Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands

The feasibility of detecting the seasonal variation in leaf area index (LAI) in boreal conifer forests is investigated using optical instruments. The LAI of six stands was measured. They include young and old jack pine (Pinus banksiana) and old black spruce (Picea mariana) located near the southern border (near Prince Albert, Saskatchewan) and near the northern border (near Thompson, Manitoba) of the Canadian boreal ecotone. LAI values of the stands are obtained by making several corrections to the effective LAI measured from the LI-COR LAI-2000 Plant Canopy Analyzer (PCA). The corrections include a foliage element (shoot) clumping index (for clumping at scales larger than the shoot) measured using the optical instrument TRAC (Tracing Radiation and Architecture of Canopies) developed by Chen and Cihlar (Chen, J.M. and Cihlar, J., 1995a, Plant canopy gap size analysis theory for improving optical measurements of leaf area index of plant canopies, Appl. Opt., 34: 6211–6222), a needle-to-shoot area ratio (for clumping within the shoot) obtained from shoot samples, and a woody-to-total area ratio obtained through destructive sampling of trees. It is found that the effective LAI varied about 5% to 10% in the growing season and the element clumping index remained almost unchanged. The needle-to-shoot area ratio varied the most, about 15% to 25%, which is of the same order of magnitude as the expected seasonal variability in LAI. This demonstrates that most of the seasonal variation information is contained in the needle-to-shoot area ratio, which can not be measured indirectly using in situ optical instruments and has to be obtained from a large quantity of shoot sample analysis which is laborious and error-prone. Based on our experience, an improved and convenient shoot sampling strategy is suggested for future studies. The optically-based LAI values were compared with destructive sampling results for three of the stands. Based on error analysis, we believe that optical measurements combined with shoot sample analysis can produce LAI values for conifer stands which are more accurate than destructive sampling results.     

Leaf area index uncertainty estimates for model-data fusion applications

Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux site for illustrative purposes. We made measurements along two transects (one in a mature stand, one in a recently harvested shelterwood) before sunset on successive days using both the Li-Cor LAI-2000 plant canopy analyzer and digital hemispherical photography (DHP). The random measurement uncertainty (1σ) at a given point for a single measurement is about 5% for LAI-2000 and 10% for DHP. These uncertainties are small compared to potential systematic biases due to instrument calibration errors and data processing decisions, which are estimated to be 10-20% for each instrument. Sampling uncertainty (due to the spatial variability along each transect where we conducted our measurements) is an additional, but again relatively small, uncertainty. Assumptions about clumping parameters, for which standard literature values are typically used, remain large sources of uncertainty. This analysis can also be used to develop strategies to reduce measurement uncertainties.     

Simulation of statistical characteristics of gap distribution in forest stands

An estimate of the required amount of LAI-2000 or digital hemispheric image measurements to ensure the desired accuracy of the leaf area index value derived from optical analyser measurements by inversion of gap fraction data is given. The estimate is based on a theoretical model of the second order statistics of gap occurrence in forest canopies. The main attention is paid to gap fractions averaged over the angular rings similar to the LAI-2000 instrument. Nilson's (1999) forest gap fraction model is first extended to simulate the bidirectional gap fraction and next the approximate equations for the standard deviation of gap fraction, correlation matrix of gap readings in different LAI-2000 angular rings, standard deviation of the LAI value as determined by the inversion of gap fraction data are derived. The results of simulation are compared with those obtained by LAI-2000 instrument and digital hemispheric photographs in three mature forests from Järvselja, Estonia. The comparison shows an acceptable agreement between the simulated and measured second order statistical characteristics. The distance from the observation height to the crowns and crown dimensions appear to be important in determining the magnitude of variance. When the LAI-2000 instrument is used together with a view-cap, the angular size of the view-cap has an effect on the second order statistics and on the value of apparent clumping index. A computer program has been written to calculate the second-order gap statistics.     

