基于宽波段和窄波段植被指数的草地LAI反演对比研究

叶面积指数是一个重要的植被生理生态参数,为探讨不同植被指数反演叶面积指数的可行性,基于同空间分辨率不同光谱分辨率的HJ\|1B CCD1和Hyperion遥感影像数据,以内蒙古自治区赤峰市克斯克腾旗贡格尔草原为研究对象,选取几种常见宽波段植被指数和高光谱窄波段植被指数并结合4种常用回归模型,比较分析了不同植被指数反演叶面积指数的精度。结果表明:对于全部植被指数而言,PVI、MSAVI等综合考虑了土壤、环境等因素的植被指数较传统植被指数NDVI、RVI反演草地LAI精度更高。通过对比发现,在反演草地LAI方面,窄波段植被指数比宽波段植被指数表现出明显的优势。其中,窄波段垂直植被指数PVI验证模型的确定性系数R²为0.65,均方根误差RMSE为0.15,说明实测LAI和模拟LAI值之间具有较好的变化一致性。最后基于Hyperion影像和窄波段垂直植被指数PVI的估算模型生成研究区叶面积指数空间分布图。     

玉米叶面积指数与高光谱植被指数关系研究

探讨以不同的植被指数建立的高光谱模型对玉米叶面积指数LAI的反演精度。实测不同水肥耦合作用下，玉米冠层的高光谱反射率与叶面积指数（Leaf Area Index）数据，采用高光谱红光波段（631~760 nm）与近红外波段（760~1 074 nm）逐波段构建NDVI、RVI、DVI、TSAVI、PVI植被指数，分别找出与LAI具有最佳相关性波段组合的植被指数，建立玉米LAI估算模型。结果显示，与LAI具有佳相关性的波段组合分别是NDVI（R760，R990）、RVI（R760，R1001）、DVI（R677，R1070）、TSAVI（R 760，R 975）、PVI（R658，R966），它们反演玉米LAI的确定性系数分别：R²>0.72、R²>0.74、R²＝0.95、R²>0.79、R²>0.95。结果表明，在玉米的整个生长季的47个样本中，通过PVI和DVI方式建立的遥感估算模型能够较为准确地估算玉米LAI，TSAVI次之，NDVI、RVI稍差。     

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。     

基于MODIS数据的玉米植被参数估算方法的对比分析

基于实测数据建立了FPAR、LAI的植被指数估算模型（NDVI、RVI、NDWI）,并将其应用于MODIS BRDF数据对德惠地区玉米FPAR、LAI进行估算,然后将MODIS 15A2 FPAR/LAI产品值分别与BRDF估算值、地面实测值进行对比分析。主要得出以下结论：植被指数NDVI、RVI都能较好地用于实测数据和MODIS BRDF数据的FPAR、LAI估算;NDWI虽然在实测数据中估算玉米FPAR、LAI的效果优于NDVI、RVI,但其应用于MODIS BRDF数据估算FPAR、LAI时,效果却较差。BRDF数据估算FPAR与MODIS 15A2 FPAR值的关系因生长时期不同而异,在玉米生长前期,前者高于后者,而生长后期两者却较相近;BRDF估算LAI值一直都高于MODIS 15A2 LAI产品值。生长季前期,MOD15A2 FPAR、LAI值接近实测值,而在后期却高于实测值。通过分析也表明,玉米苗期MODIS 15A2 FPAR数值变化范围较小,产品算法对实际FPAR变化尚不够敏感,这可能是影响MODIS FPAR产品精度的一个原因。     

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。
     

结合光谱响应函数的Landsat-8影像大气校正研究

针对受大气吸收与散射影响,遥感器得到的测量值与目标物的真实值间存在误差,给反演地表反射率/反照率和地表温度等关键参数带来较大误差,影响图像分析精度的问题,该文利用Landsat-8的光谱响应函数,对OLI多光谱数据进行大气辐射校正和反射率反演,对校正前后的地物光谱曲线和归一化植被指数(Normalized Difference Vegtation Index,NDVI)的变化进行了对比。研究表明:OLI大气校正后较好地恢复各类地物光谱的典型特征;大气校正后NDVI增幅明显;类似的基于光谱响应函数的FLAASH大气校正方法可以为其他的高级陆地成像仪等传感器校正提供依据。     

