基于Sentinel-2影像的广州水体CODMn遥感反演

doi:10.13474/j.cnki.11-2246.2026.0807

测绘通报 ›› 2026, Vol. 0 ›› Issue (8): 44-50.doi: 10.13474/j.cnki.11-2246.2026.0807

基于Sentinel-2影像的广州水体COD_Mn遥感反演

赵彤彤^1,2,3,4, 邓孺孺^1,2,3,4

1. 中山大学地理科学与规划学院, 广东广州 510006;
2. 南方海洋科学与工程广东省试验室(珠海), 广东珠海 519082;
3. 广东省水环境遥感监测工程技术研究中心, 广东广州 510275;
4. 广东省城市化与地理环境空间模拟重点实验室, 广东广州 510275

收稿日期:2025-12-02 发布日期:2026-09-12
通讯作者: 邓孺孺。E-mail:eesdrr@mail.sysu.edu.cn
作者简介:赵彤彤(2002—)，女，硕士生，主要研究方向为水质遥感。E-mail:zhaott35@mail2.sysu.edu.cn
基金资助:
南方海洋科学与工程广东省实验室(珠海)资助项目(99147-42080011)；国家自然科学基金(41901352；41071230)；广东省省级科技计划(2017B020216001)；广东省基础与应用基础研究(2020A1515010780；2022B1515130001)；广州市科技计划(202102020454)

Remote sensing inversion of COD_Mn in Guangzhou water bodies based on Sentinel-2 imagery

Zhao Tongtong^1,2,3,4, Deng Ruru^1,2,3,4

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China;
2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China;
3. Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangzhou 510275, China;
4. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China

Received:2025-12-02 Published:2026-09-12

摘要/Abstract

摘要： [目的] 高锰酸钾指数(COD_Mn)是衡量水体有机污染的重要指标，其精确反演对水质监测至关重要。[方法] 本文基于水体辐射传输原理，构建考虑COD_Mn、叶绿素a和悬浮固体等光学活性物质相互作用的多组分物理反演模型，利用Sentinel-2影像逐像元反演广州水体COD_Mn浓度，并结合地理加权回归分析其影响因素。[结果] 反演结果与实测COD_Mn具有较好一致性，决定系数(R²)为0.82，均方根误差(RMSE)为0.71，平均绝对百分比误差(MAPE)为27.30%，精度满足要求；广州水体COD_Mn集中在0～ 2 mg/L，对应地表水Ⅰ类水质，整体水质较好；工业废水对南沙、番禺和增城水质影响显著，农业在南沙、增城作用突出，旅游业在番禺影响较大。[结论] 基于辐射传输的多组分COD_Mn物理反演方法可高精度刻画水体COD_Mn空间分布，为水质监测提供技术支撑。

关键词: 有机污染, 高锰酸钾指数, 物理模型, 地理加权回归, Se 8ntinel-2遥感影像

Abstract: [Purposes] COD_Mn is an important indicator for assessing the level of organic pollution in water bodies,and its accurate retrieval is crucial for quality monitoring. [Methods] Based on water radiative transfer theory,a multi-component physical inversion model was developed by considering the interactions among optically active substances,including COD_Mn,chlorophyll-a,and suspended solids.Sentinel-2 imagery was used to retrieve COD_Mn concentrations on a pixel-by-pixel basis for surface waters in Guangzhou,and geographic weighted regression (GWR)was applied to analyze the influencing factors. [Findings] The retrieved COD_Mn values showed good agreement with in situ measurements,with a coefficient of determination (R²) of 0.82,a root mean square error (RMSE)of 0.71,and a mean absolute percentage error (MAPE)of 27.30%, indicating satisfactory accuracy.COD_Mn concentrations in Guangzhou were mainly concentrated in the range of 0～2 mg/L,corresponding to Class I surface water quality,suggesting overall good water conditions.Industrial wastewater significantly affected water quality in Nansha,Panyu,and Zengcheng,agricultural activities played a prominent role in Nansha and Zengcheng,and tourism had a stronger impact in Panyu. [Conclusions] The radiative transfer-based multi-component physical inversion approach can accurately characterize the spatial distribution of COD_Mn,providing technical support for refined water quality.

Key words: organic pollution, COD_Mn, physical model, geographically weighted regression, Sentinel-2 imagery

中图分类号:

P237

赵彤彤, 邓孺孺. 基于Sentinel-2影像的广州水体COD_Mn遥感反演[J]. 测绘通报, 2026, 0(8): 44-50.

