不同株型夏玉米群体冠层反射光谱特征及其应用研究

比较不同株型夏玉米在不同时期的反射光谱差异性,研究分析了红边位置(λred)、红边振幅(Dλred)、最小振幅(Dλmin)及Dλred/Dλmin与叶片全氮含量(LTN),叶绿素含量(Chl)及叶面积指数(LAI)间的相关性,并建立预测模型。结果表明,光谱差异随生育进程呈不同程度的规律性变化。在全生育期,用Dλred/Dλmin能更好地推算LTN,尤其在吐丝期,在开花前用Dλred也佳,在拔节期和喇叭口期用λred也有较高的精度。估算Chl时,在开花前用Dλred较可靠,在喇叭口期和抽雄期用λred也可考虑。估算LAI时,抽雄期后用Dλred推算有较高的可信度,在抽雄期用λred较好,在开花期和吐丝期用Dλred/Dλmin推算更为可靠。     

基于HJ星高光谱数据红边参数的冬小麦叶面积指数反演

针对我国HJ-1A星搭载的高光谱成像仪(HSI)数据,探索基于HJ星高光谱影像的LAI反演研究,本文利用inverted Gaussian模型提取红谷位置、红边位置、红边振幅以及红边斜率4个红边参数,结合2009年4月、5月两期同步地面观测LAI数据,经过回归分析构建了反演叶面积指数的最优红边参数模型.结果表明红边位置、红边斜率和红边振幅与叶面积指数都达到了极显著相关,R2分别为0.5592,0.7796和0.8107说明HJ星高光谱影像数据在叶面积指数反演方面有很大的应用潜力.     

水稻叶面积指数的多光谱遥感估算模型研究

LAI是生态系统研究中最重要的结构参数之一,它是估计多种植冠功能过程的重要参数。通过两年的水稻田间试验,使用美国ASD背挂式野外光谱辐射仪(ASDFieldSpec),获取1999～2000年两年晚稻整个生育期的光谱数据,采用计算机测算图斑面积法测定LAI;根据已有的卫星传感器通道波段(MSS、RBV、SPOT、TM、CH)和它们的组合(比值植被指数、归一化差植被指数),以及具有物理意义的光谱区域(蓝区、绿区、黄边、红光吸收谷、红边、紫区、可见光区、近红外区、全部波段)等共有27个变量构建多光谱变量组,采用5个单变量线性与非线性拟合模型,用1999年试验数据为训练样本,建立水稻LAI的多光谱遥感估算模型。结果表明:适用于水稻LAI估算的多光谱变量是植被指数变量好于波段变量;RVI与NDVI比较,RVI好于NDVI。用2000年试验数据作为测试样本数据,对其精度进行评价和验证,非线性模型的精度高于线性模型的精度,其中以SPOT3/SPOT2为变量的对数模型,拟合R2与预测R2达到了最大,其RMSE和相对误差(%)为最低,因此,认为它是估算LAI的最佳模型。
     

水稻LAI参数的Hyperion反演研究

利用实验区32块小区水稻冠层的实测光谱数据求取红边斜率(DRE)后,与实测的叶面积指数(LAI)建立统计关系模型,其复相关系数(R2)可达0.6859。在对江苏姜堰大田的Hyperion高光谱数据做了预处理、大气校正、混合像元处理等基础上求得DRE图,结合上述统计模型完成LAI反演,并利用大田LAI的实测数据进行验证,验证结果表明实测LAI与反演LAI的线性相关系数为0.84784,因此通过此模型进行LAI反演是切实可靠的。     

基于高光谱植被指数的加工番茄生长状况监测研究

利用ASD地物非成像高光谱仪,获取2个加工番茄品种4水平施氮量和3种配置种植方式6个关键生育时期冠层的反射光谱数据,通过计算得到归一化植被指数(NDVI)、比值植被指数(RVI)、修改型二次土壤调节植被指数(MSAVI2)和红边归一化植被指数(RENDVI),并分别与其冠层叶绿素密度(CH.D)、叶面积指数(LAI)、地上鲜生物量(AFBM)和地上干生物量(ADBM)进行相关分析,经检验,相关系数均达到1%的极显著水平。其中RENDVI与CH.D的线性相关模型,RVI与LAI的幂指数函数模型的相关性最好(RRENDVI-CH.D=0.8034**,RRVI-LAI=0.8703**,n=54,α=1%),用上述2个相关模型方程分别估算加工番茄CH.D和LAI,实测值与估测值之间均呈极显著的线性相关关系(R实测CH.D-估测CH.D=0.8113**,R实测LAI-估测LAI=0.8546**,n=54,α=1%),估算精度分别为85.5%和86.3%。试验结果表明,用高光谱植被指数,可以对加工番茄冠层CH.D、LAI、AFBM和ADBM进行遥感估算,实现对加工番茄生长状况的实时、无损、非接触和定量的高光谱监测研究。     

