Performance and Analysis of a Model for Describing Layered Leaf Area Index of Rice

Layered leaf area index （LAIk） is one of the major determinants for rice canopy. The objective of this study is to attain rice LAI k using morphological traits especially leaf traits that affected plant type. A theoretical model based on rice geometrical structure was established to describe LAI k of rice with leaf length （Li）, width （Wi）, angle （Ai）, and space （Si）, and plant pole height （H） at booting and heading stages. In correlation with traditional manual measurement, the model was performed by high R2-values （0.95-0.89, n=24） for four rice hybrids （Liangyoupeijiu, Liangyou E32, Liangyou Y06, and Shanyou 63） with various plant types and four densities （3 750, 2 812, 1 875, and 1 125 plants per 100 m2） of a particular hybrid （Liangyoupeijiu）. The analysis of leaf length, width, angle, and space on LAI k for two hybrids （Liangyoupeijiu and Shanyou 63） showed that leaves length and space exhibited greater effects on the change of rice LAI k . The radiation intensity showed a significantly negative exponential relation to the accumulation of LAI k , which agreed to the coefficient of light extinction （K）. Our results suggest that plant type regulates radiation distribution through changing LAI k . The present model would be helpful to acquire leaf distribution and judge canopy structure of rice field by computer system after a simple and less-invasive measurement of leaf length, width, angle （by photo）, and space at field with non-dilapidation of plants.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Modeling Leaf Area Estimation for Arabica Coffee (<i>Coffea Arabica</i>L.) Grown at Different Altitudes of Mana District, Jimma Zone

This study was aimed at establishing allometric models for estimating LA (Leaf Area) of eight Coffea arabica genotypes in Mana district of Jimma Zone Oromia Regional State, South Western Ethiopia (7&deg;46'N, 36&deg;0'E). Many Methodologies and instruments have been devised to facilitate measurement of leaf area. However, these methods are destructive, laborious and expensive. For modeling leaf area, leaf width, leaf length and leaf area of 1200 leaves (50 leaves for each genotype) was measured for model calibration and the respective measurements on 960 leaves were used for model validation. Linear measurement was taken from leaves and branch diameters of eight genotypes of C. arabica, cultivated in field following a randomized complete blocks design at three altitudes (High, Medium and Low) were evaluated to identify best option for input in the models, and to validate the method to estimate the leaf area. Linear and non-linear models were tested for their accuracy to predict leaf area of the eight C. arabica genotypes. The use of linear model resulted in high accuracy for all of the eight C. arabica genotypes. No significant effect of growing altitude and genotype was obtained among the slopes of the models. Therefore, one single model was fitted to the combined data of all genotypes at all altitudes (LA = 0.6434LW). Comparison between observed and predicted leaf area was made using this model in another independent dataset, conducted for model validation, exhibited a high degree of correlation (r = 0.98 - 0.99, P < 0. 01). The over or under estimation of the leaf area using this model ranges between 0.02% to 1.7% and this model is adequate to estimate the leaf area for the eight C. arabica genotypes. Hence, this model can be proposed to be reliably used and with this developed model, researchers can estimate the leaf area of newly released eight genotypes of C. arabica at different altitudes accurately.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.

Simulation of Growth and Leaf Area Index of Quality Protein Maize Varieties in the Southwestern Savannah Region of the DR-Congo

Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.

Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

In the past several decades, dynamic global vegetation models（DGVMs） have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM（IAP-DGVM） has been developed and coupled to the Common Land Model（CoLM） within the framework of the Chinese Academy of Sciences＇ Earth System Model（CAS-ESM）. This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM（CoLM-DGVM） and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more ＂needleleaf evergreen boreal tree＂ and ＂broadleaf deciduous boreal shrub＂, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM,including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark.

Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

基于便携式作物生长监测诊断仪的红壤花生叶片氮积累量和叶面积指数监测

通过分析红壤花生不同生育期的生长指标动态变化特征及其与冠层光谱植被指数间的定量关系,以赣花5号和航花2号这2个花生品种为试验对象,设置4个施氮水平,在花生关键生育期(苗期、花针期、结荚期和饱果期)利用便携式作物生长监测诊断仪(CGMD-402)采集冠层光谱植被指数,并同步取样测定各处理的地上部生物量、叶片氮积累量(LNA)和叶面积指数(LAI),构建基于CGMD-402的红壤花生LNA和LAI监测模型。结果表明:施氮水平会对红壤花生植株的生长产生影响,地上部植株的生物量会随着施氮量的增加而增大;叶片氮积累量和叶面积指数均随生育进程的推进整体上表现为先升后降的动态变化特征;花针期与结荚期的花生冠层归一化植被指数(NDVI)与LAI和LNA均具有较好的相关性。因此,可利用便携式作物生长监测诊断仪CGMD-402监测红壤花生的LAI和LNA,为江西省红壤花生的精确施氮管理提供技术支撑。

基于无人机影像的冬小麦株高提取与LAI估测模型构建

株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。

基于BEPS模型的塞罕坝植被净初级生产力时空变化分析研究

叶面积指数(leaf area index,LAI)是北部生态系统生产力模拟模型(boreal ecosystem productivity simulator,BEPS)的关键驱动数据,获取高精度LAI对区域森林生态系统碳循环十分重要,然而当前大多研究采用的MODIS LAI产品缺乏可信度。为此,基于LAI动态模型、PROSAIL辐射传输模型和层状贝叶斯网络(Hierarchical Bayesian Network,HBN)构建数据同化系统,获得空间分辨率为20 m的LAI数据,驱动BEPS模型,模拟塞罕坝机械林场2011—2021年的植被净初级生产力(Net Primary Productivity,NPP),并对NPP时空变化特征及影响因子进行分析。结果表明,基于贝叶斯同化方法获得的高分辨率LAI数据极大提高了MODIS LAI产品的精度;基于同化后的LAI数据驱动BEPS模型获取模拟森林NPP,与样地实测数据计算NPP间相关性较高(R^(2)=0.77);2011—2021年塞罕坝机械林场植被NPP平均值为307.4 g/(m^(2)·a),森林NPP呈现平稳增长趋势;不同植被类型模拟NPP存在较大差异,针叶林、落叶林及混交林模拟NPP分别为484.9、402.4、287.9 g/(m^(2)·a);植被NPP与温度因子相关性较高,偏相关系数为0.2~0.8,而植被NPP与降水量的相关性总体而言相对较低,其偏相关系数为-0.3~0.4,在该地区降水量对植被NPP的影响较低,温度为该地区NPP变化的主导因子。研究结果可获取高空间分辨率的LAI数据,为森林生态系统碳循环的精准时空模拟提供依据。

应用地基雷达和机载激光雷达数据反演落叶松冠层叶面积密度

为了实现落叶松冠层叶面积密度盲区互补的协同反演,以塞罕坝机械林场为研究区,根据地基雷达和机载激光雷达数据对落叶松林冠层点云信息进行提取;利用点云分割算法对点云信息进行枝叶分离,并对冠层点云进行体素建模,分析落叶松林最优体素和接触频率的相关性;采用体素模型冠层分析法(VCP)分别对机载雷达和地基雷达数据绘制冠层叶面积密度曲线,实现对区域林木叶面积密度的反演。结果表明:运用冠层分析法适用于估算落叶松的叶面积密度,机载雷达点云数据和地基雷达点云数据协同估算的相对误差最小(平均相对误差1.95%),机载雷达数据估算次之(平均相对误差5.09%),地基雷达数据估算误差最大(平均相对误差9.37%)。因此,机载和地基雷达数据协同反演可以提升落叶松林叶面积密度的估算精度。

