长春市石头口门水库颗粒物光谱吸收特性

针对东北地区水体,以长春市重要水源地石头口门水库为例,分别于2008年6月13日和9月23 日对该水库进行了水体颗牲物吸收特性研究.结果显示:2008年6月水库悬浮物含量高于9月,而叶绿素a含量低于9月:总颗粒物的吸收光谱曲线类似于非色素颗粒物,非色素颗粒物吸收对其的贡献明显大于浮游植物色素,9月份浮游植物吸收对其的贡献略有增加,吸收曲线在440nm左右能明显看到浮游植物引起的吸收峰;非色素颗粒物在440nm的吸收系数与悬浮物浓度存在较好的函数关系,而与叶绿素a浓度的相关性较弱;浮游植物色素吸收系数较低,色素组成中叶绿索a占主导地位,浮游植物在440nm和675nm的吸收系数与叶绿素a含量均存在较好的指数函数关系;6月浮游植物比吸收系数在440nm和750nm的均值分别为0.0483m2/mg和0.0263m2/mg,而9月份的均值分别为0.0337m2/mg和0.01 87m2/mg.     

基于反射光谱和模拟MERIS数据的太湖悬浮物遥感定量模型

利用地物光谱仪研究了太湖水体的反射光谱特征,通过对比分析,发现580nm反射率值和810nm的反射峰高是太湖悬浮物的敏感波段,并通过光谱微分的方法,发现840nm附近的一阶微分与悬浮物浓度相关性最好,基于上述结论,分别建立了太湖悬浮物的反射光谱和一阶微分遥感定量模型,并利用反射光谱数据,模拟MERIS数据的波段设置,结果表明MERIS第5、12、13波段可以很好的估测太湖的悬浮物浓度.     

秋季太湖悬浮物高光谱估算模型

2004年10月,在太湖设置67个采样点,现场测量了水体遥感反射比、后向散射系数和辅助参数,实验室分析了水体的悬浮物浓度和水色要素吸收系数.本文在对遥感反射比光谱分析的基础上,比较了几种水体遥感反射比光谱估算悬浮物浓度的方法,结果发现广泛应用的带比值项的算法,虽然可以应用于太湖总悬浮物浓度估算,但是普遍存在相对误差较高的弱点.通过对光谱的分析,确定了750hm单波段算法的参数,并提出了利用近红外812nm波峰高度来估算水体悬浮物的方法.文中还详细阐述了为什么比值算法等在太湖水体悬浮物浓度估算中相对误差比较高,并解释了利用近红外估算精度高的原因,并指明以上算法在遥感应用中的优点和不足,以及现实水体(水质)遥感对现有遥感器配置和遥感算法的需求.     

基于叶绿素荧光峰特征的浑浊水体悬浮物浓度遥感反演

内陆水体光学特性复杂,其水质参数遥感反演是当前环境遥感研究的热点与难点.2004年10月在太湖实测了67个站点的遥感反射率与相应站点水质参数浓度,通过对水体反射率光谱的分析发现,秋季太湖悬浮物主导了水体光学特性,叶绿素荧光峰的特征主要体现为悬浮物浓度的变化.据此建立了基于水面实测岛光谱遥感反射率数据的叶绿素荧光峰特征与悬浮物浓度之间的拟合关系,发现二者具有很好的响应关系.具体分析了叶绿素荧光峰绝对高度、基线高度、归一化高度(分别归一化到560nm附近最大反射率波段与近红外810nm附近最大反射率波段)及荧光峰积分面积(包括积分总面积、基线以下面积与基线以上面积)等儿种光谱特征与悬浮物浓度之间的关系,其相关系数(R2)分别为0.8822、0.7483、0.8901、0.8547、0.8927、0.8877、0.8632,平均相对误差分别为27.25%、41.03%、27.11%、25.75%、24.91%、25.47%、27.54%.总体反演精度较高,其中总积分面积法效果最好,基线高度法效果最差,而叶绿索荧光峰波段的位移与悬浮物浓度之间不存在明显的相关性.研究结果表明叶绿素荧光峰特征在浑浊内陆水体悬浮物浓度信息提取中具有很好的应用前景,该方法可为浑浊的二类水体悬浮物遥感反演提供了一个新思路.     

