稻纵卷叶螟危害孕穗期水稻的光谱诊断

【目的】阐明稻纵卷叶螟（Cnaphalocrocis medinalis）危害孕穗期水稻后,水稻冠层、未受害展开叶和受害已卷叶片的光谱特征,建立基于光谱参数的稻纵卷叶螟危害程度的诊断模型。【方法】利用便携式光谱仪测定不同卷叶率小区水稻的冠层光谱反射率,同时在不同卷叶率小区内采集未受害展开叶和已卷叶带回实验室进行室内单叶的光谱反射率测定,并采用相关分析与回归建模方法组建稻纵卷叶螟危害程度的光谱诊断模型。【结果】水稻冠层光谱反射率在近红外光区域内随卷叶率级别的升高而降低,738-1 000 nm处的反射率可较好地表征出水稻受稻纵卷叶螟危害的程度。不同卷叶率小区内的未受害叶的光谱反射率也可很好地表征水稻的受害级别,在512-606和699-1 000 nm处的反射率与小区卷叶率级别呈极显著的负相关。已受害卷叶的反射率在582-688 nm处与受害级别呈极显著正相关。水稻受稻纵卷叶螟危害后,在冠层、未受害叶及已受害卷叶光谱的红边幅值与红边面积有明显变化。利用水稻冠层光谱的红边幅值、未受害健康叶片550 nm处的反射率建立的稻纵卷叶螟危害程度的一元回归模型的诊断误差较小,而同时利用冠层、受害叶和未受害叶光谱组建的逐步回归模型的诊断误差最小,可用于小区稻纵卷叶螟危害的监测。【结论】受害区域内水稻冠层在738-1 000 nm处和未受害叶片在512-606和699-1 000 nm处的光谱反射率,以及红边幅值和红边面积均可较好地表征水稻受稻纵卷叶螟危害后的卷叶率级别,可利用这两层次的光谱指标分别对小区水稻的受害程度进行诊断。     

Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes

It is widely accepted that pest infestations elicit a change in plant physiology, which cause detectable changes in crop leaf reflectance. In this study, we test the hypothesis that crop leaf reflectance may also be used to forecast the risk of pest infestation before they actually occur. We collected reflectance data in 160 spectral bands from 405 to 907 nm from excised leaf pieces from field grown maize plants under 3 irrigation regimes. Leaf material was collected at weekly intervals in two growing seasons. The same leaf pieces were used in choice bioassays with carmine spider mites to assess attractiveness to mites (spider mite “bio-response”) across irrigation regimes. In one growing season, we also obtained nutritional element data (lipid, protein, soluble sugar, starch, lignin, Ca, P, Mg, K, S, and Cl) from whole maize plants. Principal component analysis showed that potassium content (K) was highly negatively correlated with spider mite bio-response. Relative reflectance at 740 nm showed a highly significant and positive trend across spider mite bio-response classes, and that potassium content showed a highly significant and negative trend across the same classes. Thus, we argue that relative reflectance at 740 nm may be used to predict both potassium content and risk of spider mite infestation. Based on extensive reviews, potassium leaf content is known to reduce susceptibility of crops to pests. The results presented provide encouraging support for remotely sensed risk assessment of pest infestations through reflectance-based monitoring of maize leaf attractiveness and highlight that reflectance based monitoring of crop susceptibility may be possible through careful management of macro element crop properties, such as potassium content.     

A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration

This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.     

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.     

Comparison of transmittance and reflectance to detect insect infestation in Montmorency tart cherry

Different measurement modes, reflectance and transmittance, were compared for their ability to identify the internal insect infestation in tart cherry (c.v. Montomorency) using visible and near infrared spectroscopy. The cherries were collected in the summers of 2004, 2005, 2006 and 2007 from different orchards in Michigan. The samples included intact (Level 0) as well as infested cherries with different damage levels (Levels 1–5). The spectra were recorded for whole cherries with a spectroradiometer within a wavelength region between 550 and 950 nm. MANOVA analysis indicates that the spectral data, both of the transmittance or reflectance, basically falls into three clusters, showing that the intact cherries and the cherries with Level 1 infestation (slightly infested) are in one cluster; Samples with infestation degree from Levels 3 to 5 (seriously infested) are clearly distinguishable from the intact and slight infested samples; the cherries with Level 2 infestation degree are scattered in between the intact and serious infested samples. According to the results of the discriminant analysis, transmittance works slightly better than reflectance in terms of the total classification accuracy. Spectroscopic technology should provide the cherry industry with a valuable tool for rapidly detecting insect infestation in tart cherry.     

