应用MODIS监测太湖水体叶绿素a浓度的研究

以太湖作为实验区,将MODIS影像不同空间分辨率的波段反射率与叶绿素a浓度实测值进行相关分析,在此基础上通过回归拟合建立遥感监测模型,并应用模型计算出太湖水体叶绿素a浓度的分布情况,对太湖水质进行了评价。研究结果表明,MODIS影像在太湖的水质监测中是可用的,其中250m分辨率波段1、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.903),适于用来反演叶绿素a浓度。     

松花湖水体叶绿素a含量与反射光谱特征关系初探

根据2008年7月在松花湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据,对松花湖水体反射光谱特征与叶绿素浓度之间的关系进行探讨与分析。研究结果表明:水体叶绿素浓度与各波长点处反射率相关性均较好,并选择700 nm处反射率建立单波段模型。而700 nm和677 nm波长处反射率比值、685 nm处光谱一阶微分、700 nm波长处波峰几何特征具有较好的相关性,给出了松花湖水体叶绿素浓度估算模型,为松花湖水体叶绿素浓度反演监测提供了一定的理论基础与参考。     

利用MERIS产品数据反演太湖叶绿素a浓度研究

第三代水色传感器MERIS的荧光通道的合理设置为荧光遥感法的应用提供了广阔的发展前景。利用MERIS数据、同步地面光谱和水质监测数据,分别通过基线荧光高度（FLH）、归一化荧光高度（NFH）和最大叶绿素指数（MCI）建立了太湖叶绿素a浓度的荧光遥感估算模型。结果表明：MERIS荧光参数中最大叶绿素指数（MCI）较基线荧光高度（FLH）更适合太湖水体叶绿素a浓度的反演;归一化荧光高度（NFH）与实测叶绿素a浓度间的拟合效果最好。最后选取NFH进行MERIS荧光遥感模型的太湖叶绿素a浓度的反演,其结果客观地反映了太湖水体叶绿素a浓度的空间分布格局。     

太湖水体叶绿素浓度与反射光谱特征关系的研究

根据2004年10月在太湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据,对太湖水体反射光谱特征与叶绿素浓度之间的关系进行探讨与分析。研究结果表明:水体叶绿素浓度与各波长处的反射光谱相关性不大,但是反射光谱经过比值法和微分处理后,两者具有很好的相关性,而且叶绿素浓度与反射光谱700nm附近波峰几何形态特征(波峰位置、面积、净高度)相关性很好,建立太湖水体叶绿素浓度估算模型。     

太湖水体时间序列叶绿素浓度与反射光谱特征分析

对2008年5月到2009年5月采集的太湖水体反射光谱数据进行了异常数据检测、归一化等预处理后,计算了常用于叶绿素浓度反演的特征参量,包括荧光峰高度、荧光面积、特征波段比值、反射率微分;并分析建立了这些特征参量与对应叶绿素浓度的相关模型。研究表明：荧光面积、特征波段比值等与实测叶绿素浓度具有较好的相关性,而蓝绿光波段反射率比值对内陆水体叶绿素浓度反映不明显。湖泊水体的光学特征能够较好反映蓝藻的不同生长状态,太湖蓝藻随时间变化的规律大致可分为5月～11月,12月～4月两个阶段。本研究结果可为湖泊水体富营养化高光谱遥感监测的波段选择提供参考。     

应用MODIS数据监测巢湖蓝藻水华的研究

以巢湖为研究区域,以MODIS 卫星影像为数据源,结合准同步的地面水质监测数据,将MODIS 250 m分辨率的波段反射率与叶绿素a浓度实测值进行相关分析。在此基础上通过回归拟合,构建基于中分辨率成像光谱仪(MODIS) 的叶绿素遥感提取模型。应用模型成功提取出蓝藻爆发水域chl-a的分布。从MODIS遥感图像上可以清晰地反映出巢湖这次蓝藻爆发的强度、地点和分布范围 。研究结果表明:用MODIS影像监测巢湖蓝藻水华是可行的,其中250m分辨率波段1 、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.909 3),适于反演叶绿素a浓度。     

