长江口表层细颗粒泥沙絮凝特性及其对光谱反射率的影响
发布时间：2018-10-23 13:05
【摘要】：长江河口径潮流相互作用,受径流输沙、本地泥沙再悬浮和波浪掀沙影响,水体中悬浮泥沙含量高启,其中粒径小于0.032mm的颗粒占绝大多数,加之受盐淡水混合作用的影响,细颗粒泥沙絮凝频繁,给河口泥沙遥感反演精度的提高带来困扰。泥沙絮凝改变了入水光线的传播,从而导致离水辐射特性的改变,在建立卫星遥感数据和水体组分的光谱定量关系时,需要考虑这种泥沙絮凝及其解絮所致的离水辐射特性。本文研究拟通过长江口上段、口门段和口外段的定点观测,获取枯季现场表层絮凝体粒径数据、光学数据和其他水文环境数据,分析河口不同区段絮凝特性,探讨絮凝体对实测光谱反射率的影响,为建立适合长江口表层悬沙的反演模型,提高卫星遥感对二类水体监测精度提供依据。根据不同区段的絮凝体粒径的变化过程,对照同时间段内流速、浊度和盐度的变化过程,分析各个要素对絮凝体粒径的影响。运用主成分分析方法得到,絮凝体粒径在河口上段受流速和浊度控制明显,在口门段的小潮与河口上段类似受流速控制,大潮则受盐度控制,口外段则受盐度控制。投影表面积作为衡量悬浮泥沙浓度和絮凝体平均粒径的综合变量,与悬浮泥沙浓度成正比,与絮凝体平均粒径成反比。不同区段的絮团组成成分、沉速、粒径、有效密度和投影表面积都有较大的差异,枯季絮团分散粒径在空间上呈"小-大-小"分布,絮团现场平均粒径则呈"大-小-大"分布,沉速呈"大-小-大"分布,投影表面积呈"小-大-小"分布。除了悬浮泥沙浓度,现场测量的浊度、絮凝体平均粒径和絮凝体投影表面积与光谱反射率都有较好的相关性,运用多层感知器模型分析各个参数对遥感因子的贡献率大小,并建立传统的单因子回归模型、考虑絮凝的多因子回归模型和神经网络模型。结果表明,回归模型中悬浮泥沙浓度的Gordon模型拟合情况较优,达到0.9091,神经网络模型拟合精度达到0.9688,预测性能最好,逼近真实情况。
[Abstract]:The Yangtze River channel tidal current interaction is affected by runoff and sediment transport, local sediment resuspension and wave sediment lifting, and the suspended sediment content in the water is high, in which the majority of particles with diameter smaller than 0.032mm are affected by the mixed action of salt and fresh water. The frequent flocculation of fine sediment makes it difficult to improve the precision of remote sensing inversion of sediment in estuaries. Sediment flocculation changes the propagation of incoming light, which leads to the change of radiation characteristics. In establishing the spectral quantitative relationship between satellite remote sensing data and water composition, it is necessary to consider the characteristics of water leaving radiation caused by the flocculation of sediment and its deflocculation. In this paper, the surface flocculant particle size data, optical data and other hydrological environment data of the upper, outer and outer segments of the Changjiang Estuary are obtained, and the flocculation characteristics of different sections of the estuary are analyzed. The effect of flocculation on the measured spectral reflectance is discussed in order to establish the inversion model of suspended sediment on the surface of the Changjiang Estuary and to improve the precision of satellite remote sensing monitoring of the second kind of water body. According to the changing process of flocculant particle size in different sections, the influence of each factor on the particle size of flocculant was analyzed by comparing the change process of flow rate, turbidity and salinity in the same time period. By using principal component analysis method, the particle size of flocculant is controlled by velocity and turbidity obviously in the upper section of the estuary, the small tide in the mouth gate section is controlled by the velocity of velocity similar to that in the upper section of the estuary, the large tide is controlled by salinity, and the outside section is controlled by salinity. The projection surface area, as a comprehensive variable to measure the suspended sediment concentration and the average particle size of flocculants, is directly proportional to the suspended sediment concentration and inversely proportional to the average particle size of the flocculants. The composition, settling speed, particle size, effective density and projection surface area of flocs in different sections are all different. In the dry season, the dispersion particle size of flocs is "small-big-small" distribution. On the other hand, the average particle size of flocs is "big-small-large" distribution, sedimentation velocity is "big-small-big" distribution, projection surface area is "small-big-small" distribution. In addition to suspended sediment concentration, the turbidity, mean particle size and projected surface area of flocculants were all correlated with spectral reflectivity. The multilayer perceptron model was used to analyze the contribution rate of each parameter to remote sensing factors. The traditional single factor regression model was established, and the multifactor regression model and neural network model were considered. The results show that the Gordon model of suspended sediment concentration in the regression model is better, reaching 0.9091, and the fitting precision of the neural network model is 0.896. The prediction performance is the best and the model is close to the real situation.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P332.5
[Abstract]:The Yangtze River channel tidal current interaction is affected by runoff and sediment transport, local sediment resuspension and wave sediment lifting, and the suspended sediment content in the water is high, in which the majority of particles with diameter smaller than 0.032mm are affected by the mixed action of salt and fresh water. The frequent flocculation of fine sediment makes it difficult to improve the precision of remote sensing inversion of sediment in estuaries. Sediment flocculation changes the propagation of incoming light, which leads to the change of radiation characteristics. In establishing the spectral quantitative relationship between satellite remote sensing data and water composition, it is necessary to consider the characteristics of water leaving radiation caused by the flocculation of sediment and its deflocculation. In this paper, the surface flocculant particle size data, optical data and other hydrological environment data of the upper, outer and outer segments of the Changjiang Estuary are obtained, and the flocculation characteristics of different sections of the estuary are analyzed. The effect of flocculation on the measured spectral reflectance is discussed in order to establish the inversion model of suspended sediment on the surface of the Changjiang Estuary and to improve the precision of satellite remote sensing monitoring of the second kind of water body. According to the changing process of flocculant particle size in different sections, the influence of each factor on the particle size of flocculant was analyzed by comparing the change process of flow rate, turbidity and salinity in the same time period. By using principal component analysis method, the particle size of flocculant is controlled by velocity and turbidity obviously in the upper section of the estuary, the small tide in the mouth gate section is controlled by the velocity of velocity similar to that in the upper section of the estuary, the large tide is controlled by salinity, and the outside section is controlled by salinity. The projection surface area, as a comprehensive variable to measure the suspended sediment concentration and the average particle size of flocculants, is directly proportional to the suspended sediment concentration and inversely proportional to the average particle size of the flocculants. The composition, settling speed, particle size, effective density and projection surface area of flocs in different sections are all different. In the dry season, the dispersion particle size of flocs is "small-big-small" distribution. On the other hand, the average particle size of flocs is "big-small-large" distribution, sedimentation velocity is "big-small-big" distribution, projection surface area is "small-big-small" distribution. In addition to suspended sediment concentration, the turbidity, mean particle size and projected surface area of flocculants were all correlated with spectral reflectivity. The multilayer perceptron model was used to analyze the contribution rate of each parameter to remote sensing factors. The traditional single factor regression model was established, and the multifactor regression model and neural network model were considered. The results show that the Gordon model of suspended sediment concentration in the regression model is better, reaching 0.9091, and the fitting precision of the neural network model is 0.896. The prediction performance is the best and the model is close to the real situation.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P332.5
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