Monitoring leaf photosynthesis with canopy spectral reflectance in rice

Non-destructive and rapid method for assessment of leaf photosynthetic characteristics is needed to support photosynthesis modelling and growth monitoring in crop plants. We determined the quantitative relationships between leaf photosynthetic characteristics and canopy spectral reflectance under different water supply and nitrogen application rates. The responses of reflectance at red radiation (wavelength 680 nm) to different water contents and nitrogen rates were parallel to those of leaf net photosynthetic rate (P _N). The relationships of reflectance at 680 nm and ratio index of R(810,680) (near infrared/red, NIR/R) to P _N of different leaf positions and leaf layers in rice indicated that the top two full leaves were the best leaf positions for quantitative monitoring of leaf P _N with remote sensing technique, and the ratio index R(810,680) was the best ratio index for evaluating leaf photosynthetic characteristics in rice. Testing of the models with independent data sets indicated that R(810,680) could well estimate P _N of top two leaves and canopy leaf photosynthetic potential in rice, with the root mean square error of 0.25, 0.16, and 4.38, respectively. Hence R(810,680) can be used to monitor leaf photosynthetic characteristics at different growth stages of rice under diverse growing conditions.     

估测水稻叶层氮浓度的新型蓝光氮指数

基于不同氮素水平与品种类型的多个田间试验,综合分析了水稻冠层高光谱植被指数与叶层氮浓度的定量关系.结果表明：对氮反应最敏感的波段为红光665～675 nm、蓝光490～500 nm和红边区域波段680～760 nm.400～2500 nm波段范围内两波段植被指数与水稻叶层氮浓度相关性最好的是550～600 nm与500～550 nm,属绿光波段组合,决定系数（R²）最高的是比值指数SR(533,565).以3个蓝光波段构建的光谱参数R₄₃₄/(R₄₉₆+R₄₀₁)（蓝光氮指数）与水稻叶层氮浓度呈极显著的直线相关关系,与SR(533,565)相比,该参数显著提高了对叶层氮浓度的预测性.独立资料检验结果显示,R₄₃₄/(R₄₉₆+R₄₀₁)对水稻叶层氮浓度具有较好的预测性,检验根均方差（RMSE）和相对误差（RE）值分别为9.67%和8%,是一种适合于水稻叶层氮浓度估测的良好高光谱植被指数.     

基于冠层反射光谱的棉花叶片氮含量估测

通过分析不同施氮水平下棉花叶片氮含量与冠层多光谱反射率及其衍生的比值、归一化及差值植被指数之间的关系,确立了棉花叶片氮含量的敏感波段及预测方程.结果表明:由红谷区域(610、660、680和710nm4个波段)和近红外区域(760、810、870、950、1100和1220nm6个波段)组成的植被指数与棉花叶片氮含量的相关性较好,比值植被指数RVI(950,710)对叶片氮含量的预测性最好.利用独立的棉花田间试验资料对基于RVI(950,710)的预测方程进行检验,该模型适用于不同棉花品种及不同生育期棉花叶片氮含量预测.     

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

冬小麦冠层叶片氮含量是反映其产量与品质的重要指标,构建高普适性、高精准性冬小麦冠层叶片氮含量高光谱反演模型对提高其监测效率具有重要意义。以不同地点、品种、年份、施氮水平、生育期的大田试验数据为基础,基于两波段光谱植被指数NDRE和550 nm光谱反射率组合构建一个三波段植被指数NEW-NDRE,并与11个传统冬小麦冠层叶片氮素光谱指数进行比较。结果表明: NEW-NDRE及传统植被指数中NDRE、NDDA、RI-1dB与冬小麦冠层叶片氮含量的相关性较好;其中,灌浆初期NEW-NDRE与冬小麦冠层叶片氮含量相关性最好,决定系数R²为0.9,均方根误差(RMSE)为0.4;经独立数据检验,以NEW-NDRE为变量建立的冬小麦冠层叶片氮含量反演模型的平均相对误差(RE)为9.3%,明显低于以NDRE、NDDA、RI-1dB为变量的模型RE。总体上,新构建的NEW-NDRE对冬小麦冠层叶片氮含量的模拟能力显著优于传统指数,减弱了试验条件的限制性,可为精准施肥提供新的技术支撑。     