MODIS光谱指数监测小麦长势变化研究

利用多个时次的小麦地面光谱与相应的生理参量测量数据(140组)，构建了常见宽波段植被指数形式的MODIS(中分辨率成像光谱仪)光谱指数。首先确定反映作物长势的因子为生物量和LAI(叶面积指数)，以地面光谱模拟MODIS的波段1～19，穷尽所有波段两两组合，寻找同时与生物量和LAI关系显著且有物理意义的光谱指数NDSI和RDSI。综合分析得出了3个最佳的波段组合：(619，62)，(619，617)和(619，616)，这3种组合所对应的NDSI和RDSI与两个长势因子都达到99％显著相关，而且明显优于Mc)DIS自身的植被指数产品MODIS—NDVI和MODIS_EVI。与NDSI相比，RDSI对LAI更敏感。MODIS_EVI比MODIS_NDVI有显著改进，它与长势因子的相关性可达到95％置信度。对MODIS图像的初步分析表明，NDSI(619，617)能够增强云与其他地物的差异，有可能改进云的识别精度。     

大豆叶面积的高光谱模型

以ASD FieldSpec-Vnir光谱仪实测不同生长季大豆的冠层反射率,同期采集对应大豆LAI,然后逐波段分析冠层光谱反射率、导数光谱与大豆LAI的相关关系;并采用单变量线性回归逐波段分析了冠层光谱反射率、导数光谱与大豆LAI确定性系数随波长的变化趋势,建立了以近红外与可见光波段冠层光谱反射率的比值植被指数RVI与大豆LAI的高光谱遥感估算模型。结果表明,冠层光谱反射率在350￣680nm、760￣1050nm波谱区与大豆LAI相关性较大,而在红边区680￣760nm的相关性变化较大;导数光谱在红边区与大豆LAI相关程度高。通RVI方式建立的遥感估算模型能较为准确估算大豆LAI,通过对红外与蓝波段建立的RVI指数与大豆LAI的回归模型,表明其预测大豆LAI的能力较好,有进一步研究的必要;通过对比发现,神经网络模型可以大大提升高光谱反演大豆LAI的水平,模型的确定系数R2为0.9661,而总均方根误差RMSE仅为0.446m2.m-2。     

基于植被指数季节变化曲线的年总初级生产力估算

针对年总初级生产力估算的研究,提出了一种参数简单、误差较小的估算方法。以"三北"防护林工程区域各类型植被为研究对象,获取2010年研究区全年时序的MODIS植被指数并构建植被指数季节变化曲线,建立该曲线积分ΣVIs与MODIS GPP产品的拟合关系,并研究各植被类型GPP估算适用的植被指数时间序列曲线积分ΣVIs。结果表明:①ΣVIs适用于估算研究区年总GPP并与MODIS GPP在p0.01置信水平下,显著相关;②ΣNDVI估算郁闭灌丛、稀疏灌丛、草地、耕地以及荒地或稀疏植被GPP的效果要优于ΣEVI和ΣEVI2,但在森林及其他植被类型方面要比ΣEVI或ΣEVI2的精度低;③由于NDVI在高LAI地区趋于饱和,使ΣNDVI估算高LAI植被类型GPP的误差较大,而利用ΣEVI和ΣEVI2估算高LAI植被类型的GPP具有较好的精度,并且EVI2相对于EVI减少了来自于蓝光波段的限制,能够更好地应用于长时间序列GPP研究。     

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。     

Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km × 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products.Results show very good performances of neural networks to estimate the original LAI products with an overall root mean square error (RMSE) around 0.5 for MODIS LAI from both MODIS and CYCLOPES normalized reflectances and a RMSE ranging between 0.12 (CYCLOPES reflectances) and 0.29 (MODIS reflectances) for CYCLOPES LAI. A drop of 15% of performance was found by training MODIS biome dependant algorithm by a single network over all the classes at the same time. More detailed analyses show that CYCLOPES and MODIS LAI values are very consistent for grasses and crops. Conversely, other biomes including shrubs, savanna, needleleaf and broadleaf forests show significant discrepancies, mainly due to differences between LAI definitions used between CYCLOPES (closer to effective LAI) and MODIS (closer to true LAI). However, products derived from the original CYCLOPES LAI products show a better agreement with both effective and true LAI ground measurements values. MODIS LAI products show more instability, partly because of the slightly shorter temporal resolution as compared to CYCLOPES.These results confirm the interest and versatility of neural networks for operational algorithms. This approach could be extended to other products or sensors, and may constitute a step forward for the fusion of data from several sensors, hence contributing to develop ‘virtual constellations’.     

