黑麦草生长过程中有机酸对镉毒性的影响

研究了低分子量有机酸草酸、柠檬酸、乙酸及高分子量有机酸胡敏酸对黑麦草(Lolium Loinn)生长过程中Cd毒性的影响.结果表明,随着低分子量有机酸浓度增加,Cd毒性有所增强,致使黑麦草中的叶绿素含量降低及黑麦草的生物量降低,递降顺序是草酸＜乙酸＜柠檬酸.而施入胡敏酸后,Cd毒性逐渐减弱,黑麦草中的叶绿素含量及黑麦草生物量逐渐增加.对低分子量有机酸而言,无论迁移到黑麦草茎叶中,还是迁移到黑麦草根系中的Cd,随着施入的有机酸浓度增加,增加顺序为柠檬酸＞乙酸＞草酸.对胡敏酸而言,迁移到黑麦草茎叶和根系中的Cd,随着施入的胡敏酸浓度增加,Cd含量减少,说明其具有降低Cd毒性的作用.另外,根系中Cd含量明显高于茎叶中Cd含量,由此得知,黑麦草根系对Cd有较强的富集作用,并阻止Cd向茎叶中迁移.     

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.     

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.     

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.     

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.     

稻麦叶片氮积累量与冠层反射光谱的定量关系

作物氮素积累动态是评价作物群体长势及估测产量和品质的重要指标,对于作物氮素的实时监测和精确管理具有重要意义。该文以5个小麦(Triticum aestivum)品种和3个水稻(Oryza sativa)品种在不同施氮水平下的3年田间试验为基础,综合研究了稻麦叶片氮积累量与冠层反射光谱的定量关系。结果表明,不同试验中拔节后叶片氮积累量均随施氮水平呈上升趋势;稻麦冠层光谱反射率在不同施氮水平下存在明显差异,可见光区(460~710 nm)反射率一般随施氮水平的增加逐渐降低,近红外波段(760~1 220 nm)反射率却随施氮水平的增加逐渐升高;就单波段而言,810和870 nm处的冠层光谱反射率均与稻麦叶片氮积累量具有相对较高的相关性;在光谱参数中,比值植被指数(Ratio vegetation index, RVI)(870,660)和RVI(810,660)均与稻麦叶片氮积累量具有高度的相关性,且相关系数明显高于单波段反射率,尤其是水稻作物;对于小麦和水稻,均可以利用统一的波段和光谱指数来监测其叶片氮积累量,并可以采用统一的回归方程来描述其叶片氮积累量随单波段反射率和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积累量估测的准确性。     

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.     

水稻叶片气孔导度与冠层反射光谱的定量关系分析

研究了不同土壤水氮条件下水稻(Oryza sativa)叶片气孔导度与冠层光谱反射特征的量化关系。结果表明,不同水分处理下,水稻不同叶位气孔导度变化趋势为:GsL1>GsL2>GsL3>GsL4。高于W3水分条件下,高氮处理的叶片气孔导度高于低氮处理,而低于W3水分条件下,高低氮处理条件下叶片气孔导度差异不显著。发现比值指数R(1 650,760)与不同叶位叶片及不同层次叶片气孔导度的相关性大小为:GsL1>GsL12>GsL123>GsL1234>GsL2>GsL3>GsL4(水稻顶部自上而下第一、二、三、四叶以及自上而下顶部2张、3张、4张叶片的气孔导度值分别表示为:GsL1、GsL2、GsL3、GsL4、GsL12、GsL123和GsL1234),而顶1叶气孔导度与叶面积指数的乘积(冠层叶片气孔导度)同比值指数R(1 650,760)相关程度更高。R(1 650,760)与顶1叶和冠层叶片气孔导度之间皆呈极显著的幂函数负相关。利用不同年份的不同水稻试验对两者的监测模型进行了检验,模型的检验误差RMSE分别为0.05和0.24,表明比值指数R(1 650,760)可以较好地监测不同水氮条件下水稻叶片的气孔开闭特征。     

利用冠层光谱估测烟草叶面积指数和地上生物量

综合多种烟草类型、品种及肥料处理因素,分析了17种光谱参数与烟草叶面积指数（LAI）、地上鲜生物重（AFW）、地上干生物重（ADW）的关系,建立逐步回归模型对烟草LAI、AFW、ADW进行估测并结合相关分析筛选出相应的特征变量。结果表明：5个回归方程的复确定系数R^2、回归系数相伴概率均达到显著水平。包含17个光谱参量的逐步回归方程筛选出的第一自变量均为Rg／Rr,相关分析及散点图分析亦得出Rg／Rr与LAI、AFW、ADW相关系数分别为0．759、0．611、0．647,R^2为0．576、0．3727、0．4184,均达到极显著水平,证明烟草LAI、AFW、ADW的特征变量为Rg／Rr。仅采用8种植被指数建立模型,证明利用比值植被指数（RVI）估测LAI、ADW亦是可行的。经过统计检验,建立的模型估测效果均较好,估测值与实测值的相关性均达到显著水平,其中包含特征变量Rg／Rr的回归模型估测效果优于RVI构建的模型。表明采用高分辨率光谱或宽波段光谱提取光谱变量可对烟草LAI、AFW、ADW进行监测,并可根据数据条件选择有效的估测模型,为烟草遥感数据分析提供方法。     

小麦冠层反射光谱与植株水分状况的关系

研究了不同土壤水、氮条件下小麦冠层光谱反射特征与叶片和植株水分状况的相关性．结果表明,在小麦主要生育期,冠层叶片含水率与460～510、610～680和1480～1500nm波段范围内的光谱反射率有较高的相关性,植株含水率与810～870nm波段范围内的光谱反射率密切相关．在整个生长期内,小麦冠层叶片含水率与460～1500nm波段范围内的光谱反射率均有良好相关性,植株含水率与560～1480nm波段范围内光谱反射率的相关性均达到极显著水平．冠层叶片(CL)、上层叶(UL)和下层叶片(LL)含水率与光谱指数的相关程度为CL>LL>UL．冠层叶片和植株含水率与比值指(R(610,560))和光谱指数(R(610,560)／ND(810,610))呈极显著线性负相关,与归一化指数((R810-R610)／(R810+R610))呈极显著线性正相关．其中,用光谱指数(R(610,560)／ND(810,610))监测不同生育期小麦冠层叶片和植株含水率的效果最好。     

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.     

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.     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.