漓江上游山区水源林叶面积指数变化的遥感监测

为了定量评价漓江上游山区复杂地形水源林叶面积指数(LAI)的变化,对阔叶林、针叶林、竹林样地以TRAC仪器测定LAI,利用遥感数据计算归一化植被指数(NDVI)、比值植被指数(SR)、减化比值植被指数(RSR)、土壤调整植被指数(SAVI)、增强植被指数(EVI),并从DEM数据获取高程、坡度、坡向,提出并建立复杂地形最优多植被指数组合估算山区林地LAI的神经网络模型,利用模型对1989–2009年6景TM/ETM遥感图像估算LAI空间分布。结果表明,神经网络解决了LAI与多植被指数的非线性回归方程无法引入地形因素、且方程系数较多较难确定的问题,提高了LAI的估算精度。研究区成熟阔叶林减少代之以大片种植经济幼林,是导致林地LAI变化的原因。1989-2000年,LAI≥6.0的林地面积比例从78.8%逐年急剧下降到44.1%,LAI在1.0~6.0的林地面积比例从20.8%大幅上升到55.4%;2000-2009年,随着幼林的生长、竹林的速生,LAI≥6.0的林地面积比例逐渐上升恢复到74.5%,但仍未恢复到1989年的面积比例,相应LAI在1.0~6.0的林地面积比例逐渐下降到25.1%。研究成果为漓江上游水源林生态评估提供参考。     

不同叶面积指数遥感产品在呼伦贝尔草甸草原的应用对比

叶面积指数(leaf area index,LAI)是植被生理过程模拟的重要参数之一,对植被气候关系、全球气候变化研究等具有重要的意义。近年来LAI产品越来越多,这些产品的精度、区域适用性都不相同。为了选择适用于呼伦贝尔典型草甸草原的LAI产品,为以后在呼伦贝尔展开工作提供便利,该文以内蒙古呼伦贝尔草甸草原为研究区域,利用2013年6-8月6期地面试验数据,以HJ-1A/B CCD高分辨率影像为中间桥梁建立植被指数SR与LAI的统计模型,反演得到LAI参考图像,对研究区域内与地面试验同期的MODIS LAI和GLASS LAI、GEOV1 LAI产品分别进行了直接验证与交叉验证。结果显示,3个LAI产品均存在高估现象,以GLASS LAI最为显著约高估41%,其次是MODIS LAI约高估了32%。GEOV1 LAI产品准确性最高,RMSE=0.289 MAE=0.216。GLASS LAI与GEOV1 LAI产品的相关性最好(R2=0.6465)。通过对比全年LAI产品发现,3个产品具有良好的时序一致性。GLASS LAI呈现为平滑曲线,高估现象主要存在于LAI值较小时。MODIS LAI最不稳定性,波动性较大。GEOV1 LAI产品在第133天至第201天这段时间内LAI值比其他两个产品的LAI值小;在第202天后GEOV1 LAI值与GLASS LAI值相差无几,高于MODIS LAI。根据对比分析结果,GEOV1 LAI产品最适用于呼伦贝尔典型草甸草原。通过提取质量控制层数据,确定云覆盖不是影响LAI异常的原因。     

Slope correction for LAI estimation from gap fraction measurements

Digital hemispherical photography poses specific problems when deriving leaf area index (LAI) over sloping terrain. This study proposes a method to correct from the slope effect. It is based on simple geometrical considerations to account for the path length variation within the canopy for cameras pointing vertically. Simulations over sloping terrain show that gap fraction increases up-slope while decreasing down-slope. As a consequence of this balance between up- and down-slope effects, effective LAI estimates derived from inversion of the Poisson model are marginally affected for low to medium slopes (<25°) and LAI (LAI < 2). However, for larger slopes and LAI values, estimated LAI values may be strongly underestimated. The proposed correction was evaluated over four forested sites located over sloping terrain. Results indicate that in these conditions (LAI between 0.6 up to 3.0, clumped canopies with relatively erectophile leaf distribution), the effect of the slope (between 25° and 36°) was moderate as compared to other potential sources of problems when deriving LAI from gap fraction measurements, including clumping, leaf angle inclination and spatial sampling.     

Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI,errors and sampling

The theoretical background of modeling the gap fraction and the leaf inclination distribution is presented and the different techniques used to derive leaf area index (LAI) and leaf inclination angle from gap fraction measurements are reviewed. Their associated assumptions and limitations are discussed, i.e., the clumping effect and the distinction between green and non-green elements within the canopy. Based on LAI measurements in various canopies (various crops and forests), sampling strategy is also discussed.     

