大型沉水植物苦草的光谱特征识别

地物特征与其光谱特征的关系是解译遥感影像的关键.利用地物光谱仪在实验室和上海“梦清园”人工湿地中,分别实测了不同盖度沉水植物苦草的反射光谱特征.结果表明随着苦草盖度的增加,其光谱反射率也相应增加,不同盖度苦草的光谱反射率差异主要表现在500～650 nm和700～900 nm 波段范围.受水体环境影响,实验室模拟试验与室外控制试验测得的苦草光谱反射率差异主要表现在近红外波段（700～900 nm）.分别将苦草的不同盖度与其在QuickBird多光谱遥感影像4个波段与盖度相关性最大波段处的光谱反射率进行回归分析,得到了较好的线性关系.应用回归分析得到的线性方程,可以根据测定的光谱反射率定量反演水体中的苦草盖度.研究结果可为监测沉水植物的高光谱遥感影像判读和解译分类提供技术支撑,为大尺度遥感监测沉水植物的分布和动态变化提供科学依据.     

盖度与冠层水深对沉水植物水盾草光谱特性的影响

遥感技术可应用于大尺度实时监测沉水植物的分布与生长状况。然而沉水植物的光谱特征受其冠层在水下深度的影响,从而影响湖泊和河流中沉水植物的遥感影像解译与信息提取。应用地物光谱仪,通过野外原位测定和室外控制试验,实测了沉水植物水盾草(Cabomba caroliniana)群落冠层在水下不同深度的反射光谱,分析了冠层水深对水盾草反射光谱的影响,并建立了基于光谱反射率和冠层水深的水盾草群落盖度反演模型。研究结果表明(1)不同盖度的水盾草群落光谱反射率的基本特征主要体现在绿光和近红外波段;(2)水盾草群落的光谱反射率与冠层水深基本呈负相关,相同盖度水盾草群落的光谱反射率随冠层水深的增加而减小,在近红外波段尤其明显;(3)水盾草群落冠层水深越小,其盖度与光谱反射率的相关性越强,且水盾草群落盖度越大,其光谱反射率与冠层水深的相关性越显著;(4)水盾草光谱反射率与盖度相关的最佳波段在692—898 nm,与冠层水深相关最佳的波段在710 nm和806 nm附近;(5)在710 nm和806 nm处建立的结合冠层水深的修正模型,无论是回归方程决定系数(R2),还是水盾草群落盖度的反演精度都明显高于仅用光谱反射率反演盖度的简单模型,因此可有效减除冠层水深对反演精度的影响。本研究的结果可为遥感监测沉水植物的分布和动态变化,以及沉水植物生物物理参量反演提供科学依据。     

水稻叶片光谱对亚铁胁迫的响应

在湖泊富营养化治理过程中,除了要注意控制氮、磷的输入,还应考虑铁（Fe）的调控作用．本文基于“Fe^2＋假说”,探索性地将植物光谱效应应用于湖泊水体富营养化遥感预警机制研究．在水培条件下分析了亚铁（Fe^2＋）胁迫对水稻（Oryza sativa L．）体内Fe含量、叶绿素浓度及可见一近红外特征光谱的影响,并对其相关关系进行了深入探讨;同时,应用3个光谱定量化指标A1（Fe^2＋胁迫水稻叶片在460～670nm波段反射率变化积分值）、A2（Fe^2＋胁迫水稻叶片在760～1000nm波段反射率变化积分值）、S（Fe^2＋胁迫水稻叶片光谱曲线红边“蓝移”强度）分别建立了水稻叶片Fe含量与叶片光谱之间的定量相关关系模型．结果表明,随培养液中Fe^2＋浓度的升高,水稻体内Fe含量逐渐增加,叶绿素浓度降低,叶片光谱反射率在可见光波段升高,在近红外波段降低,同时红边发生“蓝移”．水稻叶片的3个光谱指标A1,A2,S与叶片中Fe含量都具有显著相关性,相关系数分别为0．933（P〈0．01）,-0．965（P〈0．01）和0．946（P〈0．01）,且A1,A2和S3个参数都能够较好地模拟（复相关系数R^2〉0．96）和估测水稻叶片Fe含量。     