基于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系

利用空间遥感信息大面积监测小麦冠层氮素营养状况和生产力指标具有重要意义和应用前景.本研究基于不同施氮水平下小麦冠层反射光谱信息,利用响应函数模拟基于不同卫星通道构建的光谱指数(包括单波段、比值光谱指数和归一化光谱指数),分析基于星载通道的光谱指数与小麦冠层叶片氮素营养指标的定量关系,确定监测小麦冠层叶片氮素营养的较好卫星传感器和光谱波段,建立小麦冠层氮素营养指标监测方程.结果表明:利用NDVI(MSS7,MSS5)、NDVI(RBV3,RBV2)、TM4 、CH2、MODIS1和MODIS2遥感数据可以预估小麦叶片氮含量(LNC),其决定系数(R2)在0.60以上;应用NDVI(PB4,PB2)、NDVI(CH2,CH1)、NDVI(MSS7,MSS5)、RVI(MSS7,MSS5)、MODIS1和MODIS2可以预测小麦叶片氮积累量(LNA),其R2大于0.86.比较而言,NDVI(MSS7,MSS5)和NDVI(PB4,PB2)分别为预测小麦LNC和LNA的适宜星载通道光谱参数.     

植被叶片及冠层层次含水量估算模型的建立

利用LOPEX'93数据库中7个鲜叶片含水量(Cw)和光谱反射率实测数据,基于光谱指数法,在叶片层次,用47个随机样本建立Cw与不同光谱指数的统计模型,并用另外20个样本验证.结果表明,Cw的两种表征形式相对含水量FMC和等价水深EWT在提取叶片Cw时差异较大,EWT与各光谱指数的相关性较FMC高,但FMC对叶片Cw的反演精度高于EWT.而反演精度更高的是基于最优子集回归建立的光谱指数线性模型.Ratio₉₇₅是叶片层次提取Cw的普适光谱指数.冠层层次,利用PROSPECT+SAILH耦合模型,模拟在不同叶面积指数LAI和Cw下的冠层光谱.为了剔除背景影响,更好地提取冠层Cw,提出用近红外和短波红外波段反射率构造土壤可调节水分指数(SAWI),该指数与其他光谱指数的比值能明显地剔除土壤背景影响,更准确地提取冠层Cw.Ratio₉₇₅的改进型光谱指数(Ratio₉₇₅-0.9)/(SAWI+0.2)能用来提取叶面积指数LAI从0.3到8.0,Cw从0.0001cm到0.07cm的冠层Cw,研究表明精度较高.     

Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor

Chlorophyll is one of the primary pigments of plant leaves, and changes in its content can be used to characterize the physiological status of plants. Spectral indices have been devised and validated for estimating leaf chlorophyll content (LCC). However, most of the existing spectral indices do not consider the influence of angular reflection on the accuracy of the LCC estimation. In this study, the spectral reflectance factors of leaves from three plant species were measured from several observations in the principal plane. The relationship between the existing spectral indices and the LCC from different directions suggests that the directional reflection of a leaf surface impacts the accuracy of its LCC estimation. Subsequently, the ratio of reflectance differences, that is, the modified Datt index, was tested to reduce the directional reflection effect when predicting LCC. Our results indicated that the modified Datt index not only estimated LCC with high accuracy for all observation directions and plant species but also consistently predicted the LCC of each species in individual observation directions. Our method opens the possibility for optical detection of LCC using multiangular spectral reflection, which is convenient for plant science studies focused on the variation in LCC.     