野外叶面积指数自动测量仪器设计及应用试验

为提高长时间序列叶面积指数测定模型的模拟精度,该文设计一种可以长期在野外间接测量叶面积指数仪器,仪器由太阳能提供持久电能,利用单片机控制垂直水平舵机,带动光强传感器记录多角度光线强度,在2台仪器同时测量的情况下,获得光线透过林分冠层多角度光强衰减率,通过编写计算程序解算林分叶面积指数。使用商品化光学仪器LAI-2200验证该仪器测量精度,对24块样地叶面积指数进行测量,两仪器测量结果高度吻合R2为0.927,绝对标准误差为0.36。长时间野外叶面积指数自动测量获取数据可节省人力、减少人为误差。     

Comparison of methods for measuring gap size distribution and canopy nonrandomness at Järvselja RAMI (RAdiation transfer Model Intercomparison) test sites

Methods for analyzing foliage nonrandomness by means of the TRAC instrument, digital hemispheric photography, and a gap fraction model are assessed at two RAMI (RAdiation transfer Model Intercomparison) mature stands in Järvselja, Estonia. The six different methods involve calculation of the canopy element clumping index, at scales coarser than that of a shoot. The major aim was to define the merits and limitations of the various methods. We conclude the gap size distribution and beyond-shoot clumping is very stable across the stands for the solar zenith angle range from 30° to 60°. Estimates based on the gap size distribution and the combination of gap size and logarithm methods performed the best while compared with an independent gap fraction model. We clarify the effect of the assumed leaf inclination angle distribution on gap size distribution and differences between estimates of beyond-shoot clumping. We show that the modified, gap-size distribution based method of Chen and Cihlar can provide reliable beyond-shoot clumping estimates without any a priori assumptions about the total gap fraction, segment length or the leaf inclination angle distribution. We also illustrate the changes in element clumping with measurement height. The compiled data extend the original parameter dataset to be used in the next phase of RAMI for different benchmark tests and reflectance modeling experiments, and contribute toward systematic validation efforts of radiative transfer models, operational algorithms, and field instruments, as promoted by the Committee on Earth Observation Satellites (CEOS).     

Foliage area and architecture of plant canopies from sunfleck size distributions

A Poisson model is developed to describe sunfleck or gap size distributions beneath clumped plant canopies. This model is based on the assumption that foliage clumps are randomly distributed in space and foliage elements are randomly distributed within each clump. Using this model, the foliage clumping index, leaf area index (L), clump area index, element area index in each clump, and element and clump widths were successfully derived for two artificial canopies and a thinned and pruned Douglas-fir forest stand. It is shown that existing theories for deriving L from measurements of canopy gap fraction have limitations, and the use of canopy architectural information derived from canopy gap size distribution can substantially improve the technique for indirectly measuring L of plant canopies.     

基于多源无人机影像特征融合的冬小麦LAI估算

为探讨无人机多源影像特征融合估测作物叶面积指数的能力,该研究以冬小麦为研究对象,利用多旋翼无人机搭载高清数码相机和UHD185成像光谱仪获取研究区冬小麦关键生育期（扬花期、灌浆期）的可见光和高光谱影像。综合考虑可见光、高光谱影像特征与冬小麦叶面积指数的相关性及影像特征重要性进行特征筛选,然后,以可见光植被指数、纹理特征、可见光植被指数+纹理特征、高光谱波段、高光谱植被指数及高光谱波段+植被指数分别作为输入变量构建多元线性回归、支持向量回归和随机森林回归的叶面积指数估测模型（单传感器数据源）;以优选的两种影像特征结合支持向量回归、随机森林回归构建叶面积指数估测模型（两种传感器数据源）,比较分析单源与多源影像特征监测冬小麦叶面积指数的性能。进一步地,考虑到小区土壤空间异质性会影响冬小麦叶面积指数估测结果,该研究探讨了不同影像采样面积下基于单源遥感数据构建的小麦叶面积指数估测模型精度。研究结果表明：在扬花期和灌浆期,使用两种影像优选特征构建的随机森林回归估测模型精度最佳,验证集决定系数分别为0.733和0.929,均方根误差为0.193和0.118。可见光影像采样面积分别为30%和50%,高光谱影像采样面积为65%时,基于单源影像特征构建的随机森林回归估测模型在扬花期和灌浆期效果最好。综上,该研究结果可为无人机遥感监测作物生理参数提供有价值的依据和参考。     

