基于PROSAIL辐射传输模型的毛竹林分冠层反射率模拟研究

冠层光谱反射率直接关系到毛竹（Phyllostachys pubescens Mazel）林冠层参数的反演,对毛竹林地土壤肥力间接估测具有重要意义。以PROSPECT5、PROSAIL模型为基础,从叶片尺度和冠层尺度分析模型参数对叶片和冠层反射率的影响,构建毛竹冠层叶面积指数（LAI）-冠层反射率查找表并通过代价函数选取最优冠层反射率,从而实现毛竹林分冠层反射率的准确模拟。结果表明,在叶片尺度,PROSPECT模型参数敏感性从高到低依次为叶肉结构参数（N） > 叶绿素含量（C_ab） > 等效水厚度（EWT） > 干物质含量（C_m） > 类胡萝卜素含量（C_ar）;在冠层尺度,PROSAIL模型参数敏感性从高到低依次为LAI > C_ab > EWT > C_m > N > C_ar > ALA（平均叶倾角）;叶片尺度反射率整体大于冠层尺度反射率;在400~900 nm波长范围内,PROSAIL模型模拟的冠层光谱反射率与实测光谱反射率拟合效果较好,相对误差为6.71%。     

基于PROSAIL辐射传输模型的毛竹林叶面积旨数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI)-冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明:PROSAIL模型各输入参数的敏感性由高到低依次为LAI＞叶绿素含量(Cab)＞叶片结构参数(N)＞平均叶倾角(ALA)＞等效水厚度(Cw)＞干物质含量(Cm),并以LAI、Cab两个主要敏感因子用于构建毛竹林LAI-冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0％,但也存在反演LAI平均值高于实际值的问题.     

基于PROSAIL模型的水稻田缨帽三角-叶面积指数模型及其应用

缨帽三角(tasseled cap triangle,TCT)-叶面积指数(leaf area index,LAI)等值线模型是一种反映植被叶面积指数等值线在红光(Red)-近红外(NIR)波段反射率组成的光谱空间中分布规律的模型,在此基础上建立LAI遥感反演模型比常用的统计关系模型更加精确.本文利用水稻田实测数据,验证了PROSAIL模型对水稻冠层反射率模拟的适用性,并对模型的输入参数进行率定,最终确定了PROSAIL模型模拟水稻冠层反射率的输入参数的取值范围.在此基础上构建了水稻田TCT-LAI等值线模型,建立了LAI遥感反演所需的查找表,将其分别用于Landsat 8和WorldView 3数据进行水稻田LAI反演.结果表明: 利用基于TCT-LAI等值线模型建立查找表反演的LAI与实测LAI具有良好的线性相关关系,R²=0.76,RMSE=0.47;与Landsat 8的LAI反演结果相比,WorldView 3反演的LAI值域范围更大,数据分布更离散.将Landsat 8、WorldView 3反射率数据重采样至1 km后进行LAI反演, MODIS LAI 产品的反演结果存在明显低估现象.     

华南地区亚热带树木叶面积指数的高光谱反演研究

为构建树种叶面积指数的估算模型,以NDVI、RVI、FREP、CIGreen、CIRed-edge、MSAVI2为高光谱特征变量,通过统计分析,确定反演树种叶面积指数的最佳光谱特征变量,构建华南农业大学校园内50种亚热带树木的叶片反射率和叶面积指数(LAI)模型。结果表明,6种高光谱特征变量与树种叶面积指数间都具有极显著相关性,其中红边位置反射率(FREP)和比值植被指数(RVI)与LAI的拟合方程的R2都大于0.8,决定系数分别为0.820和0.811。经过精度验证,FREP估算的均方根误差(RMSE)只有0.13,该回归模型为估测亚热带典型树种的叶片LAI最佳模型。从高光谱遥感的角度结合亚热带植被的群落结构特点来看,建立的红边位置光谱反射率与叶面积指数的回归模型普遍具有较高的拟合度,所以利用高光谱特征变量反演亚热带树木叶片的叶面积指数等植被参数的应用前景较好。     

