Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters

Precision Agriculture - Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods...     

Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture

Precision Agriculture - Olive orchard is one of the main crops in the Mediterranean basin and, particularly, in Spain, with 56% of European production. In semi-arid regions, nitrogen (N) is the...     

Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat

Nitrogen (N) content is an important factor that can affect wheat production. The non-destructive testing of wheat canopy leaf N content through multi-angle hyperspectral remote sensing is of great importance for wheat production and management. Based on a 2-year experiment for winter wheat in Lethbridge (Canada), Zhengzhou (China), and Kaifeng (China) growing under different cultivation practices, the authors studied the relationships between N content and wheat canopy spectral data in solar principal plane (SPP) and perpendicular plane (PP) at different observation angles. Modeling was conducted according to the spectrum index with the highest correlation coefficient and the corresponding observation angle. The results showed that correlation coefficient between the spectral index and canopy leaf N content at each observation angle of the SPP was significantly higher than that of the PP. Significant differences in the correlation coefficient were also observed at different observation angles of the same observation plane, and the correlation coefficients of angles of ?30° and ?40° were higher than others. A model fitted by a power function by using mND705 as independent variable at an angle of ?40° in the SPP showed the highest accuracy.     

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that:(1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index(GI), mND705(modified normalized difference 705), and normalized difference vegetation index(NDVI), the values for the determining coefficient(R2), and normalized root mean square error(nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.     

Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines

The estimation of nitrogen concentration from remotely sensed data has been the subject of some work. However, few studies have addressed the effective model for monitoring nitrogen status at canopy level using Support Vector Machines (SVM). The present study is focused on the assessment of an estimation model for nitrogen concentration of rape canopy with hyperspectral data. Two types of estimation model, the traditional statistical method based on stepwise linear regression (SLR) and the emerging computationally powerful techniques based on support vector machines were applied The Root Mean Square Error (RMSE) and T values were used to assess their predictability. The results show that a better agreement between the observed and the predicted nitrogen concentration were obtained by using the SVM model. Compared to the SLR model, the SVM model improved the results by lowering RMSE by 11.86–21.13 %, and by increasing T by 20.00–29.41 % for different spectral transformations. The study demonstrated the potential of SVM to estimate nitrogen concentration using canopy level hyperspectral data and it was concluded that SVM may provide a useful exploratory and predictive tool when applied to canopy-level hyperspectral reflectance data for monitoring nitrogen status of rape.     

Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis

Protein content, which represents rice taste quality, must be estimated in order to create a harvesting plan as well as next year’s basal dressing fertilizer application plan. Ground-based hyperspectral imaging with high resolution (1 × 1 mm per pixel) was used for estimating the protein content of brown rice before harvest. This paper compares the estimation accuracy of rice protein content estimation models generated from the mean reflectances of five regions of interest (ROIs): the overall target area, dark area (less illuminated parts of the rice plants), canopy area (leaves, yellow leaves, and ears), leaf area, and ear and yellow leaf area. The size of the target sampling area was 0.85 × 0.85 m. An R + G + B histogram and a GNDVI–NDVI image were used to separate the target area into the individual ROIs. The values of the coefficient of determination R ² and the root mean square error of prediction (RMSE) were similar for each model: R ² ranged from 0.83 to 0.86 and RMSE ranged from 0.27 to 0.30% for all models except for the dark area model, where R ² = 0.76 and RMSE = 0.35%. There were no significant differences in the magnitude of the estimation error among all models. This result indicates that it is not necessary to obtain an image with a ground resolution that is greater than 0.85 × 0.85 m per pixel to estimate rice protein content before harvest. This result should provide useful information when deciding the altitude of platforms for imaging rice fields.     

Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance

Bacterial leaf blight (BLB) is an important vascular disease of irrigated rice and serious infestations may cause a significant loss of yield. This study analyzed hyperspectral canopy reflectance spectra of two rice cultivars with different susceptibilities to BLB to establish spectral models for assessing disease severity for future site-specific management. The results indicated that wavebands from 757 to 1039 nm were the most sensitive region of the spectrum for the moderately susceptible cultivar TNG 67, whereas most narrow bands showed a significant relationship for the highly susceptible cultivar TCS 10. All the spectral indices (SIs) calculated had significant relationships with proportions of infested area in cultivar TCS 10, but only two SIs correlated significantly with cultivar TNG 67. The relation between the severity of the disease and spectral reflectance for the less susceptible cultivar TNG 67 can be improved by using a multiple linear regression approach.     

Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves

Precision Agriculture - Potassium (K) is one of three main crop nutrients, and the high rate of potash fertilizer utilization (second only to nitrogen) leads to high prices. Therefore, efficient...     

生姜叶片氮含量的高光谱遥感估算模型研究

作物叶片氮含量的快速估算对于及时了解作物长势、病虫害监测以及产量评估具有重要意义。该文以经济作物生姜为研究对象,获取了2015年4月-9月不同品种、不同生育期和不同氮肥梯度下生姜叶片的高光谱和氮含量数据,对比分析了比值植被指数、归一化植被指数、植被指数组合形式对生姜叶片氮含量的估算效果。在此基础上,基于波段组合算法,筛选出了生姜叶片氮含量的敏感波段,并构建了两个新型光谱指数NDSI_((754,713))和RSI_((754,713))。结果表明,所选择的植被指数中,MCARI(705,750)/OSAVI(705,750)对生姜叶片氮含量估算效果最好,模型精度R~2、RMSE和RE分别为0.73、0.27、11.64%;利用波段组合算法构建的归一化光谱指数NDSI(754,713)对生姜叶片氮含量估算效果要优于MCARI(705,750)/OSAVI(705,750),模型估算精度R~2达0.83,使用的敏感波段713 nm与754 nm均位于植被的"红边"区域。对所建模型进行验证,叶片氮含量的预测值和实测值具有较好的一致性,验证样本R~2为0.78,RMSE为0.20,RE为9.81%。上述分析结果可为农业管理部门及时掌握生姜长势信息、制定施肥策略提供技术支持。     

基于多角度高光谱遥感的冬小麦叶片含水率估算模型

准确的作物水分监测对于旱情评估具有重要意义。在分析研究区冬小麦多角度光谱特征后,利用不同水分处理下冬小麦实测叶片含水率和实测多角度光谱数据,基于植被光谱指数法,建立不同观测角度下冬小麦光谱植被指数、水分敏感波段光谱指数与叶片含水率之间的数学模型。结果显示,相对方位角与相对天顶角越小时,观测到的光谱指数与叶片含水率的相关关系越优;敏感波段组合构建的光谱指数中,1450nm波段分别与其他波段组合的NDSI、RSI指数与叶片含水率相关性在各观测角度条件下均较好,1 450 nm波段是冬小麦叶片含水率研究的最佳敏感波段;选取常见的4种植被指数(NDVI、EVI、WI和NDII)中WI和NDVI在各观测角度下与叶片含水率的相关性优于其他两种指数,决定系数R2均在0.83以上,P0.01呈极显著相关;综上建立的多角度光谱叶片含水率估算模型,平均相对误差MRE均小于0.154、均方根误差RMSE均小于0.098,拟合效果较好,尤其是光谱指数NDSI1160,1450、NDSI980,1450和植被指数NDVI、WI;基于以上4种指数建立的最优观测角度(0°,30°)模型,其中植被指数WI的估算效果最好,相关系数在各角度均达到5%的相关显著水平,MRE0.03,可作为最优观测角度反演研究的最优植被指数。     