杨树林叶面积稳定期的叶面积指数遥感估算方法

针对用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)估算植被叶面积指数(Leaf Area Index,LAI)不仅需要大量地面LAI观测及其数据统计,且在植被NDVI饱和时难以估算LAI等问题,提出了一种基于数据挖掘技术的LAI遥感估算方法。该方法借助数据挖掘技术从有限的数据中挖掘和发现有用的信息,排除人为干扰,提高模型构建效率和精度。文中以安徽滁州地区杨树林为研究对象,获取研究区杨树林展叶期和花果期的HJ-CDD遥感影像,利用LAI-2000同步测量杨树林LAI;借助数据挖掘技术并基于杨树林展叶期和花果期估算的LAI值,通过筛选优化构建了杨树林生长过程中叶面积稳定期的LAI估算模型,并结合叶面积稳定期实测的LAI值验证表明该模型用于杨树林叶面积稳定期LAI估算的可靠性,为植被NDVI饱和时的LAI遥感估算提供了一种有效的思路和方法。     

花生叶面积指数与特征导数光谱的相关性

运用导数光谱分析技术,研究了不同氮肥水平下不同品种花生的叶面积指数(Leaf Area Index,LAI)与冠层导数光谱及其衍生参数的定量关系。结果表明,花生导数光谱在红边区域680~750 nm范围内与叶面积指数的相关关系比较稳定,在680~710 nm范围内呈正相关,在710~750 nm范围内呈负相关,685 nm和735 nm波段相关程度达到最大。在三边参数中,振幅参数优于面积参数优于位置参数,且仅有红边的面积、振幅、位置参数与花生LAI的相关性最好,相关系数分别为-0.9345、-0.9869和0.7632。在系列衍生参数中,RDr.b、RDr.y、NDDr.y与LAI呈极显著正相关关系外,其它衍生参数均与LAI呈极显著负相关关系,红蓝边面积差DSDr.b、红黄边面积差DSDr.y、红黄边振幅差DDr.y与LAI的相关系数分别为-0.9690、-0.9485、-0.9764,相关程度均较高。因此,研究认为,可以利用685 nm和735 nm两波段的一阶导数光谱、红边面积、红边振幅、红蓝边面积差、红黄边面积差、红黄边振幅差等来监测花生的叶面积指数。      

新疆棉花LAI和叶绿素密度的高光谱估算研究

利用非成像高光谱仪,对棉花(2品种4水平种植密度)冠层5个关键生育时期进行光谱测定,分析棉花反射光谱及微分光谱生育期的变化规律,并对棉花冠层叶面积指数(LAI)、叶绿素密度(CH.D)与光谱数据进行回归分析,结果表明,用归一化差值植被指数(NDVI)与LAI建立的对数模型能够较好地估测棉花冠层的LAI(r=0.9123**,n=20);近红外729 nm波段处一阶微分光谱数值与CH.D高度相关(r=0.9372**,n=20),用此波段建立的CH.D估算模型,精度达84.3%,标准差为0.234g.m-2,RMSE=0.1569。研究表明,可以用高光谱数据对新疆棉花冠层LAI和CH.D进行遥感估算。     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

东北主要绿化树种叶面积指数(LAI)高光谱估算模型研究

以东北主要绿化树种为研究对象,分别在长春市南湖公园和长春公园获取了共240组树冠高光谱反射率及相应的LAI数据。对数据进行相关分析,以确定反演LAI的敏感波段,而后分别运用6种植被指数、神经网络以及小波分析等3种方法进行估算。研究结果表明,3种方法估算树冠LAI都取得了较好的效果:①与RVI、NDVI相比,由DVI、RDVI、MSAVI、TVI等植被指数建立的估算模型可以提高LAI的估算精度;②神经网络在拟合光谱反射率与树冠LAI关系时明显优于植被指数法(R2达0.850);③小波能量系数与LAI相关性较好,单变量回归分析R2可达0.683,部分小波能量系数估算LAI的精度优于植被指数法,并且验证R2也较高,说明其稳定性较好,多元变量回归分析能够实现各小波能量系数间的优势互补,R2可达0.794。     

Improving leaf area index retrieval using spectral characteristic parameters and data splitting

ABSTRACT

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented ‘blue shifts’, the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R²) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis.     