Documenting improvement in leaf area index estimates from MODIS using hemispherical photos for Australian savannas

This paper compares estimates of Leaf Area Index (LAI) obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) collections 4.8 (MC4) and 5.0 (MC5) with ground-based measurements taken along a 900 km north-south transect through savanna in the Northern Territory, Australia. There was excellent agreement for both the magnitude and timing in the annual variation in LAI from MC5 and biometric estimates at Howard Springs, near Darwin, whereas MC4 overestimated LAI by 1-2 m² m⁻² for the first 200 days of the year. Estimates of LAI from MC5 were also compared with those obtained from the analysis of digital hemispherical photographs taken during the dry season (September 2008) based on algorithms that included random and clumped distribution of leaves. Linear regression of LAI from MC5 versus that using the clumping algorithm yielded a slope close to 1 (m = 0.98). The regression based on a random distribution of leaves yielded a slope significantly different from 1 (m = 1.37), with higher Mean Absolute Error (MAE) and bias compared to the clumped analysis. The intercept for either analysis was not significantly different from zero but inclusion of five additional sites that were visually bare or without green vegetation produced a statistically significant offset of +0.16 m² m⁻² by MC5. Overall, our results show considerable improvement of MC5 over MC4 LAI and good agreement between MC5 and ground-based LAI estimates from hemispherical photos incorporating clumping of leaves.     

2000-2014年黄土高原植被叶面积指数时空变化特征

研究植被叶面积指数(LAI)时空变化特征,对植被的水土保持效具有重要意义.利用MOD15A2H遥感产品,基于Mann-Kendall趋势检验与Sen斜率分析方法,提取区域尺度与像素尺度上的植被LAI变化特征,并基于不同子流域、坡度、坡向及植被覆盖类型,对植被LAI的变化特征进行分析.基于MOD44B遥感产品,利用线性回归和偏相关系数,分析植被LAI的变化原因.结果表明:1)黄土高原2000-2014年,植被LAI呈显著增加趋势,其年绝对变化幅度为0.042,年相对变化程度为2.71％.2)空间上,在黄土高原58.6％的区域,LAI呈现显著增加趋势,仅有0.9％的区域LAI呈现显著减少趋势.植被LAI剧烈增加,主要发生在河口—龙门区间,包括皇甫川、窟野河、无定河和延河.植被在15°～35°的坡度上,LAI变化程度最剧烈,其变化在各坡向上没有显著差异,农田和草地的LAI变化程度最剧烈.3)与植被总覆盖度相比,植被垂直维结构与黄土高原植被LAI的变化更为相关,其中树木覆盖度的增加,是植被垂直维结构变化的重要原因之一.     

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages. One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor (C-factor), which represents how cropping and management practices affect the rates and potential risk of soil erosion. We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT (Soil and Water Assessment Tool) model on a watershed scale. The remotely sensed processed variables included the enhanced vegetation index (EVI), the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), leaf area index (LAI), soil available water content (AWC), slope gradient (SG), and ratio of area (AR) of every hydrologic response unit (HRU) to that of the total watershed, comprising unique land cover, soil type, and slope gradient characteristics within the Fish River catchment in Alabama, USA between 2001 and 2014. Linear regressions, spatial trend analysis, correlation matrices, forward stepwise multivariable regression (FSMR), and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors. Based on the data analysis and modeling, we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR, LAI, AWC, AR, and SG (predicted R² (R_pred²) = 0.51; R² = 0.68, n = 3 220, P < 0.15). The results showed that the developed FSMR model constituting the non-conventional factors AWC (R_pred² = 0.32; R² = 0.48, n = 3 220, P < 0.05) and FPAR (R_pred²= 0.13; R² = 0.28, n = 3 220, P = 0.31) was an improved fit for the watershed C-factor. In conclusion, the union of dynamic variables related to vegetation (EVI, FPAR, and LAI), soil (AWC), and topography (AR and SG) can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.     

21000-2300nm波段的小麦冠层光谱特性与叶面积指数和叶片氮含量间的关系及其受叶片水分含量的影响

The effects of leaf water status in a wheat canopy on the accuracy of estimating leaf area index （LAI） and N were determined in this study using extracted spectral characteristics in the 2 000-2 300 nm region of the short wave infrared （SWI） band. A newly defined spectral index, relative adsorptive index in the 2 000-2 300 nm region （RAI2000-2300）, which can be calculated by RAI2000-2300 = （R2224 - R2054） （R2224 ＋ R2054）-1 with R being the reflectance at 2 224 or 2 054 nm, was utilized. This spectral index, RAI2000-2300, was significantly correlated （P 〈 0.01） with green LAI and leaf N concentration and proved to be potentially valuable for monitoring plant green LAI and leaf N at the field canopy scale. Moreover, plant LAI could be monitored more easily and more successfully than plant leaf N. The study also showed that leaf water had a strong masking effect on the 2 000-2 300 nm spectral characteristics and both the coefficient between RAI2000-2300 and green LAI and that between RAI2000-2300 and leaf N content decreased as leaf water content increased.     

利用HJ-1-A/B CCD2数据反演冬小麦叶面积指数

叶面积指数是十分重要的作物生理生态参数,为提高利用国产环境减灾小卫星CCD数据反演冬小麦叶面积指数的精度,该文以5种常用的植被指数(归一化差值植被指数(normalized difference vegetation index,NDVI),增强植被指数(enhanced vegetation index,EVI),双波段增强植被指数(2-bands enhanced vegetation index,EVI2),比值植被指数(ratiovegetation index,RVI),土壤调节植被指数(soil-adjusted vegetation index,SAVI)为基础,结合3种常用的回归模型,按生长阶段比较分析了不同植被指数和回归模型反演叶面积指数的精度。结果表明,除生殖生长阶段外,叶面积指数和5种植被指数之间均有较强的相关关系;指数模型和一元线性模型分别为全生育期和营养生长阶段的最佳拟合模型;EVI在全生育期拟合时的表现好于其他4个指数(R2=0.9348),SAVI则是营养生长阶段表现最佳的指数(R2=0.9404)。该研究为进一步利用植被指数反演叶面积指数提供了参考。     

基于高光谱图像的茶树LAI与氮含量反演

为了对茶树进行实时、快速、无损的叶面积指数LAI和氮含量检测,该文以英红九号茶树为试验对象,利用便携式高光谱成像仪采集光谱数据、人工破坏性采摘叶片进行叶面积指数的计算以及传统化学方法测量叶片氮含量,比较不同高光谱特征变换形式与LAI和氮含量之间的相关性,并选择其中相关系数较高的高光谱特征变量作为自变量,分别采用线性、指数、对数和抛物线表达式建立LAI和氮含量的回归模型。结果显示:在多种高光谱数据变量建立的模型中,以绿峰反射率R_g为自变量的对数拟合模型最佳,其拟合样本的决定系数R~2和验证样本的均方根误差RMSE值分别为0.9和0.087 6。以植被指数变量VI_4(红边面积/黄边面积)与氮含量建立的指数模型为最佳建模效果,拟合样本的决定系数R~2和验证样本的均方根误差RMSE值分别为0.830 3和0.102 9,研究结果可为茶树叶面积指数LAI和营养成分的无损检测提供参考。     

