trends in coloured dissolved organic matter concentration in lake waters. Remote Sensing of Environment, 123: 334-338 [DOI: 10.1016/j.rse.2012.04.004http://dx.doi.org/10.1016/j.rse.2012.04.004]

Lee Z P, Carder K L and Arnone R A. 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27): 5755-5772 [DOI: 10.1364/AO.41.005755http://dx.doi.org/10.1364/AO.41.005755]

Li J Q, Li J G, Zhu L, Shen Q, Dai H Y and Zhu Y F. 2019. Remote sensing identification and validation of urban black and odorous water in Taiyuan city. Journal of Remote Sensing, 23(4): 773-784

李佳琦, 李家国, 朱利, 申茜, 戴华阳, 朱云芳. 2019. 太原市黑臭水体遥感识别与地面验证. 遥感学报, 23(4): 773-784 [DOI: 10.11834/jrs.20197292http://dx.doi.org/10.11834/jrs.20197292]

Li S J, Zhu H C, Chen D Q and Wang L L. 2016. Water quality monitoring based on multiple remote sensing imageries//2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA). Guangzhou: IEEE [DOI: 10.1109/EORSA.2016.7552777http://dx.doi.org/10.1109/EORSA.2016.7552777]

Li S J, Zhang J Q, Guo E L, Zhang F, Ma Q Y and Mu G Y. 2017. Dynamics and ecological risk assessment of chromophoric dissolved organic matter in the Yinma River Watershed: Rivers, reservoirs, and urban waters. Environmental Research, 158: 245-254 [DOI: 10.1016/j.envres.2017.06.020http://dx.doi.org/10.1016/j.envres.2017.06.020]

Lin B Q and Xie S Q. 1988. Aquatic Algae and Water Pollution Monitoring. Shenyang: Liaoning University Press

林碧琴, 谢淑琪. 1988. 水生藻类与水体污染检测. 沈阳: 辽宁大学出版社

Ma R H, Tang J W, Duan H T and Pan D L. 2009. Progress in lake water color remote sensing. Journal of Lake Sciences, 21(2): 143-158

马荣华, 唐军武, 段洪涛, 潘德炉. 2009. 湖泊水色遥感研究进展. 湖泊科学, 21(2): 143-158 [DOI: 10.3321/j.issn:1003-5427.2009.02.001http://dx.doi.org/10.3321/j.issn:1003-5427.2009.02.001]

Mcfeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]

Nie X. 2009. Phosphorus Forms and Release in Sediments of Typical Reservoirs for Drinking Water S Supply in Guangdong Province. Guangzhou. Guangzhou: Ji’nan University (聂祥. 2009. 广东省典型供水水库沉积物中磷形态与释放特征. 广州: 暨南大学)

Philpot W D. 1989. Bathymetric mapping with passive multispectral imagery. Applied Optics, 28(8): 1569-1578 [DOI: 10.1364/AO.28.001569http://dx.doi.org/10.1364/AO.28.001569]

Prangsma G J and Roozekrans J N. 1989. Using NOAA AVHRR imagery in assessing water quality parameters. International Journal of Remote Sensing, 10(4/5): 811-818 [DOI: 10.1080/01431168908903921http://dx.doi.org/10.1080/01431168908903921]

Qi Z X and Deng R R. 2007. The atmospheric correction method for nonhomogeneous atmosphere based on many dark objects. Remote Sensing for Land and Resources, 2: 16-19, 30

齐志新, 邓孺孺. 2007. 多暗像元大气校正方法. 国土资源遥感, (2): 16-19, 30 [DOI: 10.3969/j.issn.1001-070X.2007.02.004http://dx.doi.org/10.3969/j.issn.1001-070X.2007.02.004]

Qin Y. 2015. Optical Shallow Water Remote Sensing Model and its Application on Monitoring Water Quality in Guangdong Province. Guangzhou: Sun Yat-Sen University

秦雁. 2015. 光学浅水遥感模型及其在广东省水质遥感监测的应用. 广州: 中山大学

Shirke S, Pinto S M, Kushwaha V K, Mardikar T and Vijay R. 2016. Object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India. Environmental Monitoring and Assessment, 188(2): 95 [DOI: 10.1007/s10661-015-4981-9http://dx.doi.org/10.1007/s10661-015-4981-9]

Song Y L, Zhang J S, Guo X Y, Zhu J, Wang L, Tao Y and Zhang L. 2017. Spatiotemporal variations of chlorophyll a and its relationship to environmental factors in Shiyan reservoir. Environmental Science, 38(8): 3302-3311

宋云龙, 张金松, 郭小雅, 朱佳, 王丽, 陶益, 张丽. 2017. 石岩水库叶绿素a时空分布及其影响因子分析. 环境科学, 38(8): 3302-3311 [DOI: 10.13227/j.hjkx.201701046http://dx.doi.org/10.13227/j.hjkx.201701046]

