Please wait a minute...
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
全文: PDF(7414 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
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。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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
ZTFLH:  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     E-mail: esdrr@mail.sysu.edu.cn
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:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.15      或      http://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
[1] 余延年, 夏进 . 巢湖生态危机及其对策[J].水资源保护, 1989(1):48-53.
Yu Y N, Xia J . Chaohu ecological crisis and countermeasures[J].Water Resources Conservation, 1989(1):48-53.
[2] 李素菊, 吴倩, 王学军 , 等. 巢湖浮游植物叶绿素含量与反射光谱特征的关系[J]. 湖泊科学, 2002,14(3):228-234.
doi: 10.18307/2002.0306
Li S J, Wu Q, Wang X J , et al. Correlations between reflectance spectra and contents of chlorophyll-a in Chaohu Lake[J]. Journal of Lake Science, 2002,14(3):228-234.
doi: 10.18307/2002.0306
[3] 荀尚培, 翟武全, 范伟 , 等. MODIS巢湖水体叶绿素a浓度反演模型[J]. 应用气象学报, 2009,20(1):95-101.
doi: 10.11898/1001-7313.20090112
Xun S P, Zhai W Q, Fan W , et al. MODIS in monitoring the chlorophyll-a concentrations of Chaohu Lake[J]. Journal of Applied Meteorological Science, 2009,20(1):95-101.
doi: 10.11898/1001-7313.20090112
[4] 杨煜, 李云梅, 王桥 , 等. 基于环境一号卫星高光谱遥感数据的巢湖水体叶绿素a浓度反演[J]. 湖泊科学, 2010,22(4):495-503.
doi: 10.18307/2010.0404
Yang Y, Li Y M, Wang Q , et al. Retrieval of chlorophyll-a concentration by three-band model in Lake Chaohu[J]. Journal of Lake Science, 2010,22(4):495-503.
doi: 10.18307/2010.0404
[5] 谢杰, 王心源, 张洁 , 等. 基于TM/ETM+影响分析巢湖叶绿素a浓度变化趋势[J]. 中国环境科学, 2010,30(5):677-682.
Xie J, Wang X Y, Zhang J , et al. Analysing developing trend of chlorophyll-a concentration in Chaohu Lake based on TM/ETM+ image[J]. China Environmental Science, 2010,30(5):677-682.
[6] 陈静, 吴传庆, 申维 , 等. 基于环境一号卫星CCD数据的巢湖叶绿素a的动态监测[J]. 中国环境监测, 2012,28(1):116-119.
doi: 10.3969/j.issn.1002-6002.2012.01.032
Chen J, Wu C Q, Shen W , et al. Chlorophyll-a dynamic monitoring in Chaohu Lake based on environmental satellite 1 CCD data[J]. Environmental Monitoring in China, 2012,28(1):116-119.
doi: 10.3969/j.issn.1002-6002.2012.01.032
[7] 殷守敬, 吴传庆, 王晨 , 等. 综合遥感与地面观测的巢湖水体富营养化评价[J]. 中国环境监测, 2018,34(1):157-164.
doi: 10.19316/j.issn.1002-6002.2018.01.22
Yin S J, Wu C Q, Wang C , et al. Eutrophication assessment of Chao-hu Lake using remote sensing and in-situ data[J]. Environmental Monitoring in China, 2018,34(1):157-164.
doi: 10.19316/j.issn.1002-6002.2018.01.22
[8] 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[J]. Optics Express, 2010,18(23):24109-24125.
doi: 10.1364/OE.18.024109 pmid: 21164758
[9] Matthews M W . A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters[J]. International Journal of Remote Sensing, 2011,32(21):6855-6899.
doi: 10.1080/01431161.2010.512947
[10] Mishra S, Mishra D R, Lee Z , et al. Quantifying cyanobacterial phycocyanin concentration in turbid productive waters:A quasi-analytical approach[J]. Remote Sensingof Environment, 2013,133:141-151.
doi: 10.1016/j.rse.2013.02.004
[11] Stumpf R P, Davis T W, Wynne T T , et al. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria[J]. Harmful Algae, 2016,54:160-173.
doi: 10.1016/j.hal.2016.01.005 pmid: 28073474
[12] 邓孺孺, 何执兼, 陈晓翔 . 基于二次散射的水污染遥感模型及其在珠江口水域的应用[J]. 海洋学报, 2003,25(6):69-78.