Identification of grapevine varieties using leaf spectroscopy and partial least squares

Grapevine variety identification is a matter of great interest in viticulture, which is currently addressed by visual ampelometry or wet chemistry genetic analysis. This paper reports the development of a simple and automatic method of classification of grapevine varieties from leaf spectroscopy. The method consists of a classifier based on partial least squares that discriminates among grapevine varieties using a hyperspectral image of a leaf measured in reflectance mode. Hyperspectral imaging was conducted with a camera with 1040 wavelength bands operating between 380 nm and 1028 nm. The classifier was created using 300 leaves, 100 of each of the varieties Vitis vinifera L., Tempranillo, Grenache and Cabernet Sauvignon. Monte-Carlo cross-validation confirmed the classifier’s performance for the three varieties, which exceeded 92% in all cases. The proposed method has proven to satisfactory classify among grape varieties, but certainly a wider range of grapevine cultivars should be tested before it gets implemented for local sensing with the aim of providing the wine industry with a fast, automatic, environmentally friendly and accurate tool for grapevine variety identification.     

Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy

The soluble solids content (SSC) and total acidity (TA) are the major characteristics for assessing quality and maturity of Nanfeng mandarin fruits. The feasibility of charge coupled device near infrared spectroscopy (CCD-NIRS) combining with effective wavelengths selection algorithm used to measure SSC and TA nondestructively was investigated. The effective wavelengths to SSC and TA were chosen by interval partial least squares (iPLS) in the wavelength range of 600–980 nm. The predictive ability of SSC model used PLS regression was improved with r of 0.92 and RMSEP of 0.65 °Brix using effective wavelengths of 681.36–740.51 nm, 798.60–836.19 nm and 945.52–962.75 nm. The TA model was simplified with r of 0.64 and RMSEP of 0.09% using effective wavelengths of 817.57–836.19 nm, 909.85–927.60 nm and 945.52–962.75 nm. The experimental results demonstrated that the CCD-NIRS technique combining with iPLS algorithm was a feasible method to measure SSC and TA of Nanfeng mandarin fruits nondestructively.     

Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy

Visible and near-infrared reflectance spectra were used to distinguish the bruises from the intact surfaces on ‘Golden Delicious’ apples. Reflectance spectra of apples were acquired for both bruised and intact surfaces within the range of 400–1700 nm. The effective wavebands for detecting bruises were determined by analysis of the correlelogram. The wavebands around 545 and 1200 nm clearly show the time evolution of the bruised tissue. The mean-normalized reflectance difference between wavebands centred at 745 and 905 nm was also an effective discriminator for detecting old bruises. Afterwards, a quadratic discrimination analysis was performed based on the selected discriminators. The total classification error for the 1-day-old bruises was about 16.3%. A more extensive study was carried out to determine the effects of storage time on classification performance. The detection error was decreased with the elapse of storage time after bruising. However, it was difficult to remove the time influence on the classification accuracy if only depending on the intensity value of reflectance.     

A high-resolution airborne four-camera imaging system for agricultural remote sensing

This paper describes the design and testing of an airborne multispectral digital imaging system for remote sensing applications. The system consists of four high resolution charge coupled device (CCD) digital cameras and a ruggedized PC equipped with a frame grabber and image acquisition software. The cameras are sensitive in the 400 to 1000 nm spectral range and provide 2048 × 2048 active pixels with 12-bit data depth. A 24 mm lens is attached to each camera via an F to C mount adapter, resulting in an imaging size of 0.63 times the flight altitude. The four cameras are equipped with blue (430–470 nm), green (530–570 nm), red (630–670 nm), and near-infrared (NIR) (810–850 nm) bandpass interference filters, respectively, but have the flexibility to change filters for desired wavelengths and bandwidths. The cameras are arranged in a quad configuration and attached to adjustable mounts that facilitate aligning the cameras horizontally, vertically, and rotationally. The image acquisition software allows the synchronized black-and-white band images from the cameras to be viewed on the computer monitor in any one of the four modes: a quad, one band image at a time, a normal color composite, or a color-infrared (CIR) composite. The band images are refreshed continuously to allow the operator to selectively save images with correct areas of interest. The selected four-band composite image is saved as a tiff file and consecutive images can be saved in 1-s intervals. A band-to-band alignment procedure based on the first- and second-order polynomial transformations was presented to further align the four band images. The system performed well in both stationary and airborne testing conditions. Airborne images obtained from agricultural fields, rangelands, and waterways demonstrate that this system has potential for monitoring crop pest conditions, mapping invasive weeds and assessing natural resources.     