基于无人机遥感影像的大豆叶面积指数反演研究

作物叶面积指数的遥感反演是农业定量遥感研究热点之一,利用无人机遥感监测系统获取农作物光谱信息精确反演叶面积指数对精准农业生产与管理意义重大。本研究以山东省嘉祥县一带的大豆种植区为试验区,设计以多旋翼无人机为平台同步搭载Canon Power Shot G16数码相机和ADC-Lite多光谱传感器组成的无人机农情监测系统开展试验,分别获取大豆结荚期和鼓粒期的遥感影像。使用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合田间同步实测叶面积指数(leaf area index,LAI)数据,采用经验模型法分别构建了单变量和多变量LAI反演模型,通过决定系数(R2)、均方根误差(RMSE)和估测精度(EA)3个指标筛选出最佳模型。研究表明,有选择性地分时期进行农作物的叶面积指数反演是必要的,鼓粒期作为2个生育期中大豆LAI反演的最佳时期,其NDVI线性回归模型对大豆LAI的解释能力最强,R2=0.829,RMSE=0.301,反演大豆LAI最准确,EA=85.4%,生成的鼓粒期大豆LAI分布图反映了当地当时大豆真实长势情况。因此,以多旋翼无人机为平台同步搭载高清数码相机和多光谱传感器组成的无人机农情监测系统对研究大豆叶面积指数反演是可行性,可作为指导精准农业研究的一种新方法。     

Assessing the effects of the clumping phenomenon on BRDF of a maize crop based on 3D numerical scenes using DART model

Inverting radiative transfer (R-T) models against remote sensing observations to retrieve key biogeophysical parameters such as leaf area index (LAI) is a common approach. Even if new inversion techniques allow the use of three-dimensional (3D) models for that purpose, one-dimensional (1D) models are still widely used because of their ease of implementation and computational efficiency. Nevertheless, they assume a random distribution of foliage elements whereas most canopies show a clumped organization. Due to that crude simplification in the representation of the canopy structure, sizeable discrepancies can occur between 1D simulations and real canopy reflectance, which may further lead to false LAI values. The present investigation aims to appraise to which extent the incorporation of a clumping index (noted λ) into 1D R-T model could improve the simulations of Bidirectional Reflectance Distribution Function (BRDF). Canopy BRDF is simulated here for three growth stages of a maize crop with the Discrete Anisotropic Radiative Transfer (DART) model in the visible and near infrared spectral bands, for two contrasted soil types (dark and bright) and different levels of heterogeneity to represent the canopy structure. 3D numerical scenes are based on in-situ structural measurements and associated BRDF simulations are thus considered as references. 1D scenarios assume either that leaves are randomly distributed (λ = 1) or clumped (λ < 1). If BRDF simulations seem globally reliable under the assumption of a random distribution in near infrared, it can also lead to relative errors on the total BRDF up to 30% in the red spectral band. It comes out that the use of a clumping index in a 1D reflectance model generally improves BRDF simulations in the red considering a bright soil, which seems relatively independent of LAI. In the near infrared, best results are usually obtained with homogeneous canopies, except with the dark soil. Clearly, influent factors are mainly the LAI and the spectral contrast between soil and leaves.     

基于叶面积指数构建滴灌玉米营养生长期临界氮稀释曲线

明确宁夏引黄灌区基于叶面积指数(leaf area index,LAI)的滴灌玉米临界氮稀释曲线模型及其适用性,探讨以氮营养指数(nitrogen nutrition index,NNI)为监测指标对滴灌水肥一体化模式下玉米氮素营养状况诊断的可行性。该研究于2017-2018年开展了不同施氮量(0~450 kg/hm^2)下4个田块的试验,采用系统分析和统计建模的方法,分析了LAI和植株氮浓度(plant nitrogen concentration,PNC)的定量关系,构建和验证基于LAI的临界氮稀释曲线模型,并建立理论框架,将基于LAI的临界氮曲线与基于植株干物质(plant dry matter,PDM)的临界氮浓度曲线关联,比较基于LAI和PDM的临界氮曲线之间的差异。结果表明,玉米营养生长期临界氮和LAI符合幂函数关系,拟合模型的评价指标均方根误差(root mean square error,RMSE)和标准化均方根误差(normalized RMSE,n-RMSE)的结果分别为0.09和4.13%,模型具有较好的稳定性。在试验氮素水平范围内,不同生育时期NNI随施氮量的增加而增加,变化范围为0.53~1.34,NNI可以准确地反映滴灌玉米氮素营养状况。在非限氮处理下,玉米植株氮素吸收与LAI成正比,LAI与PDM的异速生长参数接近理论值2/3。构建的基于LAI的临界氮曲线可以有效地识别玉米拔节期至吐丝期植株所需的氮状态,为宁夏滴灌玉米氮肥精确管理提供了一种新的评价方法。