基于光谱指数的植物叶片叶绿素含量的估算模型

叶绿素是光合作用能力和植被发育阶段的指示器,是监测湿地植被生长健康状况的重要指标之一;高光谱遥感技术可以为植物叶绿素含量的定量化诊断提供简便有效、非破坏性的数据采集和处理方法。为保证被探测叶片面积相同,消除背景反射、叶片表面弯曲造成的光谱波动及叶片内部变异造成的影响,研究采用Field Spec 3光谱仪加载手持叶夹式叶片光谱探测器,测定野鸭湖湿地典型植物的叶片高光谱反射率数据,同时通过分光光度计室内测定相应叶片的叶绿素含量。采用相关性及单变量线性拟合分析技术,建立二者的关系模型,包括叶绿素含量与"三边"参数的相关模型以及比值光谱指数(SR)模型和归一化差值光谱指数(ND)模型,并采用交叉检验中的3K-CV方法对估算模型进行模型精度检验。结果表明:植物叶片叶绿素含量与"三边"参数大多都呈极显著相关,相关系数最大达到0.867;计算光谱反射率组成的比值(SR)和归一化(ND)光谱指数与叶绿素含量的决定系数,总体相关性比较高,较好的波段组合均为550—700nm与700—1400nm以及550—700nm与1600—1900nm,与叶绿素含量相关性最好的指数分别是SR(565nm,740nm)和ND(565nm,735nm)。并通过选取相关性最佳的光谱特征参数,分别基于"三边"参数和ND模型指数构建了植物叶片叶绿素含量的估算模型。其中,基于红边位置(WP_r)光谱特征参数和ND(565nm,735nm)光谱指数建立的叶绿素含量估算模型,取得了较好的测试效果,检验拟合方程的决定系数(R2)都达到0.8以上,估算模型分别为y=0.113x-78.74,y=5.5762x+4.4828。通过3K-CV方法进行测试和检验,植物叶绿素含量估算模型均取得了较为理想的预测精度,预测精度的分别为93.9%及90.7%。高光谱遥感技术对植被进行微弱光谱差异的定量分析,在植被遥感研究与应用中表现出强大优势,为植物叶绿素含量诊断中的实际应用提供了重要的理论依据和技术支持。     

于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系

利用空间遥感信息大面积监测小麦冠层氮素营养状况和生产力指标具有重要意义和应用前景.本研究基于不同施氮水平下小麦冠层反射光谱信息,利用响应函数模拟基于不同卫星通道构建的光谱指数(包括单波段、比值光谱指数和归一化光谱指数),分析基于星载通道的光谱指数与小麦冠层叶片氮素营养指标的定量关系,确定监测小麦冠层叶片氮素营养的较好卫星传感器和光谱波段,建立小麦冠层氮素营养指标监测方程.结果表明：利用NDVI(MSS₇, MSS₅)、NDVI(RBV₃, RBV₂)、TM₄、CH₂、MODIS₁和MODIS₂遥感数据可以预估小麦叶片氮含量(LNC),其决定系数（R₂）在0.60以上;应用NDVI(PB₄, PB₂)、NDVI(CH₂, CH₁)、NDVI(MSS₇, MSS₅)、RVI(MSS₇, MSS₅)、MODIS₁和MODIS₂可以预测小麦叶片氮积累量(LNA),其R₂大于0.86.比较而言,NDVI(MSS₇, MSS₅)和NDVI(PB₄, PB₂)分别为预测小麦LNC和LNA的适宜星载通道光谱参数.     