Applicability of leaf area index products for boreal regions of Sweden

Leaf area index (LAI) of boreal ecosystems was estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study relationships between LAI and normalized difference vegetation index (NDVI), and to evaluate the applicability of the moderate resolution imaging spectroradiometer (MODIS) LAI product for small boreal regions. Relationships between optically-estimated LAI and NDVI were significant for different forest types in Laxemar and for Forsmark, effective LAI was correlated to the NDVI for all sites. NDVI-estimated LAI was used for evaluating accuracy of the MODIS LAI product and the comparison showed no correlation in Forsmark, whereas they were correlated in Laxemar. MODIS LAI was, on average, 2.28 higher than NDVI-based LAI, and it showed larger scatter. Scale issues were the main explanation for the high MODIS LAI, since heterogeneous landscapes with open areas were seen as forest in the large pixels of the MODIS LAI product.     

Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images

Due to the information gap between the VEGETATION sensors and Sentinel-3 mission, the Belgian state decided to build a small satellite, Project for Onboard Autonomy-Vegetation (PROBA-V), to ensure the continuity of the data record for vegetation studies. In this study, simulated PROBA-V data generated by the Landsat Thematic Mapper (TM) were used to evaluate the potential of this mission to assess winter wheat status. The root mean square error (RMSE) of PROBA-V's leaf area index (LAI), which was generated using the exponential method and the interpolation method, is 0.33 and 0.96 for March 2011 and 1.40 and 3.33 for May 2011, respectively. Système Pour l'Observation de la Terre (SPOT) VEGETATION's LAI does not show a significant relationship with the reference LAI values except for the LAI values during the stem elongation 100% phenological stage generated using the exponential method (correlation coefficient, r = 0.91; p = 0.01). For the tillering and stem elongation 100% phenological stages, linear regression models for the fraction of absorbed photosynthetically active radiation (FAPAR) with PROBA-V's normalized difference vegetation index (NDVI) were developed (coefficient of determination, R ², of 0.94 and 0.88). Exponential models for LAI (R ² of 0.91 and 0.93) and fresh weight of above-ground biomass (AGBf) (R ² of 0.90 and 0.93) with PROBA-V's near-infrared (NIR) and visible and near-infrared bands (VNIR B2) were developed accordingly. The assessment of winter wheat status showed that the highest and the lowest values of PROBA-V's simulated data (SD), i.e. NDVI, normalized difference water index (NDWI), and LAI of Field 2 and Field 4, correspond to the ground-measured biometric parameters.     

Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions

The objective of this work is to study the effect of changing the sensor view angle on spectral reflectance indices and their relationships with yield and other agronomic traits. Canopy reflectance spectra of 25 durum wheat genotypes were measured with a field spectroradiometer at two view angles, nadir and 30°, from anthesis to maturity in two years and two water regimes. Nine spectral reflectance indices were calculated from reflectance measurements for correlation with yield and several agronomic traits. At off-nadir position more reflected radiation was collected, associated with the reflective characteristics of stems. The performance of the indices predicting the yield and the agronomic traits varied as a function of sensor view angle, and were moreover affected by leaf area index (LAI) value. At high LAI, simple ratio (SR) and normalized difference vegetation index (NDVI), calculated at off-nadir position, were better predictors of traits related to the density of stems and poorer predictors of traits related to green area. On the other hand, at low LAI the indices normalized pigment chlorophyll index (NPCI) and water index (WI) were better predictors of yield and all the other traits when the sensor view angle was at nadir, whereas no differences due to sensor angle were accounted for the other three indices. The different performance of indices at low and high LAI is discussed.     

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780～680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R ² and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most optimum bands to discriminate the crops under three irrigation treatments.     