Deng R R, He Z J, Chen X X . Model for water pollution remote sensing based on double scattering and its application on the Zhujiang River Estuary[J]. Acta Oceanologica Sinica, 2003,25(6):69-78.
[13] 邓孺孺, 秦雁 . 珠江三角洲水库水质遥感监测研究——以梅州水库和流溪河水库为例[ C]//全国国土资源与环境遥感应用技术研讨会论文集.深圳:中国国土经济学会, 2009: 179-188.
Deng R R, Qin Y . Monitoring water quality of reservoirs in Pearl River Delta Region by remote sensing:A case study on Meizhou Reservoir and Liuxihe Reservoir[C]//Proceedings of the National Seminar on Remote Sensing Application Technology for Land and Resources and Environment.Shenzhen:Chinese Society of Territorial Economics, 2009: 179-188.
[14] 邓孺孺, 何执兼, 陈晓翔 , 等. 珠江口水域水污染遥感定量分析[J]. 中山大学学报(自然科学版), 2002,41(3):99-103.
doi: 10.3321/j.issn:0529-6579.2002.03.026
Deng R R, He Z J, Chen X X , et al. Qualitative analysis of water pollution in the Pearl River Estuary by remote sensing method[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2002,41(3):99-103.
doi: 10.3321/j.issn:0529-6579.2002.03.026
[15] 吴仪, 邓孺孺, 秦雁 , 等. 新丰江水库叶绿素浓度时空分布特征的遥感反演研究[J]. 遥感技术与应用, 2017,32(5):825-834.
doi: 10.11873/j.issn.1004-0323.2017.5.0825
Wu Y, Deng R R, Qin Y , et al. The study of spatial-temporal characteristic for chlorophyll concentration derived from remote sensing image in Xinfengjiang Reservoir[J]. Remote Sensing Technology and Application, 2017,32(5):825-834.
doi: 10.11873/j.issn.1004-0323.2017.5.0825
[16] 徐涵秋, 唐菲 . 新一代Landsat系列卫星:Landsat8遥感影像新增特征及其生态环境意义[J]. 生态学报, 2013,33(11):3249-3257.
doi: 10.5846/stxb201305030912
Xu H Q, Tang F . Analysis of new characteristics of the first Landsat8 image and their eco-environmental significance[J]. Acta Ecologica Sinica, 2013,33(11):3249-3257.
doi: 10.5846/stxb201305030912
[17] 杨娅楠, 王金亮, 陈光杰 , 等. 抚仙湖流域土地利用格局与水质变化关系[J]. 国土资源遥感, 2016,28(1):159-165.doi: 10.6046/gtzyyg.2016.01.23.
doi: 10.6046/gtzyyg.2016.01.23
Yang Y N, Wang J L, Chen G J , et al. Relationship between land use pattern and water quality change in Fuxian Lake basin[J]. Remote Sensing for Land and Resources, 2016,28(1):159-165.doi: 10.6046/gtzyyg.2016.01.23.
doi: 10.6046/gtzyyg.2016.01.23
[18] Kaufman, Y J , Wald A E,Remer L A ,et al.The MODIS 2.1-μm channel-correlation with visable reflectance for use in remote sensing of aerosol[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(5):1286-1298.
doi: 10.1109/36.628795
[19] Richter R, Schläpfer D, Müller A . An automatic atmospheric correction algorithm for visible/NIR imagery[J]. International Journal of Remote Sensig, 2006,27(10):2077-2085.
doi: 10.1080/01431160500486690
[20] Zhang M, Carder K , Mulle-Karger F E ,et al.Noise reduction and atmospheric correction for coastal applications of Landsat thematic mapper imagery[J]. Remote Sensing of Environment, 1999,70(2):167-180.
doi: 10.1016/S0034-4257(99)00031-0
[21] 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.
doi: 10.11834/jrs.20121188
Deng R R, He Y Q, Qin Y , et al. Measuring pure water absorption coefficient in the near-infrared spectrum(900—2500 nm)[J]. Journal of Remote Sensing, 2012,16(1):192-206.
doi: 10.11834/jrs.20121188
[22] 邓孺孺 .一种自动提取水体污染信息的方法:中国,200810219844[P]. 2009-7-29.
Deng R R .A method for automatically extracting water pollution information:China, 200810219844[P]. 2009-7-29.
[23] 徐兵 . 珊瑚礁遥感监测方法研究[D]. 南京:南京师范大学, 2013.
Xu B . Reasearch on Coral Reef Remote Sensing Monitoring Methods[D]. Nanjing:Nanjing Normal University, 2013.
[24] 孙笑笑 . 联合浮标与卫星数据的赤潮预警与决策服务[D]. 杭州:浙江大学, 2017.
Sun X X . Red Tide Prediction and Decision Services by Integrating Buoy and Remote Sensing Data[D]. Hangzhou:Zhejiang University, 2017.
[25] 陈瑜丽 . 基于辐射传输模型的遥感反射率计算及叶绿素反演算法分析[D]. 上海:华东师范大学, 2015.
Chen Y L . Calculation of Remote Sensing Reflectance Based on Radiative Transfer Model and Analysis of Chlorophyll Retrieval Algorithm[D]. Shanghai: East China Normal University, 2015.
[26] Ton T, Jain A K, Enslin W R , et al. Automatic road identification and labeling in Landsat4 TM images[J]. Photogrammetric, 1989,43(5):257-276.
doi: 10.1016/0031-8663(89)90002-1
[27] 安如, 刘影影, 曲春梅 , 等. NDCI法Ⅱ类水体叶绿素a浓度高光谱遥感数据估算[J]. 湖泊科学, 2013,25(3):437-444.
doi: 10.18307/2013.0319
An R, Liu Y Y, Qu C M , et al. Estimation of chlorophyll-a concentration of caseⅡ waters from hyperspectral remote sensing data in NDCI method[J]. Journal of Lake Sciences, 2013,25(3):437-444.
doi: 10.18307/2013.0319
[28] 谢杰 . 基于遥感的巢湖水体叶绿素a浓度变化趋势研究[D]. 芜湖:安徽师范大学, 2011.
Xie J . Research Developing Trend of Chlorophyll-a Concentration in Chaohu Lake Based on Remote Sensing[D]. Wuhu:Anhui Normal University, 2011.
[29] 张晓斌 . 基于高光谱遥感的巢湖水体叶绿素-a浓度反演模型研究[D]. 合肥:安徽建筑大学, 2012.
Zhang X B . Regression of Chlorophyll Content Based on Hyperspectral Remote Sensing Data in Chaohu Lake[D]. Hefei:Anhui Jianzhu University, 2012.
[30] 宋碧霄 . 遥感图像条带去除方法研究[D]. 西安:西安电子科技大学, 2013.
Song B X . Remote Sensing Image Strip Removal Method[D]. Xi’an:Xidian University, 2013.
[31] 王晓琦, 邢小罡, 王金平 , 等. 基于遥感数据分析南海叶绿素与颗粒物的季节变化与相互关系[J]. 海洋学报, 2015,37(10):26-38.
doi: 10.3969/j.issn.0253-4193.2015.10.003
Wang X Q, Xing X G, Wang J P , et al. A satellite-based analysis on the seasonal variations and interrelationships between chlorophyll and particle in the South China Sea[J]. Acta Oceanologica Sinica, 2015,37(10):26-38.
doi: 10.3969/j.issn.0253-4193.2015.10.003
[32] 张玉娟 . 大亚湾浮游植物种群动态及锥状斯氏藻的实验生态研究[D]. 广州:暨南大学, 2006.