水稻叶片全氮浓度与冠层反射光谱的定量关系

利用数学统计方法分析了不同施氮水平和不同水稻品种群体叶片全氮浓度（LNC）与冠层反射光谱的定量关系,建立了水稻群体叶片全氮浓度的光谱监测模型.结果表明：基于原始反射率构造的光谱参数与叶片全氮浓度的相关程度均高于原始反射率,近红外波段（760～1 220 nm）与可见光波段510、560、680及710 nm组成的比值植被指数、差值植被指数和归一化植被指数与群体叶片全氮浓度呈极显著正相关,其中与归一化植被指数（NDVI）的相关性最好;对拟合较好的6个两波段组合参数及4个特征光谱参数的预测标准误（SE）和决定系数（R²）进行比较后,选取参数NDVI (1220, 710)为反演群体叶片全氮浓度的最佳光谱参数,方程为LNC=3.2708 × NDVI (1220,710) + 0.8654.利用不同粳稻品种、水分和氮肥处理的试验数据对监测模型进行了检验,估计的根均方差（RMSE）均小于20%,预测值和实测值的拟合R²为0.674～0.862,拟合斜率为0.908～1.010,RMSE为11.315%～19.491%,表明模型预测值与实测值之间符合度较高,对不同栽培条件下的水稻群体叶片全氮浓度具有较好的预测性.     

Some quantitative relationships between leaf area index and canopy nitrogen content and distribution

In a previous study (Yin et al. 2000. Annals of Botany 85: 579-585), a generic logarithmic equation for leaf area index (L) in relation to canopy nitrogen content (N) was developed: L=(1/ktn)1n(1+ktnN/nb). The equation has two parameters: the minimum leaf nitrogen required to support photosynthesis (nb), and the leaf nitrogen extinction coefficient (ktn). Relative to nb, there is less information in the literature regarding the variation of ktn. We therefore derived an equation to theoretically estimate the value of ktn. The predicted profile of leaf nitrogen in a canopy using this theoretically estimated value of ktn is slightly more uniform than the profile predicted by the optimum nitrogen distribution that maximizes canopy photosynthesis. Relative to the optimum profile, the predicted profile is somewhat closer to the observed one. Based on the L-N logarithmic equation and the theoretical ktn value, we further quantified early leaf area development of a canopy in relation to nitrogen using simulation analysis. In general, there are two types of relations between L and N, which hold for canopies at different developmental phases. For a fully developed canopy where the lowest leaves are senescing due to nitrogen shortage, the relationship between L and N is described well by the logarithmic model above. For a young, unclosed canopy (i.e. L < 1.0), the relation between L and N is nearly linear. This linearity is virtually the special case of the logarithmic model when applied to a young canopy where its total nitrogen content approaches zero and the amount of nitrogen in its lowest leaves is well above nb. The expected patterns of the L-N relationship are discussed for the phase of transition from young to fully developed canopies.     

稻麦叶片氮含量与冠层反射光谱的定量关系

作物氮素含量是评价作物长势、估测产量与品质的重要参考指标,叶片氮素含量的无损快速监测对于指导作物氮素营养的精确管理及生产力的预测预报具有重要意义.以5个小麦品种和3个水稻品种在不同施氮水平下的3a田间试验为基础,综合研究了稻麦叶片氮含量与冠层反射光谱的定量关系.结果显示：（1）不同试验中拔节后稻麦叶片氮含量均随施氮水平呈上升趋势;（2）稻麦冠层光谱反射率在不同施氮水平下存在明显差异,在可见光区（460～710 nm）的反射率一般随施氮水平的增加逐渐降低,而在近红外波段（760～1100 nm）却随施氮水平的增加逐渐升高;（3）就单波段光谱而言,610、660 nm和680 nm处的冠层反射率均与稻麦叶片氮含量具有较好的相关性;（4）在光谱指数中,归一化差值植被指数NDVI（1220,610）与水稻和小麦叶片氮含量均具有较好的相关性,且相关性好于单波段反射率;（5）对于小麦和水稻,可以利用共同的波段和光谱指数来监测其叶片氮含量,采用统一的回归方程来描述其叶片氮含量随单波段反射率和冠层反射光谱参数的变化模式,但若采用单独的回归系数则可以提高稻麦叶片氮含量估测的准确性.     