Stumpf R P, Davis T W, Wynne T T, Graham J L, Loftin K A, Johengen T H, Gossiaux D, Palladino D and Burtner A. 2016. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae, 54: 160-173 [DOI: 10.1016/j.hal.2016.01.005http://dx.doi.org/10.1016/j.hal.2016.01.005]

Vaillant S, Pouet M F and Thomas O. 2002. Basic handling of UV spectra for urban water quality monitoring. Urban Water, 4(3): 273-281 [DOI: 10.1016/S1462-0758(02)00019-5http://dx.doi.org/10.1016/S1462-0758(02)00019-5]

Vignolo A, Pochettino A and Cicerone D. 2006. Water quality assessment using remote sensing techniques: Medrano Creek, Argentina. Journal of Environmental Management, 81(4): 429-433 [DOI: 10.1016/j.jenvman.2005.11.019http://dx.doi.org/10.1016/j.jenvman.2005.11.019]

Wang J P, Cheng S T and Jia H F. 2004a. Application of artificial neural network technology in water color remote sensing inversion of inland water body using TM data//30th ISPRS Congress Technical Commission IV. Turkey: ISPRS

Wang T, Tan C, Chen L and Tsai Y. 2008. Applying artificial neural networks and remote sensing to estimate ChlorophyⅡ‍-a corcentration in water body. 2008 Second International Symposium on Intelligent Information Technology Application, 540-544[DOI: 10.1109/http://dx.doi.org/10.1109/ⅡTA.2008.279]

Wang Y P, Min Y S, Fu J M and Sheng G Y. 2001. Remote sensing method of water pollution and application on water pollution monitoring in Guangzhou section of Pearl River. Journal of Remote Sensing, 5(6): 460-465

王云鹏, 闵育顺, 傅家谟, 盛国英. 2001. 水体污染的遥感方法及在珠江广州河段水污染监测中的应用. 遥感学报, 5(6): 460-465 [DOI: 10.11834/jrs.20010610http://dx.doi.org/10.11834/jrs.20010610]

Wang Y P, Xia H, Fu J M and Sheng G Y. 2004b. Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data. Science of the Total Environment, 328(1/3): 195-206 [DOI: 10.1016/j.scitotenv.2004.02.020http://dx.doi.org/10.1016/j.scitotenv.2004.02.020]

Wen S, Wang Q, Li Y M, Zhu L, Lv H, Lei S H, Ding X L and Miao S. 2018. Remote sensing identification of urban black-odor water bodies based on high-resolution images: a case study in Nanjing. Environmental Science, 39(1): 57-67

温爽, 王桥, 李云梅, 朱利, 吕恒, 雷少华, 丁潇蕾, 苗松. 2018. 基于高分影像的城市黑臭水体遥感识别: 以南京为例. 环境科学, 39(1): 57-67 [DOI: 10.13227/j.hjkx.201703264http://dx.doi.org/10.13227/j.hjkx.201703264]

Xia X H, Yang Z F, Wang R and Meng L H. 2005. Contamination of oxygen-consuming organics in the Yellow River of China. Environmental Monitoring and Assessment, 110(1/3): 185-202 [DOI: 10.1007/s10661-005-7863-8http://dx.doi.org/10.1007/s10661-005-7863-8]

Xu S G and Wang T X. 2015. Review of research on accumulation process and effect of internal pollution of reservoir. Advances in Science and Technology of Water Resources, 35(5): 162-167

许士国, 汪天祥. 2015. 水库内源污染蓄积过程及影响研究综述. 水利水电科技进展, 35(5): 162-167 [DOI: 10.3880/j.issn.1006-7647.2015.05.022http://dx.doi.org/10.3880/j.issn.1006-7647.2015.05.022]

Yuan W Q. 2004. Study on the Internal Pollution in Xili Reservoir and Its Control. Beijing: Tsinghua University

袁文权. 2004. 西沥水库内源污染及其控制. 北京: 清华大学

Zhang X, Lai J B, Li J G, Wang L, Zhu L and Chen Y J. 2019. Remote sensing recognition of black-odor Waterbodies in Shenzhen city based on GF-1 satellite. Science Technology and Engineering, 19(4): 268-274

张雪, 赖积保, 李家国, 王力, 朱利, 陈宜金. 2019. 基于高分一号影像的深圳市黑臭水体遥感识别. 科学技术与工程, 19(4): 268-274 [DOI: 10.3969/j.issn.1671-1815.2019.04.044http://dx.doi.org/10.3969/j.issn.1671-1815.2019.04.044]

Zhao Q C, Zhao S Y, Liu K, Wang Y C and Li H R. 2019. Remote sensing inversion of COD in Baiyang Lake based on actually-measured spectra and Landsat8 image. Modern Electronics Technique, 42(3): 56-60

赵起超, 赵姝雅, 刘剋, 王延仓, 李怀瑞. 2019. 基于实测光谱与Landsat8影像的白洋淀COD遥感反演. 现代电子技术, 42(3): 56-60 [DOI: 10.16652/j.issn.1004-373x.2019.03.014http://dx.doi.org/10.16652/j.issn.1004-373x.2019.03.014]

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