Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.     

Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

This study examines the potential of hyperspectral sensor systems for the non-destructive detection and differentiation of plant diseases. In particular, a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data. Reflectance spectra (400–1050 nm) of leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew and rust, respectively, were recorded repeatedly during pathogenesis with a spectro-radiometer and analyzed for disease-specific spectral signatures. Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures. The best correlating spectral bands differed depending on the diseases. Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases. The spectral vegetation indices Normalised Difference Vegetation Index (NDVI), Anthocyanin Reflectance Index (ARI) and modified Chlorophyll Absorption Integral (mCAI) differed in their ability to assess the different diseases at an early stage of disease development, or even before first symptoms became visible. Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination.     

Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors

The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality.

     

Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.

     

Hyperspectral image analysis for water stress detection of apple trees

Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress would allow for a timely intervention and mitigation of the problem before critical thresholds were exceeded, thereby maximizing productivity. The spectral signature of plant leaves was analyzed by a hyperspectral camera to identify the onset and intensity of plant water stress. Five different levels of water treatment were created in young apple trees (cv. ‘Buckeye Gala’) in a greenhouse. The trees were periodically monitored with a hyperspectral camera along with an active-illuminated spectral vegetation sensor and a digital color camera. Individual spectral images over a 385-1000 nm wavelength range were extracted at a specific wavelength to estimate reflectance and generate spectral profiles for the five different water treatment levels. Various spectral indices were calculated and correlated to stress levels. The highest correlation was found with Red Edge NDVI at 705 and 750 nm in narrowband indices and NDVI at 680 and 800 nm in broadband indices. The experimental results indicated that intelligent optical sensors could deliver decision support for plant stress detection and management.     

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R₇₀₀/R₆₇₀) and 57% (R₄₁₈/R₄₀₅), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R₇₆₃/R₇₆₁), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R₃₇₀ and R₄₀₀ as either simple ratio or a normalized difference index performed most consistently in both experimental (R ² = 0.58) and farmers’ fields (R ² = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.     

Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet

Diseases caused by nematodes and non-sporulating soil-borne fungi have low mobility and are likely to be suitable targets for precision agriculture applications. Sensors which assess the reflectance of plant leaves may be useful tools to detect soil-borne pathogens. The development of symptoms caused by the plant parasitic nematode Heterodera schachtii and the fungal pathogen Rhizoctonia solani anastomosis group 2-2IIIB alone or in combination was studied by leaf reflectance recorded with a hyperspectral imaging system (range 400–1000 nm) for 9 weeks twice per week. Three image processing methods were tested for their suitability to generate the most sensitive spectral information for disease detection. Nine spectral vegetation indices were calculated from spectra to correlate them to leaf symptom recordings. Supervised classification by spectral angle mapper was tested for the discrimination of leaf symptoms caused by the diseases. The symptoms of Rhizoctonia crown and root rot caused by R. solani and symptoms caused by H. schachtii induced modifications that could be detected by hyperspectral image analysis. Rhizoctonia crown and root rot symptom development in mixed inoculations was faster and more severe than in single inoculations, indicating complex interactions among fungus, nematode and plant. The results from this study under controlled conditions are currently used to transfer the sensor technology to the field.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize

In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (≥0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10–V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.     

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and that the modified spectral derivative analysis had a better performance than vegetation indices.     

Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R ² values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R ² values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied.     

基于无人机多光谱和热红外影像信息融合的小麦白粉病监测

[目的]白粉病严重危害小麦生长及制约产量形成,确立实时监测小麦白粉病的多源数据融合方法,为精确防控及保证国家粮食安全提供技术支撑.[方法]在小麦开花和灌浆期,使用同时搭载多光谱仪和热成像仪的六旋翼无人机作为遥感数据获取平台,通过ENVI软件从小麦白粉病遥感影像中提取植被指数、纹理特征以及冠层温度信息,进而利用多元线性回...     

Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system

Classification of oil palm fresh fruit bunch (FFB) maturity is a critical factor that dictates the quality of produced palm oil. This study evaluates a multi-band portable, active optical sensor system; comprising of four spectral bands, 570, 670, 750, and 870 nm, to detect oil palm FFB maturity. The in-field spectral reflectance data were collected using the sensor system from a total of 120 fresh fruit bunches. These fruit bunches were categories into unripe, ripe, and overripe classes. Different classifiers were applied to assess the applicability of using the sensor system. Based on the classification accuracies, data analysis on the spectral features (reflectance data and other features extracted from vegetation indices) indicated that the spectral reflectance data could be valuable in predicting the maturity of the fruit bunches. The quadratic discriminant analysis and discriminant analysis with Mahalanobis distance classifiers yielded highest average overall accuracies of greater than 85% in classifying oil palm FFB maturity. Additionally, the average individual class (unripe, ripe, and overripe) classification accuracies were also higher than 80%. Thus, optical sensing using four-band sensor system could be useful for oil palm FFB maturity classification under field condition.     

Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves

This paper investigated the possibility of discriminating tomato yellow leaf curl disease by a hyperspectral imaging technique. A hyperspecral imaging system collected hyperspectral images of both healthy and infected tomato leaves. The reflectance spectra, first derivative reflectance spectra and absolute reflectance difference spectra in the wavelength range of 500–1000 nm of both background and the leaf area were analyzed to select sensitive wavelengths and band ratios. 853 nm was selected to create a mask image for background segmentation, while 720 nm from the reflectance spectra, four peaks (560, 575, 712, and 729 nm) from the first derivative spectra and, four wavelengths with higher values (586, 720 nm) and lower values (690, 840 nm) in the absolute difference spectra were selected as a set of sensitive wavelengths. Four band ratio images (560/575, 712/729, 586/690, and 720/840 nm) were compared with four widely used vegetation indices (VIs). 24 texture features were extracted using grey level co-occurrence matrix (GLCM), respectively. The performance of each feature was evaluated by receiver operator characteristic (ROC) curve analysis. The best threshold values of each feature were calculated by Yonden’s index. Mean value of correlation (COR_MEAN) extracted from the band ratio image (720/840 nm) had the best performance, whose AUC value was 1.0. The discrimination result for a validation set based on its best threshold value was 100%. This research also demonstrated that multispectral images at 560, 575 and 720 nm have a potential for detecting tomato yellow leaf curl virus infection in field applications.     

基于高光谱植被指数的马铃薯叶片叶绿素含量估测模型

【目的】筛选相关性好的植被指数构建马铃薯叶片叶绿素a、叶绿素b估测模型,为科学、无损地进行马铃薯叶片叶绿素含量估算提供技术支撑。【方法】采用便携式高光谱地物波谱仪,获取不同施氮水平下不同生育时期的马铃薯植株叶片光谱反射率,提取植被指数,测定马铃薯叶片叶绿素a、叶绿素b含量,并研究叶绿素含量与植被指数的相关性。【结果】12个植被指数与叶绿素a、叶绿素b含量相关性较好,其中修正归一化差异指数(mND_(705))、修正简单比值指数(mSR_(705))、地面叶绿素指数(MTCI)、修改叶绿素吸收反射指数(MCARI)与叶绿素a、叶绿素b含量相关性最好。基于这4个植被指数建立的估测模型中,MTCI构建的乘幂模型估测叶绿素a含量的效果最佳,mND_(705)构建的指数模型估测叶绿素b含量的效果最佳。【结论】MTCI构建的乘幂模型能较为精确地估测叶绿素a含量,mND_(705)构建的指数模型能较为精确地估测叶绿素b含量;这2种模型可用于间接监测马铃薯植株的氮营养亏缺状态。     

Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies

A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position). Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.     

Assessment of the spatial variability of anthocyanins in grapes using a fluorescence sensor: relationships with vine vigour and yield

The use of new, rapid and non-invasive sensors in the field allows the collection of many observations which are necessary to assess the spatial variability of berry composition. The aim of this work was to study the spatial variability in anthocyanin content in grapes and to quantify its relationship with the vigour and yield in a commercial vineyard. The study was conducted in a Tempranillo (Vitis vinifera L.) vineyard (Navarra, Spain). A new, hand-held, non-destructive fluorescence-based proximal sensor was used for monitoring the anthocyanin content in grapes at veraison and harvest. Yield, vine vigour, spectral (normalized difference vegetation index and plant cell density) and chlorophyll (SPAD and simple chlorophyll fluorescence ratio) parameters were measured. Yield variability within the vineyard was the largest of all the parameters. Fluorescence-based anthocyanin indices were less variable at harvest than at veraison. The vigour parameters (main shoot length, total shoot length and shoot pruning weight) were positively correlated to yield; the chlorophyll and the spectral indices were negatively correlated with berry anthocyanin production. The correlations between vigour, yield and anthocyanin content in grapes varied substantially in time and space across the vineyard.     

植被覆盖度模型研究进展

植被覆盖度是刻画陆地表面植被数量的一个重要参数,也是指示生态系统变化的重要指标。以遥感测量为研究手段,综合讨论目前测量植被覆盖度最常用的4种模型:植被指数模型、亚像元模型、混合光谱模型和光谱梯度差模型,分析其优缺点,并对如何提高植被覆盖度的测量精度提出讨论,指出高光谱数据、多尺度遥感数据以及数码相机和航空遥感的综合使用是未来植被覆盖度测量发展的趋势。     

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the level of crop injury. This study evaluates the combined use of statistical methods and vegetation indices (VIs) derived from multispectral images to assess the level of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight rows spaced 102 cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. CVI image values were subtracted from the CVI average values of the non-injured area to generate CVI residual images (CVI_res). Frequency distribution histograms of CVI_res image values were calculated to assess the level of injury between crops. These data suggested that injury increased from 7DAA to 21DAA with corn exhibiting higher severity of injury than cotton or soybean, while only moderate injury was observed for cotton. The techniques evaluated in this study are promising for estimating the level of glyphosate herbicide drift, which can be used to make appropriate management decisions considering crop proximity.