CAJ | 학술논문

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4.87μg?L -1、类型二为8.13μg?L -1、类型三为11.66μg?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14.10μg?L -1降低到9.29μg?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.
협록소 a 작위수질삼수지일,상용래작위형량수체부영양화정도적지시표준.이용종태호급동정호획취적326개실측수거,기우실측요감반사솔대수체광보진행광학분류,결과표명소채집적양점가분위3충수체류형.결합 GOCI 적파단설치,건립료불동류형수체적협록소 a 농도반연모형.수체류형일가이이용490 nm(3파단)화555 nm(4파단)래반연,수체류형이가이용660 nm(5파단)화443 nm(2파단),수체류형삼이용745 nm(7파단)화680 nm(6파단).정도분석표명,분류후적평균상대오차명현하강,류형일위38.91％、류형이위24.19％、류형삼위22.90％；류형일균방근오차위4.87μg?L -1、류형이위8.13μg?L -1、류형삼위11.66μg?L -1；분류전후적총체평균상대오차유49.78％강저도29.59％,총체균방근오차유14.10μg?L -1강저도9.29μg?L -1,분류후반연정도득도료현저제고.이용2013년5월13일8경 GOCI 영상반연료태호적협록소 a 농도,결과표명,2013년5월13일태호협록소 a 농도일변화현저,고치구주요집중재축산만、매량만、공호만,저치구주요집중재호심구이급남부구역,10：00이후태호서남부연안적협록소 a 농도현저강저.저충선분류후반연적방법대우이류수체적모형반연정도적제고구유중요작용.
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.