结合Landsat ETM与实测光谱估测太湖叶绿素及悬浮物含量

实地测试太湖水体的反射光谱,实验室分析水样,运用相关分析法探求叶绿素、悬浮物的光谱特征波段,估测叶绿素和悬浮物含量;对比LandsatETM波段,运用不同的函数曲线对相应的波段组合进行回归拟合,建立相应的估测模型,选取精度最好的两个分别对太湖的叶绿素和悬浮物含量进行估测.结果表明:(1)对多光谱遥感而言,LandsatTM/ETM是定量获取叶绿素和悬浮物的较好的数据源,但不是最适合的数据源;(2)通过ETM3与叶绿素建立一定的函数关系来估测叶绿素含量具有较高的精度,其中利用算术组合ETM3/ETM1估测叶绿素的精度最高;(3)ETM4与悬浮物具有较高的相关度,其中利用算术组合ETM4/ETM1估测悬浮物含量的精度最高;(4)LandsatETM卫星影像中,不同尺寸的像元窗口影响水质参数的估测精度;对叶绿素估测而青,7×7或者5×5窗口比较适合,对悬浮物估测而言,一般不超过3×3.     

有色溶解有机物吸收光谱模型对比

利用6种统计模型对吉林省石头口门水库、松花湖CDOM吸收光谱曲线(350-650nm)进行拟合,从统计F值大小和归一化残差两个角度均证明双曲线模型效果最好;采用15个波段范围拟合CDOM吸收均值光谱斜率S,单指数模型S值变化幅度较大,平均变异系数为12.41%;而双曲线模型S值相对稳定,变异系数为5.85%.将双曲线模型应用于生物光学模型,研究表明双曲线模型可以反映CDOM时空变化规律和物质组成;且双曲线模型拟合光谱斜率S值与CDOM特征波长吸收具有很好的负相关关系,呈幂指数递减,决定系数达0.8137.     

吉林查干湖水体叶绿素a含量高光谱模型研究

叶绿素a含量能够在一定程度上反映水质状况,高光谱遥感可有效反演叶绿素a含量.该研究通过分析水体叶绿素a浓度与其高光谱反射特征之间的相关关系,采用单波段相关分析、波段比值、微分光谱和神经网络模型等多种算法建立了叶绿素a高光谱定量模型.结果表明:叶绿素a与单波段光谱在蓝、绿波段相关系数较低,而在红光与近红外波段有明显提高,微分光谱也表现出同样的趋势;反射率比值算法模拟效果好于线性回归法;神经元网络模型可以大大提高实测光谱数据的反演能力,确定性系数高达0.95.这为今后利用星载高光谱传感器在查干湖进行叶绿素a浓度大面积遥感反演提供了研究基础.     

基于偏最小二乘法的巢湖悬浮物浓度反演

悬浮物浓度是水质评价的重要参数.对2009年6月巢湖实测的高光谱数据进行小波变换以去除数据冗余,减少建模时间.考虑到不同的小波基函数和分解尺度对数据压缩的影响,采用3个指标作为评价标准,最终选择小波基函数为Db4,分解尺度为4的小波变换,经小波变换后,原来的451个波段的光谱数据压缩为34个特征变量.利用小波变换后的光...     

