水面实测遥感光谱数据的叶绿素a反演模型构建—以太湖为例

叶绿素a遥感反演的关键是建立遥感数据和叶绿素含量的定量关系。本文根据2007年11月在太湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据，对太湖水体反射光谱特征与叶绿素浓度之间的关系进行分析和建模。研究结果表明：687nm附近反射峰位置对叶绿素浓度有很好的指示作用，在太湖水体叶绿素浓度的估测模型中，反射峰位置的指数模型要优于线性模型；叶绿素a浓度与684nm和633nm附近的一阶微分有很好的相关性，利用R’684和R’633所建叶绿素a浓度反演模型的估算精度R2分别达到0.89和0.91。

Modeling of Chlorophyll-a Retrieval Based on Remote Sensing Data——A Case of Taihu Lake

The key of remote sensing retrieval in chlorophyll-a is to construct a quantitative relation between remote sensing data and chlorophyll-a concentrations. Based on the observed data, the modeling was conducted for the correlation between the reflection spectrum from water surface and chlorophyll-a concentrations of the water in Taihu Lake in Nov. 2007. The results showed that the peak position near 687 nm was a good indicator for chlorophyll-a eoncentration. For estimation of ehlorophyll-a concentration, the exponential model of the reflection peak position is better compared to that of the linear model. In addition, there was a good correlation between chlorophyll-a concentration and the first derivation of the reflectance near 684 nm and 633 nm, the corresponding estimation precisions reached 0.89 and 0.91, respectively.