Development of software for spectral imaging data acquisition using LabVIEW

Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.     

Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification

Hyperspectral scattering images between 600 nm and 1000 nm were acquired for 580 ‘Delicious’ apples for mealiness classification. A locally linear embedding (LLE) algorithm was developed to extract features directly from the hyperspectral scattering image data. Partial least squares discriminant analysis (PLSDA) and support vector machine (SVM) were applied to develop classification models based on the LLE, mean-LLE and mean spectra algorithms. The model based on the LLE algorithm achieved an overall classification accuracy of 80.4%, compared with 76.2% by the mean-LLE algorithm and 73.0% by the mean spectra method for two-class classification (i.e., mealy and nonmealy) coupled with PLSDA. For the SVM models, the LLE algorithm had an overall classification accuracy of 82.5%, compared with 79.4% by the mean-LLE algorithm and 78.3% by the mean spectra method. Hence, the LLE algorithm provided an effective means to extract hyperspectral scattering features for mealiness classification.     

基于高光谱的水稻叶片含水量监测研究

【目的】建立快速、无损诊断水稻叶片含水量的估测模型,为水稻水分精确管理提供依据。【方法】基于2年不同土壤水分处理和水稻品种的池栽试验,于水稻主要生育时期同步测定顶部4张叶片的光谱反射率和含水量,系统分析350-2 500 nm波段范围内任意两波段组合而成的比值（RSI）、归一化差值（NDSI）及差值（DSI）光谱指数,并分析其与叶片含水量的量化关系。【结果】不同土壤水分处理和叶位间,叶片反射光谱具有显著的时空变化特征,叶片含水量的敏感光谱波段主要位于近红外及短波红外区域;RSI （R1402, R2272）及NDSI （R1402, R2272）光谱指数与叶片含水量呈现良好的线性相关,线性拟合R2均达到0.80。基于独立试验资料对所建模型进行测试检验也显示,预测值和观察值的拟合R2也均达到0.86。【结论】RSI（R1402, R2272）、NDSI（R1402, R2272）均可用于水稻叶片含水量的定量监测。     

A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages,sex and color by means of visible and near infrared reflectance spectroscopy

The usefulness of classifying the Alpaca wool samples according to their color, sex and location is associated with their economic value in the market, hence adequate methods for rapid classification are needed to assess the of wool value. This study evaluated the potential of the visible and near infrared (vis–NIR) spectroscopy combined with multivariate statistical analysis to classify Alpaca (Lama Pacos) fiber samples according to age (1 and 2–3-year-old), sex (Male and Female) and color (Black, Brown, LF and White). Samples (n = 291) were scanned in reflectance mode in the wavelength range of 400–2500 nm using a monochromator instrument (FOSS NIRSystems6500, Inc., Silver Spring, MD, USA). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify fiber samples. Cross-validation was used for validation of classification models developed. Results showed that PLS-DA correctly classified 100% of fiber samples into ages, intermediate classification rates were obtained for color, while lower classification rates were obtained for the discrimination of wool samples according to sex. The results from this study suggested that vis–NIR spectroscopy in combination with multivariate data analysis can be used as a rapid method to classify Alpaca fiber samples according to age, sex and color.     

Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing

Outbreaks of greenbug and Russian wheat aphid appear in the Great Plains almost every year and have had significant impacts on wheat yields. Early detection of aphid infestation is a critical part of integrated pest management (IPM) for wheat production. A study was done to determine the feasibility of using remote sensing techniques to detect stress in wheat caused by aphid infestation. The purpose of this greenhouse study was to characterize and differentiate stress in wheat due to infestation by greenbugs and Russian Wheat aphids using a hand-held Cropscan radiometer. Reflectance data and derived vegetation indices from the 16 bands of the radiometer were analyzed using SAS PROC MIXED statistical analysis procedure. Results show that it is possible to detect the stress caused by the two aphid species and to discriminate between the two aphid-induced stresses in wheat using remote sensing. Ratio-based vegetation indices (based on 800/450 nm and 950/450 nm) were found useful in differentiating the two stresses in wheat. However, more canopy-level-studies are needed to identify bands and indices that might have potential to differentiate the two stresses on wheat under field conditions.     

Development approach and evaluation of the Nutrient Expert software for nutrient management in cereal crops

Meeting the demand for more food in the next 20–30 years requires intensifying cereal cropping systems and increasing current yields to about 70–80% of the genetic yield potential. A dynamic and robust nutrient management approach such as site-specific nutrient management (SSNM) will be essential to increase yields and optimize profits while maintaining the productivity of these intensive cropping systems. SSNM has increased yield and profit in rice, maize, and wheat in major cropping systems in Asia; but, crop advisors have found it complex and difficult to implement in the field. Nutrient Expert (NE) was developed to provide crop advisors with a simpler and faster way to use SSNM. NE enables crop advisors to develop SSNM recommendations using existing site information. Nutrient Expert for Hybrid Maize (NEHM) increased yield and profit of farmers in Indonesia and the Philippines. In Indonesia, NEHM increased yield by 0.9 t ha⁻¹, which increased profit by US$ 270 ha⁻¹ over farmer’s fertilizer practice (FFP). Compared with FFP, NEHM recommendations reduced fertilizer P (−4 kg ha⁻¹), increased fertilizer K (+11 kg ha⁻¹), and did not significantly change fertilizer N. In the Philippines, NEHM increased yield by 1.6 t ha⁻¹ and profit by US$ 379 ha⁻¹ compared with FFP. Compared with FFP, NEHM gave higher rates of all three nutrients (+25 kg N ha⁻¹, +4 kg P ha⁻¹, and +11 kg K ha⁻¹), which substantially increased fertilizer costs (US$ 64 ha⁻¹) but still increased profit by about six times the additional investment in fertilizer. NE accounts for the important factors affecting site-specific recommendations, which makes it a suitable starting point for developing nutrient management tools to reach more users.     

一种新的估算水稻上部叶片蛋白氮含量的植被指数

 【目的】阐明水稻顶部4张叶片蛋白氮含量和反射光谱特征的变化规律及其相互关系,建立快速、准确诊断水稻功能叶片蛋白氮含量的方法。【方法】通过3年不同施氮水平和不同品种类型的大田试验,分生育期同步测定顶部4张叶片的光谱反射率及蛋白氮含量,系统分析叶片蛋白氮含量与多种高光谱参数的定量关系。【结果】水稻叶片蛋白氮含量和光谱反射率在不同施氮水平、不同生育期及不同叶位间均存在明显差异,叶片蛋白氮含量的敏感波段主要存在于可见光绿光区530～580 nm及红边区域695～715 nm,其中红边区域表现最为显著。红边区域700 nm附近波段与近红外短波段的比值组合(SRs)可以有效地估算水稻上部功能叶片的蛋白氮含量,其次是绿光区587 nm左右的波段与近红外短波段的比值组合。基于新提出的SR（770,700）及已报道的GM-2、SR705、RI-half光谱指数,线性回归模型的拟合精度（R2）分别达到 0.874,0.873,0.871和0.867。经独立资料的检验表明,这些回归模型可以实时监测叶片蛋白氮含量变化,预测精度R2分别为0.810、0.806、0.804和0.800,相对误差RE 分别为12.1%、12.4%、12.6%和12.9%。【结论】可以利用关键特征光谱指数来诊断水稻上部叶片的蛋白氮含量状况,尤以SR（770,700）、GM-2、SR705和RI-half表现为较强的估测能力。     

基于PROSPECT和4-scale模型的光化学植被指数尺度转换

  目的  光化学植被指数（PRI）对于准确估计植被光能利用率（LUE）有着重要的作用。但在不同的尺度（叶片、冠层、景观尺度）上，PRI与LUE二者之间的关系及其影响因素不同。传感器获得的光谱为像元及冠层光谱，叶片尺度的PRI-LUE关系模型无法直接用于冠层尺度的数据，因此需要对冠层尺度的PRI指数进行尺度转换。  方法  首先通过叶片尺度的PROSPECT模型，模拟不同生化参数下叶片的反射率与透射率，进而计算叶片尺度PRI指数与简单比值PRI指数（记为SR-PRI）。其次，将获得的叶片尺度反射率、透射率作为参数输入到4-scale模型中，获取不同叶面积指数（ LAI）下冠层尺度的反射率，计算得出冠层尺度的PRI、SR-PRI。建立不同LAI下PRI、SR-PRI的冠层?叶片尺度转换函数，并对不同尺度上影响PRI、SR-PRI的因子进行敏感性分析。  结果  PRI、SR-PRI在进行冠层与叶片尺度转化过程中，都表现出很明显的线性关系，并且拟合效果（R2）呈现出随LAI的增大而增大的趋势。对比相同LAI水平下的PRI、SR-PRI的拟合结果发现，SR-PRI的拟合效果普遍要优于PRI。  结论  4-scale模型用来进行PRI与SR-PRI在冠层、叶片间的尺度转换是可行的，通过建立不同LAI下的尺度转换函数，可以实现将冠层尺度的PRI、SR-PRI转化到叶片尺度。      

Model fusion for prediction of apple firmness using hyperspectral scattering image

Hyperspectral scattering image is an advanced technology widely used in non-destructive measurement of fruit quality. To develop a better prediction model for apple firmness, the present study investigates a model fusion method coupled with wavelength selection algorithms. The current paper first discusses two wavelength selection algorithms, namely, uninformative variable elimination (UVE) and supervised affinity propagation (SAP). The selected effective wavelengths are then set as input to the partial least square (PLS) model. Six hundred “Golden Delicious” apples were analyzed. The first 450 apples were used as sample for the calibration model, whereas the remaining 150 were used for the prediction model. Compared with full wavelengths, the number of effective wavelengths based on the UVE and SAP algorithms decreased to 34% and 35%, but the correlation coefficient of prediction (Rp) increased from 0.791 to 0.805 and 0.814, whereas the root mean-square error of prediction (RMSEP) decreased from 6.00 to 5.73 and 5.71 N, respectively. A fusion model was then developed using UVE-PLS and SAP-PLS models coupled with backpropagation neural network. A better prediction accuracy was achieved from the fusion model (Rp = 0.828 and RMSEP = 5.53 N). The model fusion provides an effective modeling method for apple firmness prediction using hyperspectral scattering image technique.     

东北水稻叶片SPAD遥感光谱估算模型

为通过构建高精度SPAD遥感估算模型,实现对水稻叶片叶绿素含量进行实时无损的监测,以东北地区多时期不同施氮水平下水稻叶片光谱反射率为研究对象,采用回归模型与BP神经网络算法构建不同输入量的SPAD高光谱估算模型,通过模型精度评价指标决定系数R~2、均方根误差RMSE,确定最优输入量和最优模型。结果表明:1)不同品种水稻成熟时期不同导致在孕穗期和抽穗期之间光谱反射率出现差异;2)回归模型中以DVI(D755,D930)为变量建立多项式模型估算精度最高;3)与回归模型相比,不同波长处单波段反射率作为输入量的BP神经网络模型估算精度显著提高,R~2为0.98。BP神经网络模型在隐藏节点数为7时估算精度达到稳定,在可见光和近红外处经过不同波段反射率作为输入量的尝试说明神经网络模型较为稳定,可以用来反演叶绿素相对含量。     

稻纵卷叶螟危害后水稻叶片的光谱特征

【目的】阐明水稻受稻纵卷叶螟危害后不同受害程度的叶片、卷叶的分布形式及卷叶率对稻叶光谱特征的影响,获取诊断水稻受害程度的模型,以便为稻纵卷叶螟的遥感监测提供理论指标与方法。【方法】试验以不同受害等级的虫害叶及健康叶为材料,在室内恒定条件下采用ASD光谱仪分别测定不同受害程度、受害叶片的不同分布形式、及不同卷叶率下稻叶的光谱反射率,并采用直线回归法,建立基于光谱参数的水稻受害程度诊断模型。【结果】水稻虫害叶光谱反射率均随受害等级的增加,在绿光区(530—570nm)和近红外区(700—1050nm)降低,而在红光区(610—700nm)增加。能反演叶片受害程度的敏感波段为530—564nm、614—695nm和706—1050nm。建立了5个反演叶片受害程度的模型,诊断准确率在80%—90%之间,并且以741nm处的反射率对叶片受害程度的诊断效果最好。在卷叶率恒定的条件下,卷叶的分布位置对光谱反射率影响较小;而卷叶率对光谱反射率的影响较大,表现为随卷叶率的增大,450—500nm和610—700nm处的反射率增大,530—570nm和700—1050nm处反射率降低。差值植被指数(Rnir-Rred)、黄边面积(SDy)及红边面积与蓝边面积的差值(SDr-SDb)等指标均能将6个不同等级的卷叶率(0、10%、30%、50%、70%和90%)区分开,并且利用黄边面积(SDy)指标诊断卷叶率的准确率达86%。【结论】水稻受稻纵卷叶螟为害后,在叶片光谱反射率上有明显的表现,可以利用光谱特征来监测稻叶的受害程度及卷叶率大小。