基于HJ-CCD和MODIS的吉林省中西部湖泊透明度反演对比

水体透明度能够反映光在水体中的穿透程度,影响水生植被及以光为依赖条件的水生生物的分布,获取透明度的传统方法是采用透明度盘进行测量,但也可以通过遥感方法获得.环境减灾卫星是专门用于环境与灾害监测预报的小卫星星座,影像覆盖范围广,空间、时相分辨率较高,可以为水环境遥感提供较好的数据源.MODIS数据在近岸水体和内陆大型湖泊水环境监测中也有广泛应用,它的时相分辨率也很高,但空间分辨率低.利用HJ-1A卫星CCD数据和MODIS日反射率产品(MOD09GA),以2012年9月吉林省石头口门水库、二龙湖、查干湖、月亮泡等地的实测透明度为基础(实测点数74个,最小值为0.134 m,最大值为1.410 m,平均值为0.488 m),根据灰色关联度选取构建模型的波段组合,建立水体透明度反演模型.HJ1A-CCD数据与MOD09GA数据建立的模型R2分别为0.639和0.894,均方根误差(RMSE)分别为0.248和0.135,模型验证的平均相对误差(MRE)分别为17.1%和9.5%,RMSE分别为0.207和0.089.MODIS数据以其较高的辐射分辨率使模型精度较高,但是HJ数据在应用于透明度小于1 m的水体时精度也较高(MRE=13.5%,RMSE=0.066).HJ-CCD数据在空间分辨率上的优势使其能够获得透明度空间分布的细节信息.比较两者反演得到的湖泊平均透明度,结果较为一致.     

高光谱遥感混合像元盲分离模型

针对高光谱遥感影像中"混合像元"问题,在前人研究的基础上,本文提出了结合盲源信号分离技术的混合像元盲分离模型,该模型的核心算法是HFC算法和基于负熵的改进FastICA算法,优点是能够通过非常少的先验知识就可以进行混合像元分解,完成特征提取工作,且收敛速度快,精度较高.采用高光谱模拟数据和加噪数据,分别检验混合像元盲分离模型的有效性和稳定性,实验结果表明:模型受端元提取数目多少的影响以及高斯白噪声的影响较小,对高光谱遥感数据特征提取具有有效性和稳定性.     

Multivariate approach for chlorophyll-a and suspended matter retrievals in Case II type waters using hyperspectral data

Abstract

Remote sensing has become promising in providing temporal and spatial information on biogeodynamics in large and open freshwater bodies. In optically complex environments, such as in the Western Basin of Lake Erie (WBLE), the water contains multiple biogeochemical constituents or colour producing agents (CPAs), such as phytoplankton, suspended matter and dissolved organic carbon; identifying and analysing such in-water constituents is crucial for understanding and assessing many biogeochemical processes. For example, concentrations of chlorophyll-a and total suspended matter can be used as proxies to assess phytoplankton dynamics and particulate loading. However, quantitative estimation of their concentrations from satellite observations is complicated when working with mixed spectral signatures. Hyperspectral remote sensing is fast emerging as a key technology for advanced and improved understanding of optically complex waters. This study estimates concentrations of chlorophyll-a and total suspended matter (TSM) in the WBLE by applying the partial least squares (PLS) method to a full range (400–900 nm) of continuous narrow spectral bands. The PLS method models the covariance between hyperspectral bands and CPAs, and identifies the optimal bands that characterize most of the variance in the CPAs. This method avoids the curse of dimensionality and the effects of multi-collinearity, a challenge that is associated with new-generation hyperspectral satellite sensors. Validation parameters for the PLS-based models produced R² of 0.84 for chlorophyll-a (RMSE = 1.18 μg/L), and R² of 0.90 for TSM (RMSE = 1.26 mg/L), illustrating the potential of the PLS method for isolating and extracting absorption features characterizing the various CPAs in optically complex Case II type waters.

Editor Z.W. Kundzewicz Associate editor Not assigned     

基于高频次GOCI数据的太湖悬浮物浓度短期动态和驱动力分析

总悬浮物是水体中重要的光学敏感物质之一,很大程度上决定了水柱中光的吸收、散射和衰减,同时吸附营养盐、重金属和有毒有害物,对水体物质生物地球化学过程、沉积物埋藏动力和湖泊环境演化具有重要的意义.基于星地同步实验和静止水色成像仪GOCI(Geostationary Ocean Color Imager)构建了太湖悬浮物浓度估算模型,并分析了典型风浪过程中太湖悬浮物浓度短期动态变化过程.研究表明:对太湖水体悬浮物浓度较为敏感的波段为GOCI的第7波段(745nm)和第8波段(865 nm),悬浮物浓度与对应波段遥感反射率线性相关决定系数分别为0.72和0.55;基于GOCI第7波段的悬浮物浓度单波段遥感估算模型能较为准确地估算太湖的悬浮物浓度,模型相对均方根误差和平均绝对百分误差分别为28.3%和24.4%.通过研究典型风浪过程前后太湖悬浮物浓度变化发现其短期动态变化显著,风速、风向是悬浮物浓度短期动态变化的重要驱动因素,悬浮物浓度与风速呈正比,并随着风向扩散;高频连续GOCI影像结果显示悬浮物浓度短期动态变化对风浪扰动的响应有一定的滞后性,滞后时间为数小时到1天,悬浮物沉降与沉积物再悬浮的临界风速约为3.4 m/s.     