上海盐沼植被的多季相地面光谱测量与分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键.研究选择上海崇明东滩候鸟自然保护区的盐沼植被为研究对象,使用ASD便携式地物光谱仪测定芦苇、互花米草、海三棱藨草和糙叶苔草4类主要群落的春、夏、秋各季冠层反射光谱,并计算生成350～1000nm的反射率曲线的一阶导数曲线,在此基础上分析反射率与一阶导数曲线在可见光与近红外波段以及物候特征的“绿峰”和“红边”等波段的差异.分析显示,不同盐沼群落在各生长季都有较独特的光谱特征,四类盐沼群落的光谱特征在季相上表现各异.上海地区盐沼植被各类群落的遥感识别和分类的适宜季相不尽相同,应用多季相影像进行综合分类可取得较好的效果.研究结果可为遥感监测外来种互花米草的空间分布与动态提供技术支撑,为高光谱遥感的影像判读和解译分类以及盐沼植被制图提供科学依据.     

太湖梅梁湾夏季水体组分光谱吸收特性

2006年8月16、17日对太湖梅梁湾湖区15个样点水体进行采样,利用分光光度计和定量滤膜技术测量了水体要素CDOM、非藻类颗粒物和浮游植物的吸收系数,同时进行水质参数的测定,分别对各水体要素的光谱吸收特性进行分析,并结合水质参数建立相应的区域模式.其中,分UV-C(250～290nm)、UV-B (290～320nm)、UV-A(320～400nm)和蓝光(400～500nm)4个波段建立CDOM光谱吸收的关系模式,同时发现曲线斜率值S与440nm处吸收系数存在很好的二次函数关系,在紫外和蓝光波段R2分别达到0.958和0.835;总悬浮物的光谱吸收特征在不同深度处有些相近,有些则存在明显差异,主要是由有机和无机颗粒物剖面分布的不确定性和总悬浮物浓度所引起;非藻类颗粒物吸收系数在400～700nm的指数函数拟合斜率值S的变化范围为0.0056～0.0090nm-1(平均值(0.0070±0.0008)nm-1),各样点指数函数拟合的R2在0.91以上.在可见光波段范围各水体要素对总吸收系数的贡献大小顺序是:浮游植物＞非藻类颗粒物＞CDOM.浮游植物在蓝、绿和红光波段的平均贡献率都在0.5以上,是水体吸收的主要贡献者;在蓝、绿和红光波段,非藻类颗粒物的平均贡献率分别为0.350±0.145、0.412±0.162和0.232±0.125,CDOM的分别为0.121±0.052、0.088±0.059和0.050±0.038.     

太湖水体漫射衰减系数的光学特性及其遥感反演模型

利用2007年11月8—22日太湖实测光谱数据和水质分析数据,分析了秋季太湖水体漫射衰减系数（K_d）的光谱特性及其影响因子,并在此基础上,利用水面以上遥感反射率, 以490 nm波长的漫射衰减系数［K_d(490)］作为中间变量,建立了漫射衰减系数与水面以上遥感反射率的遥感反演模型.结果表明：在可见光波段范围内,秋季太湖水体大部分点位的漫射衰减系数随波长的增加呈指数递减趋势;由于部分点位浮游藻类的含量较高,对漫射衰减系数影响较大,导致这些点位的漫射衰减系数在波长675 nm附近出现峰值;无机悬浮物对漫射衰减系数的影响大于有机悬浮物,主要是由于悬浮物中无机悬浮物的含量远大于有机悬浮物.漫射衰减系数与遥感反射率具有很好的相关性,以R_rs(550)、R_rs(675)、R_rs(731)作为自变量,与K_d(490)进行回归分析,得出K_d(490)与R_rs(731)具有很好的线性关系,而利用R_rs(550)、R_rs(675)、R_rs(731)进行多元线性回归的效果（R²>0.96）好于单波段［R_rs731)］算法.     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