农田小麦状况的光谱特征分析

根据田间光谱观测提出了小麦光谱特征点(可见光、近红外、中红外)反射率域值和三个小麦生育光谱特征段，并利用统计分析法确定出不同农田小麦状况下光谱响应差异最明显时期、分辫小麦不同状况下的最佳光谱段。并以实际资料找出小麦反射光谱达饱合点时的LAI值以及小麦受灾后在光谱上的变化。     

Retrieval of the canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis

This paper reports on the use of linear spectral mixture analysis for the retrieval of canopy leaf area index (LAI) in three flux tower sites in the Boreal Ecosystem-Atmosphere Study (BOREAS) southern study area: Old Black Spruce, Old Jack Pine, and Young Jack Pine (SOBS, SOJP, and SYJP). The data used were obtained by the Compact Airborne Spectrographic Imager (CASI) with a spatial resolution of 2 m in the winter of 1994. The convex geometry method was used to select the endmembers: sunlit crown, sunlit snow, and shadow. Along transects for these flux tower sites, the fraction of sunlit snow was found to have a higher correlation with the field-measured canopy LAI than the fraction of sunlit crown or the fraction of shadow. An empirical equation was obtained to describe the relation between canopy LAI and the fraction of sunlit snow. There is a strong correlation between the estimated LAI and the field-measured LAI along transects (with R² values of 0.54, 0.71, and 0.60 obtained for the SOBS, SYJP, and SOJP sites, respectively). The estimated LAI for the whole tower site is consistent with that obtained by the inversion of a canopy model in our previous study where values of 0.94, 0.92, and 0.63 were obtained for R² for the SOBS, SYJP and SOJP sites, respectively.The CASI 2-m summer data over the SOBS site was also employed to investigate the possibility of deriving canopy LAI from the summer data using linear mixture analysis. At a spatial resolution of 10 m, the correlation between the field-measured LAI and the estimated LAI along transects is small at R² less than 0.3, while R² increases to 0.6 at a spatial resolution of 30 m. The difficulty in canopy LAI retrieval from the summer data at a spatial resolution of 10 m is likely due to the variation of the understory reflectance across the scene, although spatial misregistration of the CASI data used may also be a possible contributing factor.     

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。     

水稻冠层光谱特征及其与LAI的关系研究

氮素营养是影响作物生长与产量的最主要限制因子之一。准确及时地监测或诊断出作物氮素营养状况,对提高氮素利用效率和作物管理水平、减少过度施氮造成的环境污染具有重要意义。本研究在不同施氮水平处理的水稻试验小区,对水稻整个生长期内冠层反射光谱进行了较系统、密集的测定,同时测定了几个重要生育期水稻的叶面积指数。研究结果表明:随着施氮量的增加,水稻冠层光谱在各生育期间呈现出一定的规律性,在近红外部分(710～1 220 nm),冠层光谱反射率随着施氮水平的提高而升高,而在可见光部分(460～680 nm),水稻冠层的光谱反射率反而逐渐降低。经冠层光谱差异显著性检验发现:水稻灌浆期以前,对施氮水平最为敏感的波段是绿光(560～610 nm)和近红外(710～760 nm)部分;转换为归一化植被指数(NDVI)以后,差异最显著的是(R760-R560)/(R760+R560)。不同氮肥处理的水稻LAI随时间变化曲线大致都呈抛物线型,中低水平施氮肥水稻LAI随时间的变化曲线比较平缓,而高水平施氮肥LAI曲线则变化比较剧烈。冠层光谱反射与叶面积的相关分析结果表明:在水稻抽穗前,叶面积与冠层光谱反射率相关性较差;而抽穗后,叶面积与冠层光谱有较高的相关性。     

Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics

Leaf area index (LAI) is a key variable for the understanding of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors.This study proposed a method to estimate LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM). The CSDM described the temporal evolution of the LAI as function of the accumulated daily air temperature as measured from classical ground meteorological stations.The retrieval performances were evaluated for two different data sets: first, a data set simulated by the RTM but taking into account realistic measurement conditions and uncertainties resulting from different error sources; second, an experimental data set acquired over maize crops the Blue Earth City area (USA) in 1998. Results showed that the proposed approach improved significantly the retrieval performances for LAI mainly by smoothing the residual errors associated to each individual observation. In addition it provides a way to describe in a continuous manner the LAI time course from a limited number of observations during the growth cycle.