Study of crop growth parameters using Airborne Imaging Spectrometer data

High-spectral-resolution data of the Airborne Imaging Spectrometer (AIS) developed by the Indian Space Research Organisation were analysed to check their potential for extraction of information concerning crop growth parameters. Reflectance spectra of wheat plots at different growth stages were generated to study the position of red edge. The shift of inflection wavelength towards longer wavelength was observed with increase in Leaf Area Index (LAI) and chlorophyll content. For the range of LAI from 0.08 to 3.16 and chlorophyll content from 2 to 39 (gm^-2), the shift of inflection wavelength was observed in the range of 713-723 nm. A linear relationship was observed between the inflection wavelength and LAI as well as chlorophyll content.     

基于BP神经网络的夏玉米多生育期叶面积指数反演研究

叶面积指数（Leaf Area Index,LAI）是生物地球化学循环中重要的植被结构参数。针对目前基于我国GF-1 WFV卫星影像的夏玉米多生育期LAI反演研究较少的问题,基于不同隐含层构建BP神经网络模型（BP1模型和BP2模型）,对比分析BP1模型、BP2模型和6种统计模型（NDVI、RVI、DVI、EVI、SAVI、ARVI）反演之间的精度差异,并根据实测数据绘制BP1模型和BP2模型的夏玉米多生育期LAI动态变化图。结果表明:LAI与6种常用的统计模型均有良好相关性,其中NDVI指数方程式回归模型拟合度最优;BP神经网络模型整体R ²略小于统计模型,而RMSE则小于统计模型,取得了与实测值差异更小的结果,统计模型与BP神经网络模型各有优劣之处;BP2模型在R ²和RMSE均优于BP1模型,能获得更为精确的反演值,BP2整体预测精度更高;基于BP神经网络模拟夏玉米生育期反演,LAI值呈现缓慢升高—快速增长—逐渐减小的S型变化过程,基本符合作物生长规律。该研究结合不同隐含层建立的BP神经网络模型,为GF-1卫星在作物叶面积指数多生育期反演的应用推广提供了方法支撑。     

Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position

The aim of this study was to compare the performance of various narrowband vegetation indices in estimating Leaf Area Index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study uses a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrowband vegetation indices involved all possible two band combinations which were used for calculating RVI, NDVI, PVI, TSAVI and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using any of the three methods was generally not sensitive to variations in LAI (R ² < 0.1). However, LAI was estimated with reasonable accuracy from red/near-infrared based narrowband indices. We observed a significant relationship between LAI and SAVI2 (R ² = 0.77, RMSE = 0.59 (cross validated)). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3 ≤ LAI ≤ 6.1), linear relationships exist between LAI and the selected narrowband indices.     

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

Biophysical parameters such as leaf area index (LAI) are key variables for vegetation monitoring and particularly important for modelling energy and matter fluxes in the biosphere. Therefore LAI has been derived from remote sensing data operationally based on data with a somewhat coarse spatial resolution. This study aims at deriving high-spatial resolution (6.5 m) multi-temporal LAI for grasslands based on RapidEye data by statistical regressions between vegetation indices (VIs) and field samplings. However, the suitability of those data for grassland LAI derivation has not been tested to date. Thus, the potential of RapidEye data in general and its red edge band in particular are investigated, as well as the robustness of the established relationships for different points in time.

LAI was measured repeatedly over summer 2011 at about 30 different meadows in the Bavarian alpine upland using the LAI-2000 and correlated with VI values. The best relationships resulted from using the ratio vegetation index and red edge indices (NDVI_rededge, rededge ratio index 1, and relative length) in non-linear models. Thus the indices based on the red edge channel improved regression modelling. The associated transfer functions achieved R² values ranging from 0.57 to 0.85. The temporal transferability of those transfer functions to other dates was shown to be limited, with the root mean square errors (RMSEs) of several scenes exceeding one. However, when the LAI ranges are similar, a reliable transfer is possible: for example, the transfer of the regression function based on early autumn measurements showed RMSEs of only 0.77–0.95 for the other scenes except for the high-density stage in July, when the LAI reaches unprecedented maximal values. Also, the combination of multi-temporal training data shows no saturation of the selected indices and enables a satisfactory LAI mapping of different dates (RMSE = 0.59 – 1.02).     