浅水富营养化湖泊生态修复过程中大型沉水植物群落结构变化以及对水质影响

实现富营养化浅水湖泊生态修复的根本途径是一个完善的生态系统的建立,而实现这一途径的核心则是沉水植物群落的恢复。因此,研究生态修复过程中沉水植物群落结构变化以及水质变化,对富营养化浅水湖泊的生态修复具有重要意义。本研究从2007年12月至2008年7月,对惠州西湖南湖生态修复过程中沉水植物群落结构变化以及对水质的影响进行了研究,以期为富营养化浅水湖泊生态修复提供重要的理论依据和实践指导。研究结果表明:5月份之前,南湖中黑藻比例明显高于苦草,分别为65%和30%左右;之后,在鱼类调控以及人为干扰等措施下,黑藻比例急剧下降,降至约5%以下;而苦草则大幅增到95%左右。沉水植物覆盖率也从40%增加到约95%左右。由此可见,南湖已由先锋物种黑藻为主的生态系统逐步过渡到以苦草为主生态系统。在沉水植物群落结构变化过程中,水中TN、TP以及chl a浓度变化趋势基本一致。在群落结构变化之前都保持较低水平,在5月份达最大值后迅速降低。其可能原因是沉水植物群落转变过程中,生态系统内部各因子变化剧烈,由原来以吸收水体中营养为主的黑藻生态系统逐步过渡到以底泥中营养为主的苦草生态系统,加之鱼类搅动及此过程中人为、降雨等因素干扰,导致营养盐水平和chl a浓度增加,但随着生态系统的逐步稳定,经过大型沉水植物对营养盐的竞争、悬浮物抑制以及克藻效应等因素,水体质量又逐步得到改善,TN、TP 浓度迅速降到0.840mg·L^-1和0.028 mg·L^-1L,chla则降到2.562μg·L^-1左右,透明度增加到120cm。由此可见,南湖已由修复前浮游植物主导的混浊态,经过一个短暂的黑藻为主的生态系统,逐步过渡到已苦草为主且较稳定的清水态生态系统。     

泥炭藓群落的光谱特征及遥感识别研究

以中位泥炭藓(Sphagnum magellanicum Brid.)为研究对象,分别从实测冠层光谱和遥感传感器模拟光谱层面分析其群落的光谱特征。研究结果显示,中位泥炭藓与北方针叶林光谱差异明显,最佳光谱识别区间为740～1140 nm和1230～1412 nm。在可见光波段上,中位泥炭藓与云杉(Picea engelmannii Parry ex Engelmann)和黑松(Pinus contorta Douglas ex Loudon)的绿峰位置有所差异。水竹(Phyllostachys heteroclada Oliver)和中位泥炭藓的光谱识别特征波段集中在可见光-近红外波段,分别为400～550、560～696、1025～1143 nm。中位泥炭藓与北方针叶林以及水竹的特征光谱区间存在细微差异,且与水竹在可见光波段有较好的可分性,因此不同纬度带上中位泥炭藓群落的特征谱宽有所差异。红外波段是中位泥炭藓识别的最佳光谱区间。在多光谱遥感水平上,中位泥炭藓识别效果较好,传感器的识别能力依次为:MSI> ALI> OLI> ASTER。在2个中位泥炭藓群落的光谱特征分析中,导数、对数、包络线去除法的光谱降维能力有所差异,其中包络线去除法效果最好。     

基于光谱特征和HSV变换融合MODIS影像的滇池叶绿素a浓度监测

使用实测高光谱数据,研究滇池水体的光谱特征,应用统计方法建立滇池叶绿素a浓度的高光谱反演模型,并基于滇池水体的光谱特征,运用HSV变换融合遥感影像技术,监测水体叶绿素a浓度分布。结果表明:滇池水体光谱的反射峰位于550和700nm附近;此2个反射峰的位置和大小对水体叶绿素a浓度的变化反应最敏感。随着水体叶绿素a浓度升高,2个反射峰的峰值越接近,同时,550nm附近反射峰向短波方向偏移,而700nm附近反射峰向长波方向偏移。用这2个反射峰峰值的差值作为参数建立的滇池水体叶绿素a浓度估测模型,其精度较高;HSV变换融合MODIS遥感影像的假彩色合成图能直观反演滇池水体叶绿素a浓度的空间分布。     

The spectral responses of a submerged plant <Emphasis Type="Italic">Vallisneria spiralis</Emphasis> with varying biomass using spectroradiometer

The relationship between land features and their spectral characteristics is a key for the interpretation of remote sensing images. This study was designed to investigate the spectral responses of Vallisneria spiralis, a common submerged aquatic plant in Shanghai, with varying biomass both in the laboratory and in the Middle Lake section of a field-scale constructed wetland, using a FieldSpec™ Pro JR Field Portable Spectroradiometer. The results showed that the reflectance rate of V. spiralis increased with its increasing biomass, and this was exhibited both at the visible band (500–650 nm) and the near infrared band (700–900 nm). The water environment influenced the reflectance rate and the primary differences between the laboratory and field results mainly occurred at the near-infrared band (700–900 nm). A regression analysis was carried out between the biomass of V. spiralis and the reflectance rate at the wavelengths of QuickBird™ bands where the biomass responded most strongly. The results of this analysis showed a clear linear relationship by which the biomass of V. spiralis could be quantitatively deduced from the reflectance rate measured in situ. The implications of this observation, in terms of the ability of hyperspectral remote sensing to estimate and monitor the distribution and dynamics of submerged aquatic vegetation on a large scale, are discussed. Handling editor: S. M. Thomaz     

