基于鲜叶光谱估测氮素营养的新植被指数(英)

采用田间试验的方法开展利用鲜叶光谱反射率估测水稻氮素营养状况的研究.基于氮素在水稻不同功能叶片之间运转规律的机理,文章重点分析了第一和第三完全展开叶红边斜率和红边位置的变化,并基于红边位置和红边斜率构建了一个新的植被指数(命名为"红边曲线肩夹角植被指数",简称为RSAVI)监测水稻氮素营养状况.为了证明RSAVI在监测水稻氮素营养状况的可行性,分析了不同生育时期氮素含量和RSAVI之间的相关性.结果表明RSAVI和叶片氮素含量显著相关,相关系数介于0.867～0.938之间.并且RSAVI和氮素含量之间建立的回归模型以多项式模型效果最佳.决定系数介于0.7512～0.8796之间,模型均通过0.01水平检验.因此研究结果表明,在本次试验中使用RSAVI估测水稻氮素营养是可行的.     

基于自然光照反射光谱的温室黄瓜叶片含氮量预测

利用便携式光谱辐射仪测量了自然光照条件下温室黄瓜叶片的光谱反射率,并计算了反射率光谱的一次微分光谱。反射率光谱以及一次微分光谱与叶片含氮量的相关分析表明,温室内光谱特性与叶片含氮量相关性最大的敏感波段分别是505～664 nm和685～722 nm。当利用原始光谱进行分析时,通过变量筛选得到了4个敏感波长,分别是568、596、640和664 nm。偏最小二乘回归分析(PLSR)以及归一化颜色指数(NDCI)分析都表明,建模时的相关系数R_C>0.800,模型验证时的相关系数R_V>0.700。对微分光谱进行的相关分析结果表明,利用单一敏感波长520 nm就可获得理想模型,建模时的相关系数为0.880,模型验证时的相关系数为0.787。对比原始光谱的PLSR模型与一阶微分光谱的一元线性回归模型可以得知,原始光谱以及一阶微分光谱都可用于温室内叶片含氮量的预测,而且一阶微分光谱在一些特殊的波长处具有更高的预测能力,这些模型将成为开发便携式作物长势诊断仪器的技术基础。     

基于漫反射光谱的叶面药液浓度检测方法

应用漫反射反射光谱对叶面药液质量浓度进行了检测研究。选择350～1900nm波段,以标准偏差归一化、三点滑动平均滤波、一阶导数组合预处理,应用逐步回归分析、主成分、主成分+人工神经网络、偏最小二乘、偏最小二乘+人工神经网络回归分析建立了5种数学模型。试验结果表明这5种算法的预测均方根误差分别为0.067、0.061、0.059、0.039、0.056,偏最小二乘法建模效果优于其他模型。考虑到不同作物种类对叶面药液浓度影响,选用八角金盘、油菜、青菜3种作物叶片为对象,在偏最小二乘下建模,其预测集相关系数分别为0.994、0.974、0.929,预测均分根误差分别为0.039、0.050、0.075。表明不同种类作物对叶面药液浓度检测影响较小,漫反射光谱技术检测叶面药液浓度是可行的。     

基于叶片光谱透过特性的植物氮素测定

该文通过不同施氮水平下营养液栽培的水稻和黄瓜叶片在300～1100 nm的分光光谱透过率,与其叶绿素含量和含氮量的相关性分析,确定了560、650和720 nm作为特征波长,940 nm作为参比波长可用于植物营养的快速无损诊断。以上述波长的光谱透过率构建的21组光谱特征参数中,(T940-T560)/(T940+T560)、log(T940/T560)和log(T940/T650)与水稻和黄瓜叶片的叶绿素含量和含氮量的相关性较好,且经回归估测检验的相对误差均小于8%。因此,上述光谱特征参数可作为植物氮素营养指标用于植物叶片的叶绿素含量和含氮量的快速无损估测,从而为植物营养无损诊断提供技术支持。     

不同品种玉米氮含量与叶片光谱反射率及SPAD值的相关性

为明确不同玉米品种SPAD值及光谱反射率进行氮素营养诊断的可行性,以华北地区的6个主要玉米杂交品种中单909、隆平206、郑单958、金赛06-9、农华101与登海605为材料,设置0.04、0.4、2.0和4.0 mmol/L 4个氮水平的砂培试验,研究了玉米拔节期氮含量、叶片光谱反射率及SPAD值的处理间差异,及其不同氮水平下玉米氮含量与叶片550 nm处的光谱反射率及SPAD值的相关性。结果表明:来源于氮水平的变异远高于品种及二者交互作用引起的变异,植株氮含量与叶片550 nm处的光谱反射率呈显著负相关关系(R=-0.808 8),氮含量与SPAD值存在极显著的正相关性(R=0.895 6)。因此,通过SPAD值及光谱反射率对华北地区玉米杂交种进行氮素营养诊断是可行的,且用SPAD值来诊断氮素营养状况要比光谱反射率的精确度高。     