棉花冠层叶片叶绿素含量与高光谱参数的相关性

【目的】研究棉花冠层叶片叶绿素含量与高光谱参数的相关性,建立叶绿素含量估算模型。【方法】2014年,以鲁棉研28号为研究对象,测定不同施氮水平和生育期棉花冠层叶片叶绿素含量及350~2 500nm光谱反射率,以棉花冠层高光谱反射率与冠层叶片叶绿素含量为数据源,在分析叶绿素含量与原始高光谱反射率(R)、一阶导数光谱反射率(DR)、光谱提取变量和植被指数相关性的基础上,采用一元线性与多元逐步回归的方法构建了叶绿素含量估算模型,并对从中筛选的6种棉花冠层叶片叶绿素含量估算模型进行精度对比。【结果】1)棉花冠层叶片叶绿素含量在反射光谱766nm处相关系数达到最大值,相关系数r=0.836;对于一阶导数光谱,叶绿素含量的敏感波段发生在753nm处,r=0.878;2)以9种光谱提取变量与8种植被指数为自变量,建立叶绿素含量的估算模型,筛选出的特征变量为红边面积(SDr)、绿峰与红谷的归一化值((Rg-Rr)/(Rg+Rr))、绿峰幅值(Rg),仅采用8种常用植被指数建立估算模型,筛选出的变量为比值植被指数(RVI);3)所建立的6种模型中以基于一阶导数光谱反射率建立的多元逐步回归估算模型精度最高,均方根误差(RMSE)为1.075,相对误差(RE)为2.22%,相关系数(r)为0.952。【结论】采用原始光谱、一阶导数光谱、光谱提取变量及植被指数均可对棉花叶绿素含量进行监测,其中基于一阶导数光谱的多元逐步回归模型对叶绿素含量的估算效果最优。     

基于成像高光谱的苹果树叶片病害区域提取方法研究

【目的】对带病斑苹果树叶片的高光谱图像进行病斑提取,为作物病虫害的遥感监测提供支持。【方法】对带有病斑的苹果树叶片成像高光谱图像,从传统基于光谱特征和面向对象特征2个方向入手进行病斑提取。为减少高光谱图像波段之间的冗余,首先对高光谱图像采用PCA变换进行降维处理,利用降维之后的前11个波段,分别采用波谱角分类和面向对象分类的方法提取苹果树叶片病害区域。【结果】由于同物异谱和异物同谱现象的存在,波谱角分类算法在提取病斑时,对叶柄和叶脉产生了错误的分类,而且以像元为分类单位的波谱角分类,在分类结果图中存在椒盐噪声,而面向对象分类则避免了这一现象的发生。【结论】采用面向对象分类方法提取苹果叶片病斑的结果优于基于光谱特征的波谱角分类方法,其总体精度和Kappa系数分别为98.44%和0.97。     

高光谱评价植被叶绿素含量的研究进展

重点介绍利用便携式光谱仪获得的高光谱数据在评价植被叶绿素含量的研究状况。从叶绿素的光谱特性入手。通过和传统宽波段对比阐述高光谱数据在评价植被叶绿素中的特点。在此基础上简要介绍了高光谱遥感数据估计植被叶绿素含量两种方法的研究进展。一是利用光谱数据。植被指数，导数光谱评价植被叶绿素密度或浓度。二是利用红边光学参数评价植被叶绿素密度或浓度，并分析了研究中可能存在的问题。     

基于三维模型的不同树龄苹果树冠层结构评价

【目的】对高纺锤形苹果树的冠层生长规律进行研究,为苹果高纺锤形树形的生长管理提供参考。【方法】以M26中间砧短枝富士为试材,选择4,8,12年生3个树龄的果树作为3个处理,人工测量和统计各树龄冠层基本信息及枝组结构,构建3个树龄果树的虚拟树体模型,比较实际树体照片与虚拟树体,基于构建的三维模型计算果树冠层总叶面积（total leaf area,TLA）、照射叶面积（projected leaf area,PLA）、光截获效率（silhouette to total leaf area ratio,STAR）、叶面积指数（Leaf Area Index,LAI）等指标,建立3个树龄果树的虚拟果园,并模拟计算其中单株果树的光截获效率,用LAI-2000冠层分析仪测定各树龄冠层的LAI,并与三维模型计算结果进行比较和验证。【结果】数字化得到的果树冠层基本信息及枝组结构与人工测量统计结果均无显著差异,但数字化得到的枝组结构更为准确。与实际树体照片相比,虚拟树体图像整体上能够展示树体实际生长情况。3个树龄果树冠层的TLA与PLA分别为6.41,11.60,19.69 m²和2.38,3.88,5.77 m²,随树龄的增长而显著上升;STAR值分别为0.37,0.33和0.29,随树龄增长而下降,但三者之间差异并不显著,LAI分别为1.78,3.87和3.28;在虚拟果园中,模拟计算得到的4,8和12年生果树的STAR值分别为0.30,0.18和0.19,其中4年生与8,12年生果树之间有显著差异。冠层叶面积指数实测值与三维模型模拟值的均方根误差（RMSE）为0.171,可知模型的精准度高。【结论】高纺锤形富士苹果树幼树有较好的光截获效率,随树龄增长单株果树的光截获效率有所下降。且相较于低龄果园,高龄果园中果树冠层光截获效率下降明显,建议果园管理中应注意合理控制种植密度和枝叶密度,以改善高龄果园冠层的光照分布。     

