导　　师： 韦玉春

学科专业： 081802

授予学位： 硕士

作　　者： ;

机构地区： 南京师范大学

摘　　要： 本文以太湖梅梁湾为研究对象,依据水体表面光谱和水质参数特征,对不同时期的梅梁湾水体进行分类——浮游藻类主导的水体和非藻类颗粒物主导的水体。在此基础上,利用四个季节和两种类别的数据分别对已有的典型悬浮物浓度估测模型进行验证,并利用其敏感波段进行模型的重建。最后,在总结前人研究成果的基础上,进行梅梁湾悬浮物浓度反演模型的构建,主要研究内容与研究成果包括： /(1/)梅梁湾水体的光学特性 太湖梅梁湾水体具有典型的内陆二类水体的光谱特性,然而,由于采样的时间不同,导致其表观光学特性存在较大的差异,其中,2010年9月、2011年3月、2011年4月、2011年7月的梅梁湾水体表现出了浮游藻类主导的二类水体的光学特性,而2010年10月、2011年5月,则主要表现为非藻类颗粒物主导的光谱反射率光谱特征。尽管藻类的繁殖程度跟季节有着很大的关系,但具体到各个月份,则情况又有所不同,研究中发现5月份的梅梁湾水体叶绿素浓度异常的低,表面光谱表现为非藻类颗粒物主导,以季节分类的悬浮物浓度反演模型存在一定的缺陷。 /(2/)梅梁湾水体类别的划分 分析了不同时期梅梁湾水体表观光学特性,叶绿素a浓度,悬浮物浓度,依据表面光谱数据的特性,将水体划分为浮游藻类主导的类型Ⅰ水体和非藻类悬浮物主导的类型Ⅱ水体,并利用总悬浮物浓度与叶绿素a浓度之比、有机悬浮物与无机悬浮物浓度之比加以确认。水体类别划分的意义在于检验模型对不同类别水体的适应性。 /(3/)典型悬浮物浓度估测模型的检验 利用不同季节不同类别的水体数据进行已有悬浮物浓度估测模型的检验,结果发现：表面光谱的测量和计算方式不同,造成数值的偏差,所建立的模型无法适用于其他数据；简单的模型/(单波段线性模型、单波段幂函� Based on the data of Meiliang Bay, located in the north of Taihu Lake, our paper divided several datasets of spectrum reflectance into two categories according to the apparent optical properties and water quality parameters-one dominated by phytoplankton, and the other dominated by non-algae particles. On this basis, we evaluated the accuracy and applicability of the typical inversion models of suspended matter using these two datasets, and then reconstructed the model with its sensitive bands. Finally, suspended sediment inversion model of Meiliang Bay was constructed after summarizing the previous research. The main conclusions of this study are listed as follows: /(1/) Optical properties of the water in Meiliang bay The spectral characteristics of the water in Meiliang Bay show similar characteristics with that in typical inland water. However, the apparent optical properties vary greatly because of the difference of sampling time. The optical properties of datasets collected in September2010, March2011, April2011, and July2011showed inland water optical properties of phytoplankton. The optical properties of datasets in October2010and May2011were dominated by non-algal particles. Although the bleeding of the algae was largely related with the seasons, there was some difference for each month. Our study found that chlorophyll concentration was low in May which was donated by non-algal particulate particles. Therefore, dividing the inversion models by the seasonal difference had the defect. /(2/) The division method of the water categories of Meiliang Bay This study analyzed the apparent optical properties of different datasets of Meiliang Bay. According to concentration of chlorophyll and suspended solids, as well as the surface characteristics of the spectral data, we divided the datasets into two categories-Class I water dominated by algae and Class II water dominated by non-algae particles. Actually, this separation was also the division of the water category. This division method could be co

关 键 词： 光谱反射率 模型研究 表观光学特征 太湖梅梁湾 悬浮物

领　　域： [环境科学与工程]