Zhao Tongtong, Deng Ruru. Remote sensing inversion of COD_Mn in Guangzhou water bodies based on Sentinel-2 imagery[J]. Bulletin of Surveying and Mapping, 2026, 0(8): 44-50.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://tb.chinasmp.com/CN/10.13474/j.cnki.11-2246.2026.0807

http://tb.chinasmp.com/CN/Y2026/V0/I8/44

参考文献

[1] Wang Youxiao,Liu Gaohuan,Yu Ge,et al.Evaluation of the spatial heterogeneity in marine organic pollution and land-based influencing factors:a case study of the marine area of Laizhou Bay,China[J].Regional Studies in Marine Science,2021,45:101867.
[2] Huang Na,Gao Kai,Yang Weiming,et al.Assessing sediment organic pollution via machine learning models and resource performance[J].Bioresource Technology,2022,361:127710.
[3] Liu Dong,Zhang Chenxue,Yan Nuoxiao,et al.Eutrophication exacerbated organic pollution in lakes across China during the 1980s-2010s[J].Water Research,2025,268:122782.
[4] Li Ji,Luo Guobing,He Lingjun,et al.Analytical approaches for determining chemical oxygen demand in water bodies:a review[J].Critical Reviews in Analytical Chemistry,2018,48(1):47-65.
[5] Guo Hongwei,Huang J J,Chen Bowen,et al.A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery[J].International Journal of Remote Sensing,2021,42(5):1841-1866.
[6] Brivio P A,Giardino C,Zilioli E.Validation of satellite data for quality assurance in lake monitoring applications[J].Science of the Total Environment,2001,268(1/2/3):3-18.
[7] Sun Yuan,Wang Denghui,Li Lei,et al.Application of remote sensing technology in water quality monitoring:from traditional approaches to artificial intelligence[J].Water Research,2024,267:122546.
[8] Chen Lei,Liu Leizhen,Liu Shasha,et al.The application of remote sensing technology in inland water quality monitoring and water environment science:recent progress and perspectives[J].Remote Sensing,2025,17(4):667.
[9] Yang Bo,Liu Yupeng,Ou Fuping,et al.Temporal and spatial analysis of COD concentration in east Dongting Lake by using of remotely sensed data[J].Procedia Environmental Sciences,2011,10:2703-2708.
[10] Wang Yunpeng,Xia Hao,Fu Jiamo,et al.Water quality change in reservoirs of Shenzhen,China:detection using LANDSAT/TM data[J].Science of the Total Environment,2004,328(1/2/3):195-206.
[11] Yang Haibo,Kong Jialin,Hu Huihui,et al.A review of remote sensing for water quality retrieval:progress and challenges[J].Remote Sensing,2022,14(8):1770.
[12] Chen Jinyue,Chen Shuisen,Fu Rao,et al.Remote sensing big data for water environment monitoring:current status,challenges,and future prospects[J].Earth's Future,2022,10(2):e2021EF002289.
[13] Zhang Yalong,Yu Xiaolong,Lee Z,et al.Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean[J].Optics Express,2024,32(9):15741.
[14] Yang Wei,Matsushita B,Chen Jin,et al.Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables[J].Remote Sensing of Environment,2011,115(5):1247-1259.
[15] Sagan V,Peterson K T,Maimaitijiang M,et al.Monitoring inland water quality using remote sensing:potential and limitations of spectral indices,bio-optical simulations,machine learning,and cloud computing[J].Earth-Science Reviews,2020,205:103187.
[16] Bramante J,Sin T.Optimization of a semi-analytical algorithm for multi-temporal water quality monitoring in inland waters with wide natural variability[J].Remote Sensing,2015,7(12):16623-16646.
[17] Mora C,Aburto-Oropeza O,Ayala Bocos A,et al.Global human footprint on the linkage between biodiversity and ecosystem functioning in reef fishes[J].PLoS Biology,2011,9(4):e1000606.
[18] Arkema K K,Verutes G,Bernhardt J R,et al.Assessing habitat risk from human activities to inform coastal and marine spatial planning:a demonstration in Belize[J].Environmental Research Letters,2014,9(11):114016.
[19] Ma Bingran,Tian Guangjin,Kong Lingqiang.Spatial-temporal characteristics of China's industrial wastewater discharge at different scales[J].Environmental Science and Pollution Research,2020,27(8):8103-8118.
[20] Ding Jiao,Jiang Yuan,Liu Qi,et al.Influences of the land use pattern on water quality in low-order streams of the Dongjiang River basin,China:a multi-scale analysis[J].Science of the Total Environment,2016,551:205-216.
[21] Brunsdon C,Fotheringham A S,Charlton M E.Geographically weighted regression:a method for exploring spatial nonstationarity[J].Geographical Analysis,1996,28(4):281-298.
[22] Lu Binbin,Charlton M,Harris P,et al.Geographically weighted regression with a non-Euclidean distance metric:a case study using hedonic house price data[J].International Journal of Geographical Information Science,2014,28(4):660-681.
[23] Tu Jun,Xia Zongguo.Examining spatially varying relationships between land use and water quality using geographically weighted regression I:model design and evaluation[J].Science of the Total Environment,2008,407(1):358-378.
[24] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003．
[25] Menon H B,Lotliker A A,Nayak S R.Analysis of estuarine colour components during non-monsoon period through ocean colour monitor[J].Estuarine,Coastal and Shelf Science,2006,66(3/4):523-531.
[26] 邓孺孺,何颖清,秦雁,等.近红外波段(900— 2500 nm)水吸收系数测量[J].遥感学报,2012,16(1):192-206．
[27] 何颖清,邓孺孺,陈启东,等.基于ASD光谱仪的悬浮泥沙光学衰减系数研究[J].中山大学学报(自然科学版),2011,50(3):134-140．
[28] O'Sullivan D.Geographically weighted regression:the analysis of spatially varying relationships (review)[J].Geographical Analysis,2003,35(3):272-275.
[29] Jia Xuexiu,Klemeš J J,Alwi S R W,et al.Regional water resources assessment using water scarcity pinch analysis[J].Resources,Conservation and Recycling,2020,157:104749.