Hyperspectral feature analysis of chlorophyll a and suspended solids using field measurements from Taihu Lake,eastern China

Abstract

We report results of three field campaigns conducted at 39 stations. At each station, we measured reflectance spectra in situ and collected water samples for measuring chlorophyll a (CHL) and suspended solids (SS) concentrations in the laboratory. To identify the indicative bands and develop suitable estimation models for CHL (C _CHL) and SS (C _SS) concentrations in Taihu Lake, a spectral-feature method and a derivative method were applied. The following conclusions were drawn: (a) the critical C _CHL and C _SS probably causing their spectral variation are, respectively: 0, 10, 50 and 75 μg L^?1, and 0, 10, 50 and 100 mg L^?1; (b) the derivative method is better than the spectral-feature method for estimating C _CHL and C _SS; (c) the optimal variable for CHL is a reflectance second-order derivative at 501 nm or a reflectance first-order derivative at 698 nm; the optimal variable for SS can change when its concentration is low and the range is narrow; otherwise, the optimal variable is a reflectance first-order derivative at 878 nm; and (d) the CHL and SS have an effect on one another's retrieval. The C _CHL estimation accuracy would benefit from narrowing the C _SS range. With C _CHL increasing and its range broadening, the corresponding C _SS estimation accuracy decreases gradually.     

三峡小江回水区透明度季节变化及其影响因子分析

三峡成库后其季节调蓄过程使该水域湖沼学特征具有独特性.根据三峡小江流域回水区段为期2年的定位跟踪观测,对透明度(SD)和主要环境指标的相互关系进行分析研究.研究期间,小江回水区透明度均值为170±7cm,各采样点透明度差异不明显且季节变化过程一致,自春末夏初开始降至最低水平,夏季汛期相对稳定,夏末入秋持续升高,冬季维持在较高状态,入春后下降.对透明度和主要环境指标的相关性分析发现,无机悬浮颗粒(PIM)是影响透明度的主要指标.透明度同PIM、Chl.a多元回归模型为:SD=(-89.389±8.101)·lg(PIM)+(-84.008±8.624)·lg(Chl.a)+(264.132±8.232).汛期低水位状态下(145-150m)小江回水区水动力条件趋于天然河道,河道输沙量增加使无机悬浮颗粒含量远高于藻类生物量而成为影响透明度的主要环境指标.在中水位(150-156m)和高水位(156m以上),虽然藻类进入非生长季节,但水位抬升和水体滞留时间的延长促使悬浮颗粒物大量沉淀,悬浮生长于表层水体的藻类成为影响透明度的主要环境指标,生物作用对透明度的影响明显.     

Estimating suspended sediment concentrations in areas with limited hydrological data using a mixed‐effects model

Sediment rating curves are commonly used to estimate the suspended sediment load in rivers and streams under the assumption of a constant relation between discharge (Q) and suspended sediment concentrations (SSC) over time. However, temporal variation in the sediment supply of a watershed results in shifts in this relation by increasing variability and by introducing nonlinearities in the form of hysteresis or a path‐dependent relation. In this study, we used a mixed‐effects linear model to estimate an average SSC–Q relation for different periods of time within the hydrologic cycle while accounting for seasonality and hysteresis. We tested the performance of the mixed‐effects model against the standard rating curve, represented by a generalized least squares regression, by comparing observed and predicted sediment loads for a test case on the Chilliwack River, British Columbia, Canada. In our analyses, the mixed‐effects model reflected more accurate patterns of interpolated SSC from Q data than the rating curve, especially for the low‐flow summer months when the SSC–Q relation is less clear. Akaike information criterion scores were lower for the mixed‐effects model than for the standard model, and the mixed‐effects model explained nearly twice as much variance as the standard model (52% vs 27%). The improved performance was achieved by accounting for variability in the SSC–Q relation within each month and across years for the same month using fixed and random effects, respectively, a characteristic disregarded in the sediment rating curve. Copyright © 2012 John Wiley & Sons, Ltd.     