基于特征波段选择的冬小麦叶面积指数高光谱遥感估测模型研究

为提高冬小麦叶面积指数(LAI)的遥感估测精度,以实现其无损快速测定目标,在田块尺度设置多年定点不同冬小麦品种氮梯度试验,测定其不同生育时期冠层高光谱数据和LAI,通过原始冠层光谱数据与一阶导数预处理(first-derivative, FD)组合竞争自适应重加权采样(competitive adaptive reweighted sampling, CARS)、无信息变量消除(uninformative variable elimination, UVE)和随机蛙跳(random frog, RF)三种特征波段选择方法进行偏最小二乘回归(partial least squares regression, PLSR)高光谱估测模型构建。结果表明,一阶导数预处理在简化波段数量和提升模型精度上具有较好作用。经过与全波段数据及六种组合内部建模预测精度对比,RF在简化波段方面效果最好,FD-RF组合筛选波段数量为6个,建模的R^(2)和RMSE分别达到0.850和0.730,预测的R^(2)和RMSE分别为0.704和1.005;FD-CARS组合达到了最佳建模精度,R^(2)和RMSE分别为0.876和0.641;FD-UVE组合达到了最佳预测精度,R^(2)和RMSE分别为0.755和0.672。这说明基于特征波段选择可以进行冬小麦叶面积指数高光谱遥感模型建立与有效估测。

基于高光谱的小麦产量岭回归估测模型研究

为探究基于岭回归分析在小麦产量预测上的可行性,以苏隆128、扬麦20、皖西麦0638和宁麦13为供试材料,利用高光谱获取4个关键生育时期(拔节期、孕穗期、开花期、灌浆期)的光谱数据,将植被指数和岭回归分析分别与LAI结合构建小麦产量预测模型,并比较其预测精度。结果表明,各生育时期基于岭回归分析的小麦LAI预测模型比基于植被指数的小麦LAI预测模型的精确度整体偏高;相比于植被指数与LAI构建的小麦产量预测模型,各生育时期基于岭回归分析的小麦产量预测模型精度均较高,预测模型的r_(2)均在0.83以上,且RMSE、MAPE整体较低,尤其在拔节和开花期模型精度更高。因此,岭回归分析能够有效提高小麦产量预测模型的精准性与稳定性。

基于图像几何信息的遮荫条件下茄衣烟叶形态特征分析与叶面积生长模型构建

【目的】筛选遮荫影响茄衣烟叶的关键形态特征指标,建立茄衣烟叶叶面积生长模型,明确遮荫条件下茄衣烟叶动态发育规律,以期为国产茄衣烟叶精准栽培提供理论依据。【方法】以雪茄烟品种CX81为试验材料,设3个遮荫处理,分别为透光率60%(T1)、透光率70%(T2)和透光率100%不遮荫对照(CK)。基于图像几何信息提取烟叶形态特征,测定上、中、下3个叶位叶片的动态生长指标,对不同透光率下茄衣烟叶的形态特征进行正交偏最小二乘判别分析(OPLS-DA),并建立叶面积生长模型。【结果】OPLS-DA结果表明,上、中、下部烟叶在不同透光率处理下的形态特征参数存在明显差异,随着叶位提高,受遮荫影响的形态指标逐渐减少,3个部位的共同指标有叶面积生长速率、叶面积、叶周长和茎叶夹角。遮荫条件下,叶面积与遮荫时间的关系符合Richards模型,其方程为y=a/(1+e^(b-cx))^(1/d),模型稳定性好,拟合程度高,具备生物学意义。随着透光率增加,叶面积先增大后减小,T2处理叶面积最大,遮荫45 d时,T2处理的叶面积较CK增加4.7%~17.4%;同一部位烟叶平均生长速率呈慢—快—慢的变化规律,T2处理叶面积平均生长速率最大,较CK提高5.5%~13.4%。【结论】叶面积生长速率、叶面积、叶周长和茎叶夹角是影响遮荫条件下茄衣烟叶叶片形态的关键指标,Richards模型可有效预测遮荫条件下茄衣烟叶叶面积生长,70%透光率可加快叶面积生长速率,有效提高叶面积。

Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile.

基于同化植被净初级生产力的区域玉米产量估测

为了评价植被净初级生产力NPP作为同化变量提高区域玉米产量估测精度的有效性及其应用于区域玉米产量估测的潜力,选择黑龙江省双鸭山市友谊农场玉米种植区为研究对象,以WOFOST为作物生长动态模型,分别以叶面积指数LAI和植被净初级生产力NPP为同化变量,选用MODIS LAI和NPP产品为遥感观测数据,开展基于遥感观测数据与作物生长模型同化的区域玉米产量估测研究。重点比较了分别以叶面积指数LAI和植被净初级生产力NPP为同化变量的区域玉米产量估测结果精度。结果表明,相较以叶面积指数LAI为同化变量的区域玉米产量估测统计结果(均值为7755 kg/hm^(2),标准差为1303 kg/hm^(2)),以植被净初级生产力NPP为同化变量的区域玉米产量估测统计结果(均值为9214 kg/hm^(2),标准差为190 kg/hm^(2))与研究区域统计结果(均值为8970 kg/hm^(2))更为接近,但在表现玉米产量空间异质性方面稍显不足。以植被净初级生产力NPP为同化变量开展区域作物产量估测是一种可行的数据同化策略,具有较大的应用潜力。