Remote sensing for lake research and monitoring – Recent advances

Lakes are important ecosystems providing various ecosystem services. Stressors such as eutrophication or climate change, however, threaten their ecological functions. National and international legislations address these threats and claim consistent, long-term monitoring schemes. Remote sensing data and products provide synoptic, spatio-temporal views and their integration can lead to a better understanding of lake ecology and water quality. Remote sensing therefore gains increasing awareness for analysing water bodies. Various empirical and semi-analytical algorithms exist to derive remote sensing indicators as proxies for climate change or ecological response variables. Nevertheless, most monitoring networks lack an integration of remote sensing data. This review article therefore provides a comprehensive overview how remote sensing can support lake research and monitoring. We focus on remote sensing indicators of lake properties, i.e. water transparency (suspended particulate matter, coloured dissolved organic matter, Secchi disc depth, diffuse attenuation coefficient, turbidity), biota (phytoplankton, cyanobacteria, submerged and emerged aquatic vegetation), bathymetry, water temperature (surface temperature) and ice phenology (ice cover, ice-on, ice-out). After a brief background introducing principles of lake remote sensing we give a review on available sensors and methods. We categorise case studies on remote sensing indicators with respect to lake properties and processes. We discuss existing challenges and benefits of integrating remote sensing into lake monitoring and ecological research including data availability, ready-to-use tools and accuracies.     

Combined application of remote sensing and in situ measurements in monitoring environmental processes

Combined use of remote sensing in the visible, infrared and microwave spectral regions, direct in situ measurements and model numerical experiments makes it possible to study inland water bodies as elements of water body-catchment-atmosphere-systems with good spatial and temporal resolution. In this paper examples are presented of the remote sensing methods developed for detection of hydrodynamics of large water bodies (e.g. frontal and upwelling zones, internal waves, warm and cold surface layers), monitoring of chlorophyll concentration, suspended minerals and dissolved organic matter (DOM) in lakes, mapping of shallow water zones, wetlands and landscape structures, monitoring of ecological condition and changes of drainage basins, and studying the state of the atmosphere over lakes and catchment areas.     

叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型

利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持.采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量.以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与“三边”参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型.结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350-760 nm)和红外波段(760-2500 nm)的反射率均呈逐渐降低趋势.(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与“三边”参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于“三边”参数和高光谱植被指数构建了不同尺度下的含水量估算模型.其中,冠层尺度下,黄边面积(SDy)与SRWI( Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2 -2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI( Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2 -0.476x-1.009.通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92％,最大值为97.06％,表明估测模型具有较高的可靠性与普适性.(3)高光谱植被指数与含水量拟合方程的拟合度相对高于“三边”参数与之建立方程的拟合度,说明多波段组合的光谱特征参数更适合含水量的判别.     

Spectral response of the seagrass Zostera noltii with different sediment backgrounds

The efficiency of vegetation indices (VIs) to estimate the above-ground biomass of the seagrass species Zostera noltii Hornem. from remote sensing was tested experimentally on different substrata, since terrestrial vegetation studies have shown that VIs can be adversely influenced by the spectral properties of soils and background surfaces. Leaves placed on medium sand, fine sand and autoclaved fine sand were incrementally removed, and the spectral reflectance was measured in the 400–900 nm wavelength range. Several VIs were evaluated: ratios using visible and near infrared wavelengths, narrow-band indices, indices based on derivative analysis and continuum removal. Background spectral reflectance was clearly visible in the leaf reflectance spectra, showing marked brightness and spectral contrast variations for the same amount of vegetation. Paradoxically, indices used to minimize soil effects, such as the Soil-Adjusted Vegetation Index (SAVI) and the Modified second Soil-Adjusted Vegetation Index (MSAVI₂) showed a high sensitivity to background effects. Similar results were found for the widely used Normalized Difference Vegetation Index (NDVI) and for Pigment Specific Simple Ratios (PSSRs). In fact, background effects were most reduced for VIs integrating a blue band correction, namely the modified specific ratio (mSR₍₇₀₅₎), the modified Normalized Difference (mND₍₇₀₅₎), and two modified NDVIs proposed in this study. However, these indices showed a faster saturation for high seagrass biomass. The background effects were also substantially reduced using Modified Gaussian Model indices at 620 and 675 nm. The blue band corrected VIs should now be tested for air-borne or satellite remote sensing applications, but some require sensors with a hyperspectral resolution. Nevertheless, this type of index can be applied to analyse broad band multispectral satellite images with a blue band.     