不同生育期倒伏胁迫下玉米叶面积指数高光谱响应解析

为研究倒伏胁迫下不同生育期LAI高光谱响应模型,提高LAI高光谱响应模型精度,获取不同生育期倒伏玉米LAI与冠层光谱反射率,采用6种传统变换方式对高光谱反射率进行处理,构建不同生育期倒伏玉米LAI分期与统一响应模型。研究结果表明：LAI能够直接反映玉米受倒伏胁迫程度及自身恢复能力;传统光谱变换有利于提高光谱同LAI的敏感性及模型响应精度;不同生育期倒伏玉米LAI分期响应模型优于统一响应模型。该结果可有效诊断倒伏胁迫下的玉米叶面积指数,为实现不同生育期倒伏玉米长势精确监测提供理论依据和技术支撑,对玉米倒伏胁迫灾情监测可提供必要的先验知识。     

The Red Edge Parameters Diversification Disciplinarian and Their Application in Winter Wheat

Some red edge parameters( λ red, Min λ 6oo-72o, d λ,red, d λ min, d λ red / d λ min, ∑ d λ 680-750, and λ nir) and the relationship between these parameters and the parameters of biochemistry and biophysics of winter wheat were studied by regression analysis. The results indicated that there existed some changes in these red edge parameters in the whole growth stages,and there were strong correlations between red edge parameters and pramters of biochemistry and biophysics. Thus, the red edge parameters were found valuable for assessment of wheat parameters of biochemistry and biophysics. The λ red can be used to estimate the soluble sugar content and the chlorophyll content. The d λ red was the best estimator of total nitrogen content. LAI can be estimated by Min λ 600-720 satisfactorily.     

Estimating winter wheat plant water content using red edge parameters

Remote sensing of plant water content is difficult because the absorption band sensitive to foliar liquid water is also sensitive to the atmospheric vapour. A method using non-water-absorption spectral parameters to evaluate plant water content (PWC) would be valuable. In our experiment, canopy spectra of 48 winter wheat treatments with different varieties, different fertilization and irrigation levels were measured by an ASD FieldSpec FR spectrometer in six different growth stages from erecting stage to milking stage, and the PWCs of the related wheat plant samples were also measured. Significant positive coefficients of correlation were observed between PWC and spectral reflectance in 740–930?nm region in all of the six different growth stages, which indicates that the NIR spectral reflectance increases due to the effect of PWC on the leaf internal structure. This mechanism also affects the red edge spectrum in 680–740?nm region. The spectral reflectance increases more rapidly and the red edge becomes steeper if PWC is higher. The coefficients of correlation between PWC and red edge width, derived from the inverted-Gaussian model, are significant at the 0·999 confidence-level, which is more reliable than WI and NDWI, and the statistical models for PWC based on red edge width were set up in all the six different growth stages. In addition, LAI and canopy chlorophyll density (CCD) are also related to red edge parameter, such as red edge position and red edge width. It seems that PWC plays a more important role in red edge width than LAI and CCD due to the effect of PWC on the leaf internal structure, and that CCD plays a more important role in red edge position than LAI and PWC.     

The red edge position and shape as indicators of plant chlorophyll content,biomass and hydric status.

Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were studied to evaluate plant chlorophyll content, biomassand RelativeWater Content (RWC).Plants of Capsicum annuum and Phaseolus vulgaris under different nitrogen and water availabilities, and plants of Gerbera jamesonii with different hydric status were studied. A high correlation was found between chlorophyll content and the wavelength of the red edge peak (λ_re ), and between LAI (leaf area index)and the amplitude of the red edge peak (dr_{r e} ), but the area of the red edge peak (σ_680–780 nm) was the best estimator of LAI. Thus, red edge was found valuable for assessment of plant chlorophyll concentration and LAI, and therefore nutritional status. Water stress also affected dr_re, but only when the stress was well developed.     

Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

Leaf Area Index (LAI) is an important vegetation structure parameter in biogeochemical cycling. In view of the lack of LAI inversion in the multiple growth period of summer maize based on GF-1 WFV satellite images in China, this study constructs a BP neural network model (BP1 model and BP2 model) based on different hidden layers, and compares and analyzes the accuracy of the inversion between the BP1 model, BP2 model and 6 statistical models (NDVI、RVI、DVI、EVI、SAVI、ARVI). Based on the measured data, BP1 model and BP2 model are used to map the LAI dynamic changes of summer maize. The results show that LAI has good correlation with 6 common statistical models, and the fitting degree of the NDVI exponential equation regression model is the best. The overallR ² of BP neural network model is slightly smaller than the statistical model, while RMSE is less than the statistical model, and the errors with the measured value is smaller than the statistical model. So both the statistical model and the BP neural network model have advantages and disadvantages. The BP2 model is superior to the BP1 model inR ² and RMSE, and can obtain more accurate inversion values, and the overall prediction accuracy of BP2 is higher. Based on the BP neural network simulation of summer maize growth period inversion, the LAI value presents a slow increase to the gradual decrease of S type change process, which is basically in line with the crop growth rules. The study combines with the BP neural network model established by different hidden layers to provide a method for the application of GF-1 satellite in the application of crop leaf area index multiple growth period inversion.