摘要
介绍了悬浮物质浓度遥感监测的一般原理 ,重点介绍光谱混合分析法。利用TM遥感影像 ,分别用光谱混合分析法和回归分析的经验模型计算了闽江口悬浮物质浓度的分布 ,对结果进行了分析和对比。结果表明 ,光谱混合分析法可以充分利用多波段的数据 ,不需要大量的实验数据 ,有较好的实用性 ,能用于缺乏实测数据的区域。
Backscattering in coastal waters is stronger than that in open sea because of high suspended sediment, and its information is reflected well in remote sensing images. The key to estimate suspended sediment concentration (SSC)from remote sensing images is to establish the relationship between SSC and water reflectance R.Many theoretic or empirical models have been developed for calculating SSC. But most of them need real-time survey data of SSC, which limit the use of these models. In this paper, spectral mixing analysis method, which needs little real-time data, was introduced in detail. The pixels with minimun and maximal SSC were used as endmembers, and other pixels were regarded as the mixtures of the two endmembers. A linear spectral mixing analysis model was as followed:R b=(1-f high)R Lb+f highR Hb+E b b=1,2,...,Bwhere R bwas the bth reflectance, R Lband R Hbwere the bth reflectance for minimum or maximal SSC, f high was the fraction of maximal SSC, E bwas residual error and Bwas the number of bands. In order to get the minimum sum of E b(b=1,2,...,B),the following model has been developed:f high=∑Bb=1(R b-R Lb)(R Hb-R Lb)∑Bb=1(R Hb-R Lb)2 Based on TM remote sensing images, the quantitative relationship between SSC and spectral reflectance was studied. The distribution of SSCs in coastal waters of Minjiang River was calculated by using spectral mixing analysis and empirical formula respectively. The results showed that:(1) the high SSC regions lay in the mouth of Minjiang River, and the further from the coastal, the lower was the SSC; (2) the distribution of SSC can be estimated quickly and objectly from remote sensing images; (3) spectral mixing analysis is a good method, especially in areas lack of much measured data, for it can make full use of multi-band of images and needs less samples.
出处
《遥感学报》
EI
CSCD
北大核心
2003年第1期54-57,T004,共5页
NATIONAL REMOTE SENSING BULLETIN
基金
国家 8 6 3项目资助 ( 818- 0 6 - 0 3)