基于Sentinel-2影像的广州水体COD_Mn遥感反演

Remote sensing inversion of COD_Mn in Guangzhou water bodies based on Sentinel-2 imagery

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	林羊, 于冰, 田馨, 张冠军, 刘成, 甘俊, 李广宇. 京雄城际铁路沿线InSAR地表沉降监测与影响因素分析[J]. 测绘通报, 2026, 0(2): 46-53.
[2]	赵慧, 刘茜, 张敏, 李佳玉. 基于MGWR模型的新疆景观格局与防风固沙服务关系分析[J]. 测绘通报, 2025, 0(9): 1-7.
[3]	谭超, 林镇宏, 贺秋华, 栾海军, 余姝辰, 郑雅玲, 邹娟. 顾及光谱位置及秸秆还田活动影响的土壤有机质高光谱估算[J]. 测绘通报, 2025, 0(2): 89-95.
[4]	雷楷烨, 张显云, 刘晶晖, 吴雪. 一种顾及空间异质性和噪声的遥感缺失数据重建方法[J]. 测绘通报, 2024, 0(12): 40-47.
[5]	王增铮, 张福浩, 赵阳阳, 仇阿根. 区域地理加权回归分析方法[J]. 测绘通报, 2023, 0(12): 81-87.
[6]	张瑞, 李朝奎, 姚思妤, 李维贵. 融合地理探测器和地理加权回归的太原市建设用地变化因素研究[J]. 测绘通报, 2022, 0(5): 106-109,119.
[7]	张玉娟, 曲建光, 侯建国. 哈尔滨市景观生态风险时空变化驱动力及分布预测[J]. 测绘通报, 2022, 0(2): 83-89,94.
[8]	张兰兰, 吴启明, 申朝永, 刘芳, 陈砚国. 地理空间要素与贫困分布的关系研究——以赫章县为例[J]. 测绘通报, 2021, 0(12): 120-123,129.
[9]	高照忠, 黄雅君, 魏海霞, 张金兰. 近30年云贵高原水蚀荒漠化遥感监测及影响因子时空分异规律[J]. 测绘通报, 2021, 0(10): 117-122.
[10]	刘振涛, 杨毅, 王东超, 谢晓尧. 一种基于CUDA的大数据量地理加权回归并行加速算法[J]. 测绘通报, 2020, 0(12): 1-5.
[11]	李月, 何宏昌, 王晓飞, 张国民. 农作物冠层光谱分析及反演技术综述[J]. 测绘通报, 2019, 0(9): 13-17.
[12]	于志英, 张福浩, 仇阿根, 赵阳阳. 基于IGGⅢ的地理加权回归模型研究[J]. 测绘通报, 2019, 0(7): 23-27.
[13]	邓悦, 刘洋, 刘纪平, 徐胜华, 罗安. GBGWR模型在降水量空间分布模拟中的应用[J]. 测绘通报, 2018, 0(3): 32-37,42.
[14]	肖屹, 何宗宜, 苗静, 潘峰, 杨好. 利用搜索引擎数据模拟疾病空间分布[J]. 测绘通报, 2018, 0(2): 94-98.
[15]	董珍珍, 王亮, 仇阿根. 协同遥感信息与统计信息的人口空间格局分析[J]. 测绘通报, 2017, 0(5): 88-94.

基于Sentinel-2影像的广州水体CODMn遥感反演

Remote sensing inversion of COD Mn in Guangzhou water bodies based on Sentinel-2 imagery

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

基于Sentinel-2影像的广州水体COD_Mn遥感反演

Remote sensing inversion of COD_Mn in Guangzhou water bodies based on Sentinel-2 imagery