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.     

不同干旱条件下夏玉米全生育期冠层吸收光合有效辐射比的高光谱遥感反演

冠层吸收光合有效辐射比(fAPAR)是植被生产力遥感模型的重要参数.但关于不同干旱条件下作物全生育期的fAPAR遥感反演研究仍未见报道.本研究利用2015年夏玉米5个灌水处理模拟试验的高光谱反射率和fAPAR观测资料,分析了不同干旱条件下夏玉米关键生育期fAPAR和高光谱反射率变化特征,探讨了fAPAR与反射率、一阶导数光谱反射率和植被指数的关系.结果表明: 轻度水分胁迫和充分供水条件下,fAPAR较高;重度水分胁迫和重度持续干旱条件下,fAPAR较低.冠层可见光、近红外光和短波红外光区的反射率与fAPAR分别呈负相关、正相关和负相关关系.fAPAR与可见光和短波红外光区的383、680和1980 nm附近的反射率的相关性最强,相关系数均达-0.87.一阶导数光谱反射率与fAPAR相关性强且稳定的波段为580、720和1546 nm,相关系数分别为-0.91、0.89和0.88. 9个常用植被指数与fAPAR呈线性或对数关系,其中,增强型植被指数、复归一化植被指数、土壤调节植被指数和修正的土壤调节植被指数与fAPAR的关系模型最好,决定系数(R²)均在0.88以上,平均相对误差分别为16.6%、16.6%、16.7%和16.2%;基于一阶导数光谱反射率与fAPAR的对数关系在(720±5) nm波段处的模拟效果较好,R²达0.86;直接选择反射率数据估算fAPAR的效果较差,R²最高为0.81.研究结果可为fAPAR的准确反演及评估作物干旱状况提供支撑.     

Remote sensing as a tool for assessing water quality in Loosdrecht lakes

The underwater light field in 7 lakes in the Loosdrecht lake area was measured in situ. Subsurface upwelling irradiance and irradiance reflectance, together with estimations of scattering and laboratory measurements of absorption by aquatic humus and particulate matter, enabled an analysis of the spectral signature of these waters. Aircraft imaging spectrometer measurements of upwelling radiance at 1 km altitude were used to simulate the PMI Chlorophyll #1, the CAESAR Inland Water Mode spectral bandsets and the Thematic Mapper bands 1 to 4. This made it possible to compare the effects of spectral band width and selection on the estimation of water quality parameters. Correlations increased to r > 0.94, at a significance level of 1% for the simulated C-IWM data with the 6 water quality parameters. Images of the PMI Chlorophyll #1 and of the TM were analysed and found to be in accordance with the statistical modelling results.A significant increase in correlation of remote sensing data with water quality parameters can be achieved through the selective use of 10 to 20 nm wide bands in the spectral range of 500 to 720 nm in these eutrophic waters. Sum of chlorophyll a and phaeopigments, seston dry weight, Secchi disc transparency, and coefficients for vertical attenuation of light, absorption and scattering can be estimated accurately. TM image data for water quality assessment is of limited use due to the relatively low spectral and radiometric resolution. However, the revisit capability and relatively low price per area are positive aspects of these satellite images.Abbreviations CAESAR = CCD Airborne Experimental Scanner for Applications in Remote sensing - C-IWM = CAESAR Inland Water Mode - CCD = charge coupled device - EOS-A = Earth Observation System Platform A - PAR = photosynthetically active radiation from 400–700 nm. - PMI = Programmable Multispectral Imager - RSLL = Remote Sensing Loosdrecht Lakes Project - SPOT = Systeme Pour l'Observation de la Terre - SPOT-HRV = Sensor on board of the SPOT satellite - TM = Thematic Mapper instrument aboard the Landsat 5 satellite