云南松比叶面积和叶干物质含量随冠层高度的垂直变化规律

比叶面积（SLA）和叶干物质含量（LDMC）综合反应了植物利用资源的能力,是植物适应环境所体现出的关键叶性状。为深入了解SLA和LDMC沿冠层高度的垂直变化规律,该文对云南省永仁县万马乡2株云南松（树高14.4 m,树龄29年;树高30.2 m,树龄138年）不同冠层高度叶片SLA和LDMC进行调查研究,分析了不同年龄树木、不同叶龄SLA和LDMC的差异,初步探讨了SLA和LDMC在云南松冠层中的垂直空间分布。结果表明:①对于SLA,29年生云南松当年叶和1年叶比138年生云南松分别高27.66%和16.71%;对于LDMC,138年生比29年生分别高14.67%和5-10%;②SLA与LDMC成负相关;③各龄叶SLA随冠层高度的增加均具有对数递减趋势;④约在22 m高度以下,LDMC随冠层高度的增加而增加;22 m以上,当年叶LDMC呈现下降趋势,但1年叶变化不明显。研究结果指出,光照和水分资源在冠层不同高度的分配,共同导致SLA和LDMC沿冠层垂直方向发生变化。      

水稻冠层叶片SPAD数值变化特征及氮素营养诊断

建立水稻Oryza sativa氮营养诊断模型,实时反映水稻植株氮素营养状况,对水稻田间管理至关重要。于2015年在浙江省德清市开展田间试验,选择‘甬优538’‘Yongyou 538’和‘秀水134’‘Xiushui 134’作为代表品种,设置5个施氮水平0（N₀）,70.0（N₁）,140.0（N₂）,210.0（N₃）,280.0（N₄）kg·hm-2,通过研究不同施氮水平下2个水稻品种冠层叶片作物分析仪器开发值（SPAD）变化规律,探究5个不同施氮水平下植株氮质量分数的变化趋势,并利用归一化SPAD指数（I_NDSPAD14）估算植株氮质量分数。结果表明:顶4叶相较其他叶片更能指示水稻植株氮质量分数,归一化SPAD指数与N₀~N₄所有不同施氮量组别之间冠层叶片氮质量分数呈显著正相关（P < 0.05）,‘甬优358’水稻品种决定系数为0.69~0.96,‘秀水134’品种决定系数为0.64~0.94。该指数可以对水稻冠层叶片氮质量分数快速估测。     

基于无人机和地面数字影像的水稻氮素营养诊断研究

选用扫描仪和无人机平台获取水稻叶片和冠层的数字图像,运用数字图像处理技术研究不同氮素营养水平水稻叶片和冠层的综合特征信息,从而应用于水稻的氮素营养诊断。结果表明:1)通过叶片叶绿素a含量和扫描叶片颜色参量之间的相关性分析,得到可用于诊断水稻氮素营养水平的叶片颜色特征参量B、b、b/(r+g)、b/r、b/g。通过叶片的颜色、形状综合特征信息与YIQ电视信号彩色坐标系统的参量建立氮素营养的识别模型,4个不同氮素水平的正确识别率分别为:N0(0 kg N.hm-2)74.9%,N1(60 kgN.hm-2)52%,N2(90 kg N.hm-2)84.7%,N3(120 kg N.hm-2)75%;2)无人机获取的田间冠层图像识别水稻氮素营养水平的综合特征参量是G、B、b、g、b/(r+g)、b/r、b/g、H、S、DGCI,选择相同的CB参量建立冠层氮素营养的识别模型,4个不同氮素水平的正确识别率为:N0(0 kg N.hm-2)91.6%,N1(60 kgN.hm-2)70.83%,N2(90 kg N.hm-2)86.7%,N3(120 kg N.hm-2)95%。初步研究表明基于综合特征的氮素诊断模型区分效果比较好,利用叶片扫描图像和无人机识别与诊断田间水稻氮素是可行的。     

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.     

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.     

Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

Precision Agriculture - Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural...