在水分胁迫下棉花冠层叶片全氮含量的高光谱遥感估算模型研究

研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:①随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;②.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;③由一阶微分光谱衍生出基于光谱“红边”位置变量的分析方法,使我们认识到“红边”的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;④如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。     

在水分胁迫下棉花冠层叶片全氮含量的高光谱遥感估算模型研究

研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:①随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;②.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;③由一阶微分光谱衍生出基于光谱“红边”位置变量的分析方法,使我们认识到“红边”的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;④如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。     

基于HJ星高光谱数据红边参数的冬小麦叶面积指数反演

针对我国HJ-1A星搭载的高光谱成像仪(HSI)数据,探索基于HJ星高光谱影像的LAI反演研究,本文利用inverted Gaussian模型提取红谷位置、红边位置、红边振幅以及红边斜率4个红边参数,结合2009年4月、5月两期同步地面观测LAI数据,经过回归分析构建了反演叶面积指数的最优红边参数模型.结果表明红边位置、红边斜率和红边振幅与叶面积指数都达到了极显著相关,R2分别为0.5592,0.7796和0.8107说明HJ星高光谱影像数据在叶面积指数反演方面有很大的应用潜力.     

冬小麦冠层光谱红边参数的变化及其与氮素含量的相关分析

通过对不同氮肥条件下的小麦植株由上而下进行器官疏剪,分析了不同处理下冠层光谱反射率及其红边参数的变化。结果表明,冠层光谱反射率因不同肥力、不同疏剪处理而有较大的差异,表现出不同程度的红边的“红移”和“蓝移”现象。各处理的红边曲线形状均出现双峰现象,表现为第二个峰值高于第一个峰值,并且均为N1>N2>N0。相关分析表明,随着由上而下的疏剪处理,不同叶位叶片光谱反射率对冠层光谱的贡献增加,并且其红边参数与相应的叶片全氮含量的相关系数也增加,部分达到显著或极显著相关水平。该结果为利用下部缺素敏感叶片的光谱特征进行小麦养分的及时补充提供了可靠的理论依据。     

基于植被指数的叶绿素密度遥感反演建模与适用性研究

利用遥感数据反演叶绿素密度是对作物长势进行评估的有效手段.本文利用实测冬小麦和夏玉米两种作物、不同生育期的冠层光谱和叶片叶绿素含量数据,收集了14种光谱指数,分析各种光谱指数的叶绿素密度遥感模型的精度.优选了其中的8种植被光谱指数,建立了植被指数与叶绿素密度之间的回归模型,并利用不同生育期小麦数据和玉米数据对各模型进行验证,分析评价它们对不同生育期、不同作物类型的适用性.研究发现:利用SRI、RVI I、R-M和MTCI 4种植被指数所建模型对冬小麦不同生育期数据适用性较好,各生育期冠层叶绿素密度反演相对误差优于27％.其中,MTCI模型对不同作物类型的适用性最好,冠层叶绿素密度反演相对误差优于35％.     

利用统计和物理模型反演植被叶片生化组分

通过比较统计模型、物理模型以及两者的联合模型在反演鲜叶片生化组分含量时的效果，结果表明，对于叶绿素知水份含量。物理模型的反演效果较好，对于蛋白质、纤维素 木质素含量，统计模型的反演效果相对较好，由物理模型改造得到的三种联合模型，能在一定程度上提高物理模型反演蛋白质、纤维素 木质素含量的精度，但和利用统计模型良演这两种生化组分的结果比较，改进作用并不明显。对于不同样本组叶片生化组分含量，不同模型反演效果均存在一定差异，统计模型存在的差异相对较大。     

Study on Grain Quality Effecting Factors and MonitoringMethods by Using Hyperspectral Data in Winter Wheat