Zhang Y J . Seasonal Changes in the Phytoplankton Community and Experimental Ecology of Scrippsiella Trochoidea in Daya Bay,South China Sea[D]. Guangzhou:Jinan University, 2006.
[1] 熊俊楠,李伟,程维明,范春捆,李进,赵云亮. 高原地区LST空间分异特征及影响因素研究——以桑珠孜区为例[J]. 国土资源遥感, 2019, 31(2): 164-171.
[2] 李静,孙强强,张平,孙丹峰,温礼,李宪文. 基于多时相热红外遥感的钢铁企业生产状态辅助监测[J]. 国土资源遥感, 2019, 31(1): 220-228.
[3] 孙桂芬,覃先林,刘树超,李晓彤,陈小中,钟祥清. 典型植被指数识别火烧迹地潜力分析[J]. 国土资源遥感, 2019, 31(1): 204-211.
[4] 王月如,韩鹏鹏,关舒婧,韩宇,易琳,周廷刚,陈劲松. 基于Landsat8 OLI数据的富贵竹种植区域信息提取[J]. 国土资源遥感, 2019, 31(1): 133-140.
[5] 陈瀚阅, 朱利, 李家国, 范协裕. 基于Landsat8数据的2种海表温度反演单窗算法对比——以红沿河核电基地海域为例[J]. 国土资源遥感, 2018, 30(1): 45-53.
[6] 庞海洋, 孔祥生, 汪丽丽, 钱永刚. ENDSI增强型雪指数提取积雪研究[J]. 国土资源遥感, 2018, 30(1): 63-71.
[7] 张雅莉, 阿尔达克·克里木, 张东, 依力亚斯江·努尔麦麦提, 张飞. 基于Landsat8 OLI影像光谱的土壤盐分估算模型研究[J]. 国土资源遥感, 2018, 30(1): 87-94.
[8] 张成才, 罗蔚然, 窦小楠, 王金鑫. 应用Landsat8数据改进FCD模型方法[J]. 国土资源遥感, 2017, 29(4): 33-38.
[9] 杨雨薇, 戴晓爱, 牛育天, 刘汉湖, 杨晓霞, 兰燕. 基于CASI数据的黑河绿洲区叶面积指数反演[J]. 国土资源遥感, 2017, 29(4): 179-184.
[10] 章钊颖, 鲁奕岑, 吴国周, 王永利. 基于多时相Sentinel-1A SAR数据草原地区降水量反演[J]. 国土资源遥感, 2017, 29(4): 156-160.
[11] 刘瑶, 张文娟, 张兵, 甘甫平. 中红外大气强吸收通道的地表辐亮度图像模拟[J]. 国土资源遥感, 2017, 29(3): 98-103.
[12] 王世新, 田野, 周艺, 刘文亮, 林晨曦. 基于后向散射模型的多极化SAR影像建筑物高度提取[J]. 国土资源遥感, 2017, 29(2): 37-45.
[13] 梁守真, 隋学艳, 姚慧敏, 王猛, 侯学会, 陈劲松, 马万栋. 落叶阔叶林冠层非光合组分对冠层FPAR的影响分析——一种分层模拟的方法[J]. 国土资源遥感, 2017, 29(2): 29-36.
[14] 许斌. 基于非高斯分布的全极化SAR数据无监督分类[J]. 国土资源遥感, 2017, 29(2): 90-96.
[15] 何连, 秦其明, 任华忠. 一种自适应的混合Freeman/Eigenvalue极化分解模型[J]. 国土资源遥感, 2017, 29(2): 8-14.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《国土资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 Email:gtzyyg@agrs.cn; gtzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发