中文摘要：

【目的】叶片形状系数（α）为测量作物的叶面积和叶面积指数提供了简单快捷的方法。然而,以往研究表明对作物叶片形状系数的选取存在很大的随意性,缺乏统一标准,且通常将其视为常数,不考虑它的时间和空间变异性。为解决这一问题,文章对陕西关中地区夏玉米不同生长阶段和不同叶位叶片形状系数的时间和空间变异性进行了深入研究。【方法】选取2015年6—10月生长季6个夏玉米品种,将玉米生育期划分为三叶、拔节、抽雄、开花、吐丝、成熟等6个不同生长阶段,每6天采样一次,测量叶片面积（LA）、叶片长度（L）和宽度（W）,计算各个阶段的α值,同时对比α值在单个玉米植株不同叶位之间的差异。然后分别建立线性、二次、对数等3类共5个叶面积估算模型,以RMSE、RRMSE和ARE 3个统计量作为评价指标,对各叶片面积估算模型的精度进行评价。【结果】对全生育期6个夏玉米品种的760个叶片的面积和长宽乘积进行线性回归分析,夏玉米叶片形状系数均值约为0.78;在被验证的5种叶面积估算模型中,叶面积模型LA=α×L×W,其中α=0.78时精度最高,其相对均方根误差（RRMSE）约为9.50%,绝对相对误差（ARE）约为6.96%。α值范围为0.72—0.87,并随玉米生育期的变化而变化,自三叶期到开花期逐渐增大到全生育期最大值0.87,开花后缓慢下降至0.78,其中开花期叶片的α值与开花前各阶段的α值存在显著差异,而与开花后各阶段的α值不存在显著差异。不同熟性的夏玉米品种之间叶片α值也只在开花、吐丝期表现显著差异。不同叶型叶片α值表现出不同的变化规律,三叶期到拔节前,短宽型叶片的α值大于细长型叶片,此后一直到成熟期,细长型叶片的α值则大于短宽型叶片。在单个植株不同叶位叶片之间,α值变异性明显,开花期、吐丝期、成熟期均呈现出两头大中间小的规律,其?

英文摘要：

【Objective】 Leaf shape coefficient（α） provides a simple and fast way for the measurement of crop leaf area and LAI（leaf area index） in field. However, the selection of values of this coefficient was very arbitrary and there was no unique standard to follow. In addition, this coefficient was usually considered as a constant regardless of its variations through the whole lifetime of a given crop.【Method】 In this study, the temporal and spatial variations of α values of summer maize were investigated through a field experiment conducted from June to October in 2015. A total of six maize cultivars with different properties of ripening were involved. The whole growth season of maize was divided into six different stages, i.e. trefoil, jointing, heading, flowering, silking, and maturity. Maize plants were randomly sampled every six days and all leaves were cut off and measured for their length, width, and area with a digital leaf area scanner. Then, α value was calculated for each leaf. The variations of α values were analyzed for different growth stages and among different leaf positions within a single maize plant. Finally, five different models of leave area estimation, which belong to the linear, quadratic, and logarithmic types, were established to estimate the area of each maize leaf. Three different statistics of RMSE（root mean square error）, RRMSE（relative root mean square error）, and ARE（absolute relative error） were used to represent the estimation accuracy. 【Result】 Based on linear regression analysis between leaf areas and products of leaf length and width of 760 leaf samples of six different maize cultivars, the general average value of α was about 0.78. Then, when estimating maize leaf areas with the model of LA=0.78×L×W, the relative root mean square error（RRMSE） and absolute relative error（ARE） were 9.50% and 6.96%, respectively. The accuracy was the highest among the five different models investigated for the estimation of maize leaf area. The results show