中文摘要：

叶绿素浓度反演算法主要有经验方法、半经验/分析方法和分析方法,其中半经验/分析方法应用最为广泛。但是反演的模型以及模型中的参数和反演精度都随着水体中叶绿素浓度的变化而改变。不同的季节水体中叶绿素浓度不同,水体反射光谱曲线特征和与叶绿素浓度相关性较高的敏感波段也不一致,使得各季节选用的反演模型和模型中的参数也存在一定的差异。本文在对2005年1—10月份叶绿素a（Chla）浓度季节差异进行分析的基础上,对4—10月份同步测量的水体光谱数据分春、夏、秋三个季节进行分析,分季节建立叶绿素浓度反演模型,并对它们进行比较,旨在为各季节选择最佳的反演模型。研究结果表明：春季和秋季选用波段比值算法反演精度较高,其中对数模型,线性模型和一元二次模型都有较高的相关性;夏季选用微分算法较好,该算法所建立的三种模型均具有较高的相关性。

英文摘要：

Retrieval chlorophyll concentration models could be classified into three categories, namely empirical approach, semi-empirical/analytical approach and analytical approach. Among those algorithms, the semi-empirical/analytical algorithm is applied widely. But as chlorophyll concentration changes, retrieval models as well as parameter in models and retrieval precision are changed accordingly. Different season has different chlorophyll concentration in water body, character of water reflect-spectral and sensitivity band which are better related to chlorophyll concentration also have difference, resulting in the difference in retrieval models and parameters in models for each season. This paper first analyzes the seasonal difference of chlorophyll a （Chla） concentration data which were collected from January to October in 2005, and then analyzes reflect- spectral data which were collected synchronous with water sample from April to October in 2005. We divided the time span into spring, summer and autumn, built different models for the three seasons and compared them, so as to build an optimal model for each season. The research result indicates that： in spring and autumn, the band-ration algorithm has better retrieval precision, among different models, logarithm model, liner model and quadratic equation model has higher relativity; in summer, differential algorithm has better retrieval result, three models built dependent on the algorithm has high relativity.