应用近红外光谱预测水稻叶片氮含量

以水稻(Oryza sativa)新鲜叶片和干叶粉末两种状态的样品为研究对象, 基于近红外光谱(NIRS)技术, 应用偏最小二乘法(PLS)、主成分回归(PCR)和逐步多元回归(SMLR), 建立并评价了水稻叶片氮含量(NC)近红外光谱模型。结果表明, 基于PLS建立的模型表现最好, 鲜叶氮含量近红外光谱校正模型校正决定系数R_C²为0.940, 校正标准误差RMSEC为0.226; 干叶粉末氮含量的近红外光谱校正模型R_C²为0.977, RMSEC为0.136。模型的内部交叉验证分析表明, 预测鲜叶氮含量内部验证决定系数R_CV²为0.866, 内部验证标准误差RMSECV为0.243; 预测干叶粉末氮含量R_CV²为0.900, RMSECV为0.202。模型的外部验证分析表明, 预测水稻鲜叶氮含量的外部验证决定系数R_V²大于0.800, 外部验证标准误差RMSEP小于0.500, 预测干叶粉末氮含量的R_V²为0.944, RMSEP为0.142。说明, 近红外光谱分析技术与化学分析方法一致性较好, 且基于干叶粉末建立的近红外光谱预测模型的准确性和精确度较新鲜叶片高。     

基于光谱指数的喀斯特植物叶片叶绿素含量定量估算

为了探讨适合于喀斯特植物叶片叶绿素含量估算的光谱指数,在总结以往基于光谱指数的植物生化参数估算研究基础上发现,常用光谱指数通常采用差值、比值、归一化以及倒数差值方式来构建。因此,我们通过上述4种光谱指数构建方式对所采集的4种典型喀斯特植物——黄荆(Vitex negundo)、盐麸木(Rhus chinensis)、朴树(Celtis sinensis)和红背山麻杆(Alchornea trewioides)叶片原始光谱反射率及其一阶导数值与同步测定的叶片叶绿素含量进行遍历分析,以期获得最优光谱指数并将其应用于喀斯特植物叶片叶绿素含量定量估算研究。结果表明：(1)常用光谱指数中,改良红边归一化指数(modified red-edge normalized difference vegetation index, mND705)对喀斯特植物叶片叶绿素含量估算效果较好(决定系数为0.45,均方根误差为0.26 mg·g^-1)。(2)虽然荧光比值(fluorescence ratio index, FRI1)和叶绿素吸收面积光谱指数(chlorophyll absorp...     

Coordination theory of leaf nitrogen distribution in a canopy

It has long been observed that leaf nitrogen concentrations decline with depth in closed canopies in a number of plant communities. This phenomenon is generally believed to be related to a changing radiation environment and it has been suggested by some researchers that plants allocate nitrogen in order to optimize total whole canopy photosynthesis. Although optimization theory has been successfully utilized to describe a variety of physiological and ecological phenomena, it has some shortcomings that are subject to criticism (e.g., time constraints, oversimplifications, lack of insights, etc.). In this paper we present an alternative to the optimization theory of plant canopy nitrogen distribution, which we term coordination theory. We hypothesize that plants allocate nitrogen to maintain a balance between two processes, each of which is dependent on leaf nitrogen content and each of which potentially limits photosynthesis. These two processes are defined as W_c, the Rubiscolimited rate of carboxylation, and W_j, the electron transport-limited rate of carboxylation. We suggest that plants allocate nitrogen differentially to, leaves in different canopy layers in such a way that W_c and W_j remain roughly balanced. In this scheme, the driving force for the allocation of nitrogen within a canopy is the difference between the leaf nitrogen content that is required to bring W_c and W_j into balance and the current nitrogen content. We show that the daily carbon assimilation of a canopy with a nitrogen distribution resulting from this internal coordination of W_c and W_j is very similar to that obtained using optimization theory.     