基于小波分析的大豆叶面积高光谱反演

实测了不同水肥耦合、经营制度及有效营养面积条件下的大豆(Glycinemax)冠层高光谱反射率与叶面积指数(LAI),并对光谱反射率、微分光谱与LAI的关系进行了分析;采用比值植被指数(RVI)与归一化植被指数(NDVI)建立了大豆LAI反演模型;采用小波分析对采集的光谱反射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆LAI进行估算。结果表明:大豆LAI与光谱反射率在可见光波段呈负相关;在近红外波段呈正相关;微分光谱在红边处与大豆LAI密切相关(R2=0.92);RVI与NDVI可以提高大豆LAI的估算精度(R2分别达0.79、0.84);各植被指数各有优缺点,应根据需要进行选择;小波能量系数回归模型可以进一步提高大豆叶面积的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆LAI回归确定系数R2高达0.884;以4个和6个小波能量系数建立LAI回归分析模型(R2分别达0.92、0.93),2个模型LAI预测值与大豆LAI实测值线性回归确定性系数R2分别为0.90、0.92。比较可知,小波分析可以对高光谱进行特征变量提取,进而反演大豆生理参数,并且反演的LAI精度较光谱反射率、微分光谱及植被指数都有明显提高,小波分析在植被生理参数的高光谱提取方面有着广阔的应用前景。     

利用PROSPECT+SAIL模型反演植物生化参数的植被指数优化模拟

分析3个植被生化参数(叶绿素含量、叶片含水量和叶面积指数)对冠层光谱反射率变化的敏感程度以及影响波段区间,选择3个植被指数作为代价函数的优化比较对象,然后运用微粒群算法和PROSPECT+SAIL模型分别反演叶绿素含量、叶片含水量和叶面积指数,结果表明:基于植被指数作为优化比较对象的模型反演效率较全波段方法有所提高;叶绿素含量、叶片含水量和叶面积指数反演值与实测值的复相关系数分别为90.8％、95.7％和99.7％,均方根误差分别为4.73μg·cm-2、0.001 g·cm-2和0.08.采用植被指数作为优化比较对象可有效地提高基于PROSPECT+SAIL模型反演植被生化参数的精度和效率.     

基于PROSAIL辐射传输模型的毛竹林叶面积指数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI) 冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明： PROSAIL模型各输入参数的敏感性由高到低依次为LAI>叶绿素含量(C_ab)>叶片结构参数(N)>平均叶倾角(ALA)>等效水厚度(C_w)>干物质含量(C_m),并以LAI、C_ab两个主要敏感因子用于构建毛竹林LAI 冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0%,但也存在反演LAI平均值高于实际值的问题.     

森林冠层叶面积指数遥感反演——以小兴安岭五营林区为例

以中国东北小兴安岭五营林区为研究区,基于MODIS BRDF遥感模型参数产品数据,首先利用4-Scale模型建立查找表计算像元尺度上各组分比例,估算研究区森林乔木冠层反射率,然后利用冠层反射率数据,获取研究区3种常用森林冠层植被指数,最后基于植被指数与实测叶面积指数构建研究区冠层叶面积指数反演模型,并选取最优模型实现研究区森林冠层叶面积指数反演。结果表明:研究区冠层LAI遥感反演模型中,基于比值植被指数SR(simple ratio,SR)构建的二次多项式反演模型精度最高,且反演精度比未考虑背景反射影响的SR反演模型精度有较大幅度提高,模型决定系数由0.38提高至0.54;反演获取的研究区冠层LAI在2.38~12.67,平均值6.52,LAI值在阔叶林区域相对较高。     

陇中黄土高原春小麦光谱反射特征

通过田间小区试验,测定了3个春小麦品种(高原602、陇春8139和定西24)在不同生育期和不同种植密度的光谱反射率及对应叶面积指数(LAI)和地上生物量,分析了其光谱反射的一般特征和红边参数特征以及光谱变量与LAI和地上生物量的相关性.结果表明:在整个波段内,春小麦冠层光谱表现为高原602＞陇春8139＞定西24,其叶片光谱表现为定西24＞陇春8139＞高原602;春小麦冠层光谱在可见光波段和中红外波段成熟期明显大于孕穗期,而叶片光谱在近红外波段孕穗期明显大于成熟期;随着种植密度的提高,在近红外波段冠层和叶片的光谱反射率逐渐增加;冠层光谱的红边均具有"双峰"现象,从孕穗期到成熟期,冠层红边位置呈现"蓝移"现象;LAI和地上生物量与冠层光谱变量之间存在较好的相关性.     