长江口及邻近海域悬浮颗粒物对叶绿素a遥感反演算法的影响分析

利用水介质光辐射传输数值模型Hydrolight,结合前人对长江口及邻近海域水体的生物—光学模型研究,模拟不同光学水体的遥感反射率,并分析遥感反射率对悬浮颗粒物(SPM)的敏感性以及SPM对4种叶绿素a(Chla)反演算法(二波段、三波段、荧光基线高度(FLH)和综合叶绿素指数(SCI)算法)的影响。结果表明:由Hydrolight模拟得到的遥感反射率与现场同步实测的遥感反射率的均方根误差小于0.01sr-1,其中可实现遥感反射率在550~725nm波段较精确的模拟。遥感反射率对SPM的敏感性随着Chla浓度的升高而降低。二波段、三波段算法适合低SPM浓度水体的Chla浓度反演,FLH算法反演Chla浓度时易受SPM的影响,而SCI算法在中、高SPM浓度水体中消除SPM的影响进而反演Chla的潜力较好。     

春季太湖叶绿素a浓度的光谱估算与分析评价

2011年3月27日于太湖梅梁湾和湖心区域进行光谱数据采集,同步水质理化分析数据得到叶绿素a浓度区间为4.99μg/L～31.06μg/L。基于较低叶绿素a浓度水平的实测光谱数据及同步的理化分析数据分别采用二波段模型、光谱反射率一阶微分模型、反射峰位置模型、三波段模型和四波段模型对梅梁湾和湖心区域的叶绿素a浓度进行建模遥感估算。5个模型的回归分析结果对应R2分别为0.775,0.811,0.786,0.826和0.846,RMSE分别为4.02μg/L,3.52μg/L,3.82μg/L,3.44μg/L和3.24μg/L。并针对春季较低叶绿素a浓度水平下的光谱估算模型在应用价值和精度方面做了比较评价。     

新庙泡叶绿素a浓度高光谱定量模型研究

利用吉林省新庙泡的高光谱实测数据和水质采样分析数据,尝试通过单波段、波段比值、一阶微分和峰谷间距法建立叶绿素a反演模型。结果表明:单波段光谱反射率与叶绿素a浓度的相关性较差,不宜用于该区域的叶绿素a浓度估算;680 nm和700 nm波段反射率之比、700 nm处光谱一阶微分值和两波段峰谷间距反演模型都具有较高的决定系数,分别为0.783 4、0.792 7、0.796 9,验证模型的决定系数为0.651 3、0.431 7、0.756 4,均方根误差分别为8.69μg·L-1、14.50μg·L-1、10.04μg·L-1,显著水平P<0.01。这3种方法皆可以用于新庙泡叶绿素a浓度的定量遥感,其中又以峰谷间距法为最优。     

运用分段法反演太湖水体的叶绿素α浓度

为了提高太湖水体叶绿素口浓度的反演精度,本文采用了浓度分段法,将采样点按其浓度分成两类后分别建立统计模型,并在相关性较低的低浓度模型中采用了光谱修正因子OSS/TSS进行混合光谱分解.最后的验证结果显示,利用浓度分段模型估测叶绿素α浓度的均方根误差(RMSE)为21.12 μg/L,R2=0.92;而利用传统经验模型的估测精度为RMSE=35.72μg/L,R2=0.72.表明浓度分段法可以有效地提高内陆富营养化水体的叶绿素反演精度.     

基于四波段半分析算法和Hyperion遥感影像反演太湖叶绿素a浓度

水体叶绿素a浓度不仅是水质状况的重要指标,也是制定水环境保护和水资源开发利用方案的重要依据。以2004年8月19日太湖水质浓度实验数据和同步的Hyperion影像为数据基础,研究适用于Hyperion影像的四波段半分析算法。由模型参数标定数据集(37组)对四波段半分析算法参数的拟合分析和模型检验数据集(5组)对算法精度的评估可知,基于指数拟合方法获取的四波段半分析算法具有较高的叶绿素a浓度估算精度(相关系数为0.8913,平均绝对误差为1.1109μg/L,对应的平均相对误差为5.69%,其对应的4个波段波长分别为671.02nm、701.55nm、711.72nm和742.25nm)。用以上四波段半分析算法从Hyperion影像中提取的叶绿素a浓度呈湖心低、沿湖区域高的格局。与22.23 μg/L的年均叶绿素a浓度相比较,2004年8月19日的叶绿素a浓度处于年际较高水平。     

