首页 >  2007, Vol. 11, Issue (5) : 756-762

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引用本文:

DOI:

10.11834/jrs.200705103

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修改日期:

2006-08-10

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夏季太湖叶绿素a浓度的高光谱数据监测模型
南京师范大学虚拟地理环境教育部重点实验室,南京 210046
摘要:

本文依据2004年7月的实测数据构建了太湖夏季叶绿素a浓度的实测光谱数据估计模型,并使用2004年8月的数据对模型进行了验证。调查样点覆盖了太湖内的典型水域,水样数据由无锡太湖环境监测站采集。样点的光谱数据用ASDFieldSpec野外光谱仪获取,每个样点测量10次,测量结果被转换为遥感反射率。对不同的波段组合进行比较分析后,从可解释性出发,最终选择了归一化指数表达式作为最佳波段组合,所建立的模型为:Chla(μg/L)=EXP(2.478 +16.378*N66),其中,N66为(R696 -R661) /(R696 +R661)。模型的R^2为0.9051,显著性p〈0.0001。与其他模型相比,本文的模型比较稳健,用于估计8月的叶绿素a浓度具有较小的绝对误差。本文的工作同时表明,在太湖的夏季相邻月份,可以使用实测光谱数据模型进行水体叶绿素a浓度的估计。

The Hyperspectral Data Monitoring Model of ChlorophyH-a of Summer in Taihu Lake, China
Abstract:

In this paper, a new hyperspectral datamodel that estimates chlorophyll-a concentration (Chla) in Taihu Lake of summer is proposed. Themodelwas developed based onmeasurement in situ in July 2004, andwas validated by hyperspectraldata inAugust2004. Water sampleswere collected byWuxiTaihu Lake EnvironmentMonitoring Station and covered the typicalwaterareas. Ateach site, hyperspectraldataweremeasured ten times by field spectroradiometer ASD FieldSpec, and were converted into remote sensing reflectance. Different band combinations were calculated and compared, and the normalized band index was selected because it ismore explicable. Themodel built by data in July 2004isChla(μg/L)=EXP(2·478+16·378*N66),whereN66is (R696-R661)/(R696+R661). Goodness-of-fit statistics of themodelR2is0·9051, andp<0·0001. Comparedwith othermodels, this one ismore stable, and is of less absolute errorwhen used to estimate Chla inAugust2004. Theworks in the paper also showed that hyperspectral data model can be used to estimateChla bymonth in the summerofTaihu Lake.

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