小麦叶面积指数与冠层反射光谱的定量关系

在分析不同氮素水平下小麦叶面积指数（LAI）和冠层光谱反射率随生育期变化模式的基础上,确立了LAI与冠层光谱反射率及光谱参数的相关关系,提出了小麦LAI的敏感光谱参数及预测方程.结果表明,小麦LAI和近红外短波段（760～1 220 nm）反射率都随施氮量的增加呈上升趋势,可见光波段反射率则相反;从拔节期到成熟期,LAI和近红外短波段反射率均表现为先上升后下降的趋势,而可见光波段（460～710 nm）反射率随生育期的推进先降低后升高,以孕穗期反射率最低,近红外长波段区域（1 480～1 650 nm）反射率的变化与可见光部分相同.LAI与可见光波段反射率呈负相关,与近红外短波段反射率呈极显著正相关,其中以810 nm相关性最好.可以选择RVI(810,510)和DVI(810,560)作为反演小麦LAI的光谱参数.另外,在证明垂直植被指数PVI和转换型土壤调整指数TSAVI对LAI预测能力的同时,发现利用RVI(810,510)、DVI(810,560)和PVI 3个植被指数共同推算小麦LAI的准确度更高.     

A comparison of spectral reflectance indices in response to water: A case study of Quercus aliena var. acuteserrata

Aims Using leaf spectral reflectance to detect plant status in real time and non-destructively is a new method of forest drought assessment, but each spectral index possesses considerable moisture sensitivity. Therefore, determining moisture index applicable to tree leaf and its sensitive spectral index are both very important. Methods This study selected Quercus aliena var. acuteserrata leaves in different growth stages and canopy positions as the research object, and measured leaf moisture index and its synchronous reflectance spectral response curve during the dehydration process, explored the relationship between changes of leaf spectral reflectance and water status, compared and evaluated the advantages and disadvantages of correlation between the moisture indices of leaves in different growth stages and space positions and different spectral reflectance indices. Important findings Results indicated: (1) The variability of relative water content (RWC) and equivalent water thickness (EWT) in different growth stages and canopy positions was smaller than specific leaf water content (SWC) and leaf moisture percentage on fresh quality (LMP) as measured by the four different moisture indices. RWC and EWT could steadily characterize the holistic water status of trees, and they had greater spectral sensitivity. Therefore, they were suitable for application in remote sensing detection. (2) Spectral reflectance difference analysis and spectral reflectance sensitivity analysis showed that the leaf spectral sensitivity is strongly influenced by growth stage. In short wave infrared region, spectral reflectance of mature leaves changed slightly in the initial stage of dehydration stress, but new expended leaves showed obvious spectral differences during the dehydration process. (3) Through the correlation analysis between 15 different spectral indices and moisture indices, we found that water index (WI)-RWC and double difference index (DD_{n (1530,525)})-EWT has higher correlation. The fitted relations of WI-RWC are greatly influenced by leaf growth stage and canopy position, while those of DD_n(1530,525)- EWT are more stable.     

Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements

Vegetation water content (VWC) is an important variable for both agriculture and forest fire management. Remote sensing technology offers an instantaneous and non-destructive method for VWC assessment provided we can relate in situ measurements of VWC to spectral reflectance in a reliable way. In this paper, based on radiative transfer models, three new normalized difference water indices (NDWI) are proposed for VWC [fuel moisture content (FMC), and equivalent water thickness (EWT)] estimation, taking both leaf internal structure and dry matter content into account. Reflectance at 1,200, 1,450 and 1,940 nm were selected and normalized with reflectance at 860 nm to establish three water indices, NDWI₁₂₀₀, NDWI₁₄₅₀ and NDWI₁₉₄₀. Good correlations were observed between FMC (R ² = 0.65–0.80) and EWT (both at the leaf scale, R ² = 0.75–0.81 for EWT_L and at the canopy scale, R ² = 0.80–0.83 for EWT_C) at various stages of wheat crop development.     

高光谱植被指数与水稻叶面积指数的定量关系

基于不同水稻品种、施氮水平和不同生育期下的大田试验,确立了水稻叶面积指数（LAI）与冠层光谱特征参数的定量关系.结果表明：水稻叶面积指数与部分高光谱植被指数存在良好的相关性,其中原始光谱组成的2波段差值指数（DI）形式相关性最好,其次为比值（RI）和归一化（NI）植被指数.相关最好的原始光谱植被指数是由近红外波段组成的差值指数DI(854,760),相关最好的一阶导数光谱植被指数是红光和近红外光组成的导数差值指数DI(D₆₇₆, D₇₇₈),但总体上导数光谱指数不如原始光谱指数与LAI关系密切.独立试验数据检验结果表明,以差值指数DI(854,760)为变量建立的水稻LAI监测模型具有较好的表现,可用于水稻LAI的估测.     

Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales

We report a multiscale study in the Wind River Valley in southwestern Washington, where we quantified leaf to stand scale variation in spectral reflectance for dominant species. Four remotely sensed structural measures, the normalized difference vegetation index (NDVI), cover fractions from spectral mixture analysis (SMA), equivalent water thickness (EWT), and albedo were investigated using Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Discrimination of plant species varied with wavelength and scale, with deciduous species showing greater separability than conifers. Contrary to expectations, plant species were most distinct at the branch scale and least distinct at the stand scale. At the stand scale, broadleaf and conifer species were spectrally distinct, as were most conifer age classes. Intermediate separability occurred at the leaf scale. Reflectance decreased from leaf to stand scales except in the broadleaf species, which peaked in near-infrared reflectance at the branch scale. Important biochemical signatures became more pronounced spectrally progressing from leaf to stand scales. Recent regenerated clear-cuts (less than 10 years old) had the highest albedo and nonphotosynthetic vegetation (NPV). After 50 years, the stands showed significant decreases in albedo, NPV, and EWT and increases in shade. Albedo was lowest in old-growth forests. Peak EWT, a proxy measure for leaf area index (LAI), was observed in 11- to 30-year-old stands. When compared to LAI, EWT and NDVI showed exponentially decreasing, but distinctly different, relationships with increasing LAI. This difference is biologically important: at 95% of the maximum predicted NDVI and EWT, LAI was 5.17 and 9.08, respectively. Although these results confirm the stand structural variation expected with forest succession, remote-sensing images also provide a spatial context and establish a basis to evaluate variance within and between age classes. Landscape heterogeneity can thus be characterized over large areas—a critical and important step in scaling fluxes from stand-based towers to larger scales.     

不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系

研究了不同土壤水氮条件下水稻 (Oryzasativa) 冠层光谱反射特征和植株水分状况的量化关系。结果表明, 水稻冠层近红外光谱反射率随土壤含水量的降低而降低, 短波红外光谱反射率随土壤含水量的降低而升高。相同土壤水分条件下, 高氮水稻的冠层含水率高于低氮水稻的冠层含水率 ;同一水分条件下, 高氮处理的可见光区和短波红外波段光谱反射率低于低氮处理, 近红外波段光谱反射率高于低氮处理。发现拔节后比值植被指数 (R810 /R460 ) 与水稻叶片含水率和植株含水率呈极显著的线性相关, 模型的检验误差 (RootmeansquareError, RMSE) 分别为 0.93和 1.5 0。表明比值植被指数R810 /R460 可以较好地监测不同生育期水稻叶片和植株含水率。     

A Novel Moisture Adjusted Vegetation Index (MAVI) to Reduce Background Reflectance and Topographical Effects on LAI Retrieval

A new moisture adjusted vegetation index (MAVI) is proposed using the red, near infrared, and shortwave infrared (SWIR) reflectance in band-ratio form in this paper. The effectiveness of MAVI in retrieving leaf area index (LAI) is investigated using Landsat-5 data and field LAI measurements in two forest and two grassland areas. The ability of MAVI to retrieve forest LAI under different background conditions is further evaluated using canopy reflectance of Jack Pine and Black Spruce forests simulated by the 4-Scale model. Compared with several commonly used two-band vegetation index, such as normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, optimized soil adjusted vegetation index, MAVI is a better predictor of LAI, on average, which can explain 70% of variations of LAI in the four study areas. Similar to other SWIR-related three-band vegetation index, such as modified normalized difference vegetation index (MNDVI) and reduced simple ratio (RSR), MAVI is able to reduce the background reflectance effects on forest canopy LAI retrieval. MAVI is more suitable for retrieving LAI than RSR and MNDVI, because it avoids the difficulty in properly determining the maximum and minimum SWIR values required in RSR and MNDVI, which improves the robustness of MAVI in retrieving LAI of different land cover types. Moreover, MAVI is expressed as ratios between different spectral bands, greatly reducing the noise caused by topographical variations, which makes it more suitable for applications in mountainous area.