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国土资源遥感  2019, Vol. 31 Issue (2): 102-110    DOI: 10.6046/gtzyyg.2019.02.15
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于辐射传输模型的巢湖叶绿素a浓度反演
刘文雅1, 邓孺孺1,2,3(), 梁业恒1, 吴仪1, 刘永明1
1.中山大学地理科学与规划学院,广州 510275
2.广东省水环境遥感监测工程技术研究中心,广州 510275
3.广东省城市化与地理环境空间模拟重点实验室,广州 510275
Retrieval of chlorophyll-a concentration in Chaohu based on radiative transfer model
Wenya LIU1, Ruru DENG1,2,3(), Yeheng LIANG1, Yi WU1, Yongming LIU1
1.School of Geographic Science and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2.Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangzhou 510275, China
3.Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Guangzhou 510275, China
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摘要 

应用局限性小、普适性强的叶绿素a浓度反演算法是提高定量遥感技术实用性的关键。基于辐射传输机理,分析内陆湖中叶绿素a等因子的光学特性,建立像元反射率与因子浓度的物理模型。应用模型同时反演巢湖不同时相的叶绿素a浓度,决定系数R 2可达0.877 8,证明了模型时相局限性小、普适性强。进而选取预处理后的2016年巢湖不同时相Landsat8影像,反演并分析巢湖叶绿素a浓度的时空分布特征。研究表明,该模型不受时相限制、普适性强,可推动定量遥感技术在水质污染研究方面的应用。

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刘文雅
邓孺孺
梁业恒
吴仪
刘永明
关键词 叶绿素a浓度Landsat8辐射传输巢湖吸收散射    
Abstract

The algorithm of chlorophyll-a concentration inversion with higher universality is the key to improving the practicability of quantitative remote sensing technology. Based on the radioactive transfer mechanism, the optical characteristics of chlorophyll-a and other factors in inland lakes are analyzed, and a physical model of pixel reflectivity and factor concentration is established. The model was applied to the remote sensing data of different phases in Chaohu. The determination coefficient was 0.877 8 and the average relative error was only 11.61%. This proved that the precision of the model was higher and the universality was stronger. Then, the preprocessed Chaohu remote sensing image was applied to the model, and the spatial and temporal distribution characteristics of eutrophic pollution in Chaohu were obtained, which is consistent with the regulation of the seasonal multiplication of algae. The model used in this study has high accuracy and universality and thus can promote the application of quantitative remote sensing technology in water pollution research.

Key wordschlorophyll-a concentration    Landsat8    radiative transmission    Chaohu Lake    absorption    scattering
收稿日期: 2018-03-12      出版日期: 2019-05-23
:  TP79  
基金资助:中国博士后科学基金资助项目“基于高光谱影像的自然水体重金属铁和铜遥感反演研究”(2017M612792);广东省省级科技计划项目“珠江三角洲大气污染高分遥感监测及预警”(2017B020216001);广东省水利科技创新项目“广东省中小河流水量水质水生态联合监测技术体系研究”(2016-08);广东省自然科学基金项目“内陆光学浅水遥感模型及其在流溪河流域水质遥感监测的应用”(2017A030313238);中山大学青年教师培育项目“内陆有机污染光学浅水模型及水质、水深、底质一体化遥感反演研究”共同资助(17lgpy41)
通讯作者: 邓孺孺
作者简介: 刘文雅(1995-),女,硕士研究生,主要从事水质遥感研究。Email: liuwy28@mail2.sysu.edu.cn。
引用本文:   
刘文雅, 邓孺孺, 梁业恒, 吴仪, 刘永明. 基于辐射传输模型的巢湖叶绿素a浓度反演[J]. 国土资源遥感, 2019, 31(2): 102-110.
Wenya LIU, Ruru DENG, Yeheng LIANG, Yi WU, Yongming LIU. Retrieval of chlorophyll-a concentration in Chaohu based on radiative transfer model. Remote Sensing for Land & Resources, 2019, 31(2): 102-110.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.15      或      https://www.gtzyyg.com/CN/Y2019/V31/I2/102
Fig.1  电磁波与水体及大气相互作用
Fig.2  大气校正后影像
(OLI B4(R),B3(G),B2(B)假彩色合成)
Fig.3  水陆分离结果对比
参数 红光波段B4 近红外波段B5
波长/μm 0.654 6 0.864 6
αw 0.372 5 4.458 5
βw 0.000 904 382 0.000 271 848
αs 0.001 638 971 0.000 917 383
βs 0.18 0.11
αu 0.96 0.28
βu 0 0.18
Tab.1  水质因子光学参数值
Fig.4  叶绿素a浓度反演结果
Fig.5  模型反演值与实测值对比
Tab.2  模型反演值与实测值数值统计对比
统计指标 2006年7月30日 2009年3月27日
R2 0.877 765 53 0.848 814 29
RE/% 11.611 396 65 16.247 777 47
REmin/% 2.456 953 64 1.954 996 50
REmax/% 31.508 057 41 57.745 832 20
RMSE/(μg/L) 16.247 777 47 7.448 528 43
Tab.3  模型反演值与实测值的误差
Fig.6  2016年6—11月巢湖叶绿素a浓度反演结果
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