Changes in leaf photosynthetic parameters with leaf position and nitrogen content within a rose plant canopy (Rosa hybrida)

This paper deals with changes in leaf photosynthetic capacity with depth in a rose (Rosa hybrida cv. Sonia) plant canopy. Measurements of leaf net CO₂ assimilation (A_l) and total nitrogen content (N_l) were performed in autumn under greenhouse conditions on mature leaves located at different layers within the plant canopy, including the flower stems and the main shoots. These leaves were subjected (i) to contrasting levels of CO₂ partial pressure (p_a) at saturating photosynthetic photon flux density (I about 1000 μ mol m ^{? 2} s ^{? 1}) and (ii) to saturating CO₂ partial pressure (p_a about 100 Pa) and varying I, while conditions of temperature were those prevailing in the greenhouse (20–38 °C). A biochemical model of leaf photosynthesis relating A_l to intercellular CO₂ partial pressure (p_i) was parameterized for each layer of leaves, supplying corresponding values of the photosynthetic Rubisco capacity (V_lm) and the maximum rate of electron transport (J_m). The results indicated that rose leaves growing at the top of the canopy had higher values of J_m and V_lm, which resulted from a higher allocation of nitrogen to the uppermost leaves. Mean values of total leaf nitrogen, N_l, decreased about 35% from the uppermost leaves of flower stem to leaves growing at the bottom of the plant. The derived values of non‐photosynthetic nitrogen, N_b, varied from 76 mmol_N m ^{? 2}_leaf (layer 1) to 60 mmol_N m ^{? 2}_leaf (layer 4), representing a large fraction of N_l (50 and 60% in layer 1 and 4, respectively). Comparison of leaf photosynthetic nitrogen (N_p = N_l–N_b) and I profiles supports the hypothesis that rose leaves acclimate to the time‐integrated absorbed I. The relationships between I and N_p, obtained during autumn, spring and summer, indicate that rose leaves seem also to acclimate their photosynthetic capacity seasonally, by allocating more photosynthetic nitrogen to leaves in autumn and spring than in summer.     

Non-destructive determination of maize leaf and canopy chlorophyll content

The objective of this study was to develop a rapid non-destructive technique to estimate total chlorophyll (Chl) content in a maize canopy using Chl content in a single leaf. The approach was (1) to calibrate and validate a reflectance-based non-destructive technique to estimate leaf Chl in maize; (2) to quantify the relative contribution of each leaf Chl to the total Chl in the canopy; and (3) to establish a relationship between leaf Chl content and total Chl in a maize canopy. The Red Edge Chlorophyll Index CI(red edge)=(R(NIR)/R(red edge))-1 based on reflectances, R, in the red edge (720-730nm) and near infrared (770-800nm) was found to be an accurate measure of maize leaf Chl. It was able to predict leaf Chl ranging from 10 to 805mgChlm(-2) with root mean-square error less than 38mgChlm(-2). Relationships between Chl content in each maize leaf and total canopy Chl content were established and showed that Chl in the collar leaf before silking or ear leaves explained more than 80% and 87% of the variation in total Chl in a maize canopy, respectively. Thus, non-destructive measurements of both reflectance and area of a single leaf (either collar or ear) can be used to accurately estimate total Chl content in a maize canopy.     

Seasonal relationships between leaf nitrogen content (photosynthetic capacity) and leaf canopy light exposure in peach (Prunus persica)

Abstract Field gas exchange measurements on intact peach (Prunus persica (L.) Batsch) leaves indicate that leaf nitrogen content (N_L) and leaf weight per unit leaf area (W_a) are highly correlated with CO₂ assimilation rate (A) and mesophyll conductance (g_m). Therefore, N_L and W_a were used to study seasonal relationships between leaf carboxylation capacity and natural light exposure in tree canopies. From mid-season onwards, N_L and W_a were linearly correlated with light exposure expressed as the amount of time during a clear day that a leaf was exposed to a photosynthetic photon flux density (Q) of ≥ 100 μmol m^?2 s^?1. The data support the hypothesis that whole-tree photosynthesis is optimized by partitioning of photosynthetic capacity among leaves in deciduous tree canopies with respect to natural light exposure.     

