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基于GOCI影像和水体光学分类的内陆湖泊叶绿素a浓度遥感估算
摘要点击 3013  全文点击 1920  投稿时间:2014-11-12  修订日期:2014-12-25
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中文关键词  水体分类  叶绿素a  GOCI影像  反演模型  实时监测
英文关键词  water body classification  chlorophyll-a  GOCI image  retrieval model  real-time monitoring
作者单位E-mail
冯驰 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023 fengchi9011@163.com 
金琦 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023  
王艳楠 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023  
赵丽娜 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023  
吕恒 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
江苏省地理信息资源开发与利用协同创新中心, 南京 210023 
HengLu@njnu.edu.cn 
李云梅 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
江苏省地理信息资源开发与利用协同创新中心, 南京 210023 
 
中文摘要
      叶绿素a作为水质参数之一,常用来作为衡量水体富营养化程度的指示标准. 利用从太湖及洞庭湖获取的326个实测数据,基于实测遥感反射率对水体光谱进行光学分类,结果表明所采集的样点可分为3种水体类型. 结合GOCI的波段设置,建立了不同类型水体的叶绿素a浓度反演模型. 水体类型一可以利用490 nm(3波段)和555 nm(4波段)来反演,水体类型二可利用660 nm(5波段)和443 nm(2波段),水体类型三利用745 nm(7波段)和680 nm(6波段). 精度分析表明,分类后的平均相对误差明显下降,类型一为38.91%、类型二为24.19%、类型三为22.90%; 类型一均方根误差为4.87 μg ·L-1、类型二为8.13 μg ·L-1、类型三为11.66 μg ·L-1; 分类前后的总体平均相对误差由49.78%降低到29.59%,总体均方根误差由14.10 μg ·L-1降低到9.29 μg ·L-1,分类后反演精度得到了显著提高. 利用2013年5月13日8景GOCI影像反演了太湖的叶绿素a浓度,结果表明,2013年5月13日太湖叶绿素a浓度日变化显著,高值区主要集中在竺山湾、梅梁湾、贡湖湾,低值区主要集中在湖心区以及南部区域,10: 00以后太湖西南部沿岸的叶绿素a浓度显著降低. 这种先分类后反演的方法对于二类水体的模型反演精度的提高具有重要作用.
英文摘要
      Chlorophyll-a as one of the important water quality parameters is often used as a measure of the level of water eutrophication. The 326 measured data collected from Lake Taihu and Lake Dongting were classified based on their measured values of remote sensing reflectance spectra using an automatic clustering algorithm-two-step method, and three water types were finally classified. According to the location and width of GOCI satellite bands, the specific algorithm to estimate chlorophyll-a concentration for different water body types was developed. The bands at 490 nm and 555 nm were used for water body type Ⅰ, while bands at 660 nm and 443 nm were selected for water body type Ⅱ and bands at 745 nm and 680 nm were applied for water body type Ⅲ. The accuracy assessment showed that the mean relative error decreased from 49.78% to 38.91%, 24.19% and 22.90% for water body type Ⅰ, Ⅱ and Ⅲ, respectively, while the root mean square error decreased from 14.10 μg ·L-1 to 4.87 μg ·L-1, 8.13 μg ·L-1 and 11.66 μg ·L-1 for water body type Ⅰ, Ⅱ and Ⅲ, respectively. The overall mean relative error decreased from 49.78% to 29.59% after classification, while the overall root mean square error was reduced from 14.10 μg ·L-1 to 9.29 μg ·L-1 after classification. The retrieval accuracy was significantly improved after classification. The chlorophyll-a concentration in Lake Taihu was retrieved using the GOCI image on May 13, 2013. The results showed that there was a significantly diurnal variation in the concentration of chllorophyll-a on May 13, 2013, and the regions with higher chlorophyll-a concentration were mainly distributed in the Zhushan Bay, Meiliang Bay and Gonghu Bay, while the regions with lower values were mainly located in the centre of the lake and the southern region. The chlorophyll-a concentration reduced significantly after 10:00 in the south- western region of Lake Taihu. This method of retrieving after classification played an important role in improving the model retrieval accuracy of case 2 water.

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