Numerical modelling of transspectral processes in natural waters: Implications for remote sensing

Based on a previously developed and thoroughly validated hydrooptical model, numerical simulations of the spectral composition of water leaving radiance are presented. These simulations take into account absorption, elastic scattering, water Raman (inelastic) scattering as well as the fluorescence of chlorophyll ( chl ) and dissolved organics ( doc ). The results obtained for forward modelling were also used for the inverse problem: retrieval of water quality parameters from water volume reflectance ( R ) spectra. The Levenberg-Marquardt multivariate optimization procedure was used for this purpose. Unlike water Raman scattering, the chl and doc fluorescence has an impact on R, so the retrieval results can change substantially for waters rich in chl or doc . Suspended minerals ( sm ) suppress both the chl and doc fluorescence influence on R . The retrieval results indicate that chl can be accurately assessed if the concentration of sm is not low and the doc concentration is < 2 mgCl -1 . For waters devoid of doc, the concentration of chl can be accurately retrieved even if the sm concentration is very low. Retrieval errors prove to be strongly dependent on the fluorescence yield value of both chl and doc .     

Feasibility of airborne imaging spectrometry for lake monitoring—a case study of spatial chlorophyll a distribution in two meso-eutrophic lakes

The spatial distribution of the sum of chlorophyll a and phaeophytin a concentrations (chl-a) under light wind (0–2 m s^?1) conditions was studied in two lakes with an AISA airborne imaging spectrometer. Chl-a was interpreted from AISA radiance data using an algorithm based on the near-infrared (700–710 nm) to red (660–665 nm) ratio. The results of Lake Lohjanjärvi demonstrate that the use of one monitoring station can result in over- or underestimation by 29–34% of the overall chl-a compared with an AISA-based estimation. In Lake Hiidenvesi, the AISA-based estimation for the mean chl-a with 95% confidence limits was 25.19±2.18 µg l^?1. The use of AISA data together with chl-a measured at 15 in situ sampling stations decreased the relative standard error of the mean chl-a estimation from 20.2% to 4.0% compared with the use of 15 discrete samples only. The relative standard error of the mean chl-a using concentrations at the three routine monitoring stations was 15.9 µg l^?1 (63.1%). The minimum and maximum chl-a in Lake Hiidenvesi were 2 and 101 µg l^?1, 6 and 70 µg l^?1 and 11 and 66 µmg l^?1, estimated using AISA data, data from 15 in situ stations and data from three routine in situ stations, respectively.     

Curve-fit analysis of chlorophyll fluorescence spectra: Application to nutrient stress detection in sunflower

The in vivo laser-induced chlorophyll fluorescence (LICF) spectra of healthy and nutrient-deficient sunflower plants were measured on a Jobin Yvon monochromator with He---Ne laser excitation. To correctly determine the peak center and to evaluate the relative contributions of the bands in the total fluorescence spectrum, the steady state LICF spectra were analyzed with a nonlinear iterative procedure using Gaussian, Lorentzian, Pearson, Voigt, and exponential Gaussian spectral functions. It was observed that curve fitting performed by using two Gaussian peaks centered at 690 and 730 nm usually fits well to the chlorophyll fluorescence spectra. After curve fitting, the mean peak centers of the red and far-red chlorophyll bands of control plants were observed at 688.2 and 725.4 nm, respectively. A blue shift of as much as 9 nm in the peak position of the far-red band was observed with nutrient stress, whereas the shift in position of the red band was only of the order of a few nanometers. Further, the width at half maximum of the far-red band was found to increase by as much as 20 nm with nutrient stress. Curve fitting could thus separate out the red and far-red fluorescence spectra from a pair of normally distributed curves centered at 690 and 730 nm wavelengths, thereby differentiating the effects due to reabsorption from those due directly to changes in photosynthetic electron transport. The F690/F730 fluorescence intensity ratio obtained from curve-fitted parameters was found to be more sensitive to plant stress than were fluorescence values alone. Results indicate that a curve-fitting analysis of LICF spectra using Gaussian spectral functions is a very useful and sensitive method of evaluating spectral features from a statistical point of view and for accurate determination of contributions from constituent bands in the whole leaf fluorescence spectrum.     

Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.