Measuring and modeling vertical gradients in suspended sediments in the Solimões/Amazon River

Accurately measuring sediment flux in large rivers remains a challenge due to the spatial and temporal cross‐sectional variability of suspended sediment concentrations in conjunction with sampling procedures that fail to accurately quantify these differences. This study presents a field campaign methodology that can be used to improve the measurement of suspended sediment concentrations in the Amazon River or similarly large rivers. The turbidity signal and Rouse model are together used in this study to define the spatial distribution of suspended sediment concentrations in a river cross‐section, taking into account the different size fractions of the sediment. With this methodology, suspended sediment fluxes corresponding to each sediment class are defined with less uncertainty than with manual samples. This paper presents an application of this methodology during a field campaign at different gauging stations along a 3,000‐km stretch of the Solimões/Amazon River during low water and flood periods. Vertical concentration profiles and Rouse model applications for distinctive sediment sizes are explored to determine concentration gradients throughout a cross‐section of the river. The results show that coupling both turbidity technology and the Rouse model may improve our understanding of the spatial distribution of different sediments fractions sizes in the Solimões/Amazon River. These data are very useful in defining a pertinent monitoring strategy for suspended sediment concentrations in the challenging context of large rivers.     

Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM+: an example from the Yangtze River,China

Suspended sediment concentration (SSC) is a critical parameter in the study of river sediment transport and water quality variation, but traditional measurement methods are costly and time‐consuming. This paper is focused on presenting a methodology that may be useful in estimating SSC which is of key importance in process geomorphology and hydrology. In previous studies, remote sensing has been applied to estimate the SSC of sea waters as well as low turbid inland waters like lakes, reservoirs and short river reaches visible within a single Landsat satellite image coverage. Rivers, especially highly turbid large rivers, have largely been ignored. The dataset used in this paper includes measured SSC and multi‐temporal Landsat ETM+ images covering most part of the Yangtze River. Using an effective easy‐to‐use atmospheric correction method that does not require in situ atmospheric conditions, retrieved water reflectance of Band 4 was found to be a good SSC indicator within the large SSC range 22–2610 mg l^–1. The newly developed regression relation between SSC and water reflectance of Band 4 appears to be able to provide a relatively accurate SSC estimate directly from Landsat ETM+ images for the Yangtze River from the upper, the middle to the lower reaches. With the relation it is possible to estimate or map out SSC dynamics of large rivers which lack SSC data due to constraints of conventional measurements. Copyright © 2009 John Wiley & Sons, Ltd.     

氮和磷是影响三峡库区香溪河表层沉积物中微生物群落结构的主要因素

香溪河是三峡库区最大的入库支流,目前关于香溪河沉积物微生物群落结构特征及其主要影响因素尚不明确。本研究监测香溪河上、中、下游上覆水及沉积物的主要理化指标,通过16S rDNA高通量测序研究香溪河表层沉积物中微生物群落结构特征,使用统计学软件分析细菌与理化指标间的关系。结果表明,上覆水中氨氮(NH₃-N)、叶绿素a(Chl.a)浓度自下游向上游逐渐升高,其中NH₃-N浓度从下游的2.04 mg/L升高到上游的3.14 mg/L。不同点位上覆水中磷酸盐(PO³₄^-)浓度在0.14～0.18 mg/L之间波动;同一点位间隙水中NH₃-N、PO₄^3-浓度明显高于上覆水,10～15cm沉积物间隙水中NH₃-N与PO₄^3-浓度分别是0～5 cm沉积物间隙水的3.4倍和1.7倍。沉积物中Fe(Ⅱ)、Fe(Ⅲ)、酸可挥发性硫化物和铬还原性硫化物含量均表现为上游点位...