黄河源区下垫面变化对水文过程的影响

量化评估下垫面变化对水平衡要素的影响,对揭示黄河源区径流变化归因具有重要意义。论文建立了考虑土地覆被变化(LUCC)和叶面积指数(LAI)动态变化的可变下渗容量(VIC)模型,模拟了下垫面变化条件下的降水-径流响应关系。结果表明,考虑下垫面动态变化的VIC模型能更好地模拟源区水文过程,相对误差降低8.8%~12.9%。2001—2018年LAI和LUCC综合作用导致植物蒸腾量年均增加约15%,玛曲和唐乃亥断面径流分别减少约9.19%和7.17%。LAI对径流影响较LUCC大,LAI对玛曲和唐乃亥断面多年平均径流量的贡献分别为-4.80%和4.48%,而LUCC的贡献为0.16%和-3.15%。研究解释了下垫面变化是源区降水增加条件下径流变化不显著的原因,对认识气候变化和生态保护的水文响应规律有借鉴意义。

基于ME-BiLSTM模型的苜蓿叶面积指数预测方法

连续时序的叶面积指数(Leaf Area Index,LAI)可反映苜蓿长势的变化情况,预测苜蓿未来时段的LAI对指导田间管理决策具有重要作用。针对LAI数据采集困难,导致苜蓿时序LAI存在训练数据不足的问题,该文以生长天数为自变量,采用修正的Logistic模型对实测苜蓿LAI变化的动态过程进行建模,根据LAI模拟曲线进行数据插补,从而构建宁夏引黄灌区试验区3年的逐日苜蓿LAI数据集。在插补数据集的基础上,为解决苜蓿刈割后数据突变问题,提出了一种ME-BiLSTM模型。该模型集成移动累计和检验方法(MOSUM)以及基于双向长短期记忆网络(BiLSTM)的编码器-解码器神经网络。MOSUM方法可以实现LAI数据集中突变点检测,并剔除包含突变点训练批次,同时应用改进的BiLSTM模型进行预测。结果表明:ME-BiLSTM模型能较好地进行苜蓿LAI未来曲线变化的预测,其决定系数(R^(2))、均方根误差(RMSE)值分别为0.9985和0.0722。对于苜蓿生长的各个茬次,预测模型对于第1茬、第4茬的预测精度最高,第2茬和第3茬的预测精度稍有降低。

14种园林绿化植物的叶面积测量及经验模型构建

【目的】本文旨在探索快速且准确测定植物叶面积的有效方法。【方法】本试验以中国北方常用的14种园林绿化植物为材料,采用5种不同方法测定其叶面积,并将叶长、叶宽等叶片参数与叶面积进行回归分析,确定系数回归法中叶面积换算的最佳叶片参数,以期为生产和科研提供参考依据和方法。【结果】叶面积仪法、方格计数法、复印纸称重法和数字图像法所获得的叶面积数据之间均存在显著的线性相关关系(P <0.01),数字图像法是叶面积测量中最稳定的一种方法。14种植物的叶面积、叶周长、叶长、叶宽、叶长/叶宽差异均较显著(P <0.01)。14种植物的平均叶面积、叶周长、叶长、叶宽、叶长/叶宽分别为1.11~20.62 cm2、4.43~22.33 cm、1.47~7.90cm、1.02~4.76 cm、1.11~2.89。【结论】叶长和叶宽的乘积与叶面积拟合的线性关系(A=0.72L·W+0.37)和幂指数关系(A=0.78(L·W)^(0.97))最好,均达到显著相关性(P <0.01),在实践中这两个方程均可作为测算14个树种叶面积的回归方程。