Predicting leaf traits of herbaceous species from their spectral characteristics

Trait predictions from leaf spectral properties are mainly applied to tree species, while herbaceous systems received little attention in this topic. Whether similar trait–spectrum relations can be derived for herbaceous plants that differ strongly in growing strategy and environmental constraints is therefore unknown. We used partial least squares regression to relate key traits to leaf spectra (reflectance, transmittance, and absorbance) for 35 herbaceous species, sampled from a wide range of environmental conditions. Specific Leaf Area and nutrient‐related traits (N and P content) were poorly predicted from any spectrum, although N prediction improved when expressed on a per area basis (mg/m² leaf surface) instead of mass basis (mg/g dry matter). Leaf dry matter content was moderately to good correlated with spectra. We explain our results by the range of environmental constraints encountered by herbaceous species; both N and P limitations as well as a range of light and water availabilities occurred. This weakened the relation between the measured response traits and the leaf constituents that are truly responsible for leaf spectral behavior. Indeed, N predictions improve considering solely upper or under canopy species. Therefore, trait predictions in herbaceous systems should focus on traits relating to dry matter content and the true, underlying drivers of spectral properties.     

Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress

Maize is one of the most widespread grain crops in the world; however, more than 70% of corn in China suffers some degree of drought disaster every year. Leaf area index (LAI) is an important biophysical parameter of the vegetation canopy and has important significance for crop yield estimation. Using the data of canopy spectral reflectance and leaf area index (LAI) for maize plants experiencing different levels of soil moisture from 2011 to 2012, the characteristics of the canopy reflective spectrum and its first derivative, and their relationships to leaf area index, were analyzed. Soil moisture of the control group was about 75% while that of the drought stress treatment was about 45%. In addition, LAI retrieval models for maize were established using vegetation indices (VIs) and principal component analysis (PCA) and the models were tested using independent datasets representing different soil water contents and different developmental stages of maize. The results showed that canopy spectral reflectances were in accordance with the characteristics of green plants, under both drought stress and at different developmental stages. In the visible band, canopy reflectance for both healthy and damaged vegetation had a green-wavelength peak and a red-wavelength valley; reflectance under drought stress, especially in the green peak (about 550 nm) and the red valley (about 676 nm) was higher than in the control group. In the near-infrared band, the canopy spectral reflectance decreased substantially between 780 and 1350 nm under drought stress. Moreover, the red edge of the spectrum was shifted toward blue wavelengths. The first derivative spectrum showed a double peak phenomenon at the edge of the red band at different developmental stages: the main peak appeared between 728 and 732 nm and the minor peak at about 718 nm. The double peaks become more obvious through the growth and development of the maize, with the most notable effect during the silking and milk stages, after which it gradually decreased. During maize growth, the LAI of all plants, regardless of soil moisture conditions, increased, and the largest LAI also occurred during the silking and milk stages. During those stages, the LAI of plants under different drought stress levels was significantly lower (by 20% or more) than in normal plants with sufficient water supplies. The LAI was highly significantly correlated with canopy spectral reflectance in the bands from 350 nm to 510 nm, from 571 nm to 716 nm, and from 1450 nm to 1575 nm. Also, the LAI was significantly correlated with red edge parameters and several VIs. The Perpendicular Vegetation Index (PVI) had the best correlation with LAI, with a coefficient of determination (R²) of 0.726 for the exponential correlation. Using dependent data, a LAI monitoring model for the maize canopy was constructed using PCA and VI methods. The test results showed that both the VI and PCA methods of monitoring maize LAI could provide robust estimates, with the predicted values of LAI being significantly correlated with the measured values. The model based on PVI showed higher precision under the drought stresses, with a correlation coefficient of 0.893 (n = 27), while the model based on PCA was more precise under conditions of adequate soil moisture, with a correlation coefficient of 0.877 (n = 32). Therefore, a synthesis of the models based on both VI and PCA could be more reliable for precisely predicting LAI under different levels of drought stresses in maize.