冬小麦品质的影响因素及高光谱遥感监测方法黄文江,王纪华,刘良云,赵春江,宋晓宇,马智宏(国家农业信息化工程技术研究中心,北京　100089)摘要:研究了小麦品质的分类及其构成因素与环境条件之间的关系,各品质因素之间的关系。运用相同栽培条件下不同品种品质指标间的关系和变化规律,研究了品种因素对小麦品质的影响程度以及品种因素与品质指标之间的相关性,得出相同环境条件下籽粒的蛋白质含量与湿面筋含量、沉降值、吸水率、形成时间和稳定时间之间存在极显著的相关性。并利用不同品种、不同肥水条件下的作物关键生育时期的生化参量与光谱指数进行分析,得出开花期冬小麦叶片的类胡萝卜素与叶绿素a的比值与结构不敏感植被指数(SIPI)之间存在极显著的正相关,决定系数达到0.7207,冬小麦体内的全氮含量与类胡萝卜素与叶绿素a的比值之间存在极显著的负相关,决定系数为0.7245,并通过分析开花期冠层生化组分与籽粒品质指标间的相关性,得出开花期叶片全氮与籽粒蛋白质、湿面筋、干面筋和沉降值之间存在极显著的正相关,表面运用开花期光谱指数来反演叶片全氮含量,进而用来预测预报籽粒品质是切实可行的。关　键　词:冬小麦;高光谱数据;籽粒品质;监测方法中图分类号:TP 79     

冬小麦品质的影响因素及高光谱遥感监测方法

研究了小麦品质的分类及其构成因素与环境条件之间的关系，各品质因素之间的关系。运用相同栽培条件下不同品种品质指标间的关系和变化规律，研究了品种因素对小麦品质的影响程度以及品种因素与品质指标之间的相关性，得出相同环境条件下籽粒的蛋白质含量与湿面筋含量、沉降值、吸水率、形成时间和稳定时间之间存在极显著的相关性。并利用不同品种、不同肥水条件下的作物关键生育时期的生化参量与光谱指数进行分析，得出开花期冬小麦叶片的类胡萝卜素与叶绿素a的比值与结构不敏感植被指数(SIPI)之间存在极显著的正相关，决定系数达到0．7207，冬小麦体内的全氮含量与类胡萝卜紊与叶绿素口的比值之间存在极显著的负相关，决定系数为0．7245，并通过分析开花期冠层生化组分与籽粒品质指标间的相关性，得出开花期叶片全氮与籽粒蛋白质、湿面筋、干面筋和沉降值之间存在极显著的正相关，表面运用开花期光谱指数来反演叶片全氮含量，进而用来预测预报籽粒品质是切实可行的。     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

基于GA的GRNN高光谱遥感反演冬小麦叶片氮含量模型的建立与验证

基于人工神经网络理论,针对高光谱遥感中数据冗余问题,本文建立了基于遗传算法(GA)的广义回归神经网络(GRNN)模型,利用回归分析问题中参数筛选方法,对表征冬小麦叶片全氮的光谱参数进行了筛选,并和线性回归方法对比,线性回归方法的均方根误差(RMSEP):在冬小麦叶片氮含量为34.0g kg-1~62.5g kg-1预测范围内,逐步回归模型为14.4g kg-1,后向选择为11.8g kg-1,而广义回归神经网络为3.40g kg-1。说明神经网络方法所筛选到的光谱参数更能反映小麦叶片全氮含量,且神经网络模型预测精度高。     

Estimating winter wheat plant water content using red edge parameters

Remote sensing of plant water content is difficult because the absorption band sensitive to foliar liquid water is also sensitive to the atmospheric vapour. A method using non-water-absorption spectral parameters to evaluate plant water content (PWC) would be valuable. In our experiment, canopy spectra of 48 winter wheat treatments with different varieties, different fertilization and irrigation levels were measured by an ASD FieldSpec FR spectrometer in six different growth stages from erecting stage to milking stage, and the PWCs of the related wheat plant samples were also measured. Significant positive coefficients of correlation were observed between PWC and spectral reflectance in 740–930?nm region in all of the six different growth stages, which indicates that the NIR spectral reflectance increases due to the effect of PWC on the leaf internal structure. This mechanism also affects the red edge spectrum in 680–740?nm region. The spectral reflectance increases more rapidly and the red edge becomes steeper if PWC is higher. The coefficients of correlation between PWC and red edge width, derived from the inverted-Gaussian model, are significant at the 0·999 confidence-level, which is more reliable than WI and NDWI, and the statistical models for PWC based on red edge width were set up in all the six different growth stages. In addition, LAI and canopy chlorophyll density (CCD) are also related to red edge parameter, such as red edge position and red edge width. It seems that PWC plays a more important role in red edge width than LAI and CCD due to the effect of PWC on the leaf internal structure, and that CCD plays a more important role in red edge position than LAI and PWC.     

Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

浅述植被“红边”效应及其定量分析方法

红边(REP)是绿色植物叶子光谱曲线在680nm～740nm之间变化率最快的点,也是一阶导数光谱在该区间内的拐点。本文总结了红边参数的种类,红边在植物种类的识别、植物时相的识别、植物生物参数估测和植物生长状况监测等方面的应用,并介绍了红边参数其适用范围和使用方法等,阐述了红边在植被研究中的重要性,分析了植被红边技术的发展方向和应用前景;同时总结了线性内插模型、反高斯模型、拉格朗日模型、多项式模型和有理函数新模型等五种红边定量分析方法及应用,以及它们的适用范围等,并介绍了G.V.G.BARANOS-KI and J.G.ROKNE采用有理函数新模型分析过程以确定红边位置的"新"方法。     

The MERIS terrestrial chlorophyll index

The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data.     

Improving leaf area index retrieval using spectral characteristic parameters and data splitting

ABSTRACT

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented ‘blue shifts’, the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R²) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis.     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

那曲典型草地植被光谱特征分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。地面实测数据由于其高空间分辨率和高光谱分辨率,能够准确反映地物光谱信息,可以用来指导卫星遥感解译工作,同时为遥感监测草地退化、草地模型建立等提供数据支持。选取西藏那曲地区的优势植被类型作为研究对象,利用ASD FieldSpec 3便携式光谱仪测定优势种的冠层光谱并进行比较,并取其中一种优势种测量其在不同覆盖度和不同生长期的光谱反射特点。研究结果表明:①不同植被群落冠层光谱具有特殊的光谱曲线,可见光波段光谱反射率依次是紫花针茅、小嵩草和藏北嵩草,近红外波段光谱反射率则依次是小嵩草、藏北嵩草和紫花针茅;红边位置可以识别藏北嵩草,但是不能区分小嵩草和紫花针茅;②不同覆盖度的小嵩草红边、“绿峰”位置不随覆盖度的变化而发生变化;连续统去除后得到吸收深度随覆盖度的增加而变大,吸收峰面积随覆盖度的增加而增加;③小嵩草衰退期内,在可见光波段和红边波段,冠层光谱反射率随着叶绿素含量的减少而下降,出现“红边蓝移,绿峰下降”的现象。     

基于蚁群优化算法的遥感器波段设置探究

卫星载荷研制发射后其光谱和空间观测模式固定,无法根据复杂地表的多样化需求进行实时灵活调整,且目前遥感器波段设置尚不完善还存在优化空间。引进基于蚁群优化算法的波段选择方法(Ant Colony Optimization-based Band Selection,ACOBS),结合北美区域33景AVIRIS航空高光谱图像,开展了不同区域、不同地表覆盖类型的高光谱波段优选研究,发现各地表类型优选波段组合存在一定差异,其中4波段组合中红光、近红外波段为2个共同入选波段,6波段组合中绿光、红光、短波红外波段为3个共有波段,8波段组合中紫光、绿光、红光、红边、近红外1、近红外2、短波红外1、短波红外2为8个共有入选波段,其他入选波段与地表覆盖类型有关。在此基础上,进一步开展了多光谱卫星波段设置评价研究,发现:4波段优化方案中,绿光、红光、近红外波段1(770~895nm)、近红外波段2(900~1 350nm)为最优波段组合;6波段优化方案中,绿、红、红边、近红外1(770~895nm)、近红外2(900~1 350nm)、短波红外1(1 560~1 660nm)为最优波段组合;8波段优化方案中,蓝、绿、红、红边、近红外1(770~895nm)、近红外2(900~1 350nm)、短波红外1(1 560~1 660nm)和短波红外2(2 100~2 300nm)为最优波段组合。研究结果表明Landsat TM/OLI、SPOT等陆地资源遥感器波段设置还存在一定优化调整空间,特别是红边波段在目前传感器波段设置中没有得到足够重视。     

A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (A_REP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R² = 0.89) was found between A_REP and dry grain yield (Q ha^?1) up to 50 Q ha^?1 of wheat crop, whereas, beyond this range the relationship was found to be diminishing.