Quantification of chlorophyll content and classification of nontransgenic and transgenic tomato leaves using visible/near-infrared diffuse reflectance spectroscopy

Visible/near-infrared (vis/NIR) spectroscopy combined with multivariate analysis was used to quantify chlorophyll content in tomato leaves and classify tomato leaves with different genes. In this study, transgenic tomato leaves with antisense LeETR1 (n = 106) and their parent nontransgenic ones (n = 102) were measured in vis/NIR diffuse reflectance mode. Quantification of chlorophyll content was achieved by partial least-squares regression with a cross-validation prediction error equal to 2.87. Partial least-squares discriminant analysis was performed to classify leaves. The results show that differences between transgenic and nontransgenic tomato leaves do exist, and excellent classification can be obtained after optimizing spectral pretreatment. The classification accuracy can reach to 100% using the derivative of spectral data in the full and partial wavenumber range. These results demonstrate that vis/NIR spectroscopy together with chemometrics techniques could be used to quantify chlorophyll content and differentiate tomato leaves with different genes, which offers the benefit of avoiding time-consuming, costly, and laborious chemical and sensory analysis.     

斑潜蝇虫害叶片受害程度对其近红外反射光谱的影响

为探索实现作物虫害自动监测的方法，采用图像处理和光谱分析技术，测定了斑潜蝇虫害叶片的近红外反射光谱，计算了虫害叶片的破损率，对其破损率和干鲜比与近红外分光反射率的关系分别进行了回归分析。结果表明：在某些波段，叶片的破损率和干鲜比均与近红外分光反射率有较好的相关性。叶片的干鲜比与近红外分光反射率关系的决定系数：黄瓜为R²=0.79(在1452 nm)，番茄为R²=0.70(在1450 nm)。叶片的破损率与近红外分光反射率关系的决定系数，黄瓜为R²>0.81(在1436～1468 nm)，番茄为R²>0.69(在1436～1466 nm)。试验和分析结果证明斑潜蝇虫害叶片的虫害程度能很好地被近红外光谱信息反映。     

基于近红外光谱的土壤全氮含量估算模型

土壤全氮是诊断土壤肥力水平和指导作物精确施肥所需的重要信息,建立土壤全氮的近红外光谱估测模型并对建模波段进行优化选择对于土壤养分信息快速获取和精确农业发展具有重要意义。该研究以中国中、东部地区5种主要类型土壤为研究对象,利用近红外光谱仪采集土壤样品的光谱信息,结合近红外区域分子振动特点选取全谱、合频、一倍频、二倍频和N-H基团及其组合的8个波段,采用多元散射校正等多种预处理方法组合进行处理,结合偏最小二乘法(PLS)对每个波谱区域进行定标建模。结果表明,利用4000～5500cm-1波谱区域结合附加散射校正处理过的原始光谱建立的模型精度表现最好,其内部互验证决定系数达到0.90,均方根误差为0.16。经不同类型土壤的观测资料检验,模型验证决定系数为0.91,均方根误差为0.15,相对分析误差RPD为3.40,表明模型具有极好的预测能力。因此,利用近红外光谱可以实现土壤全氮的快速估测,且以合频波段(4000～5500cm-1)为建模区域可以得到更好的预测效果。     

采用叶片光谱反射率预测寒地水稻稻米蛋白质含量

为实现利用水稻叶片光谱指数实时预测稻米蛋白质含量,该研究采集了不同年份中氮素、品种差异下寒地水稻主要生育期（T1拔节期、T2齐穗期、T3结实期）顶部3片叶（L1、L2、L3）的叶片光谱反射率,探究其变化规律以及光谱指数与稻米蛋白质含量的关系,并用P-k、均方根误差（Root Mean Square Error,RMSE）和对称平均绝对百分比误差（Symmetric Mean Absolute Percentage Error,SMAPE）对模型精度进行验证。结果显示：施氮量多则稻米蛋白质含量高,蛋白质含量高的稻米食味值评分低。提高氮肥投入量,叶片反射率在可见光区域内呈降低趋势,而在近红外平台叶片反射率上升。随着生育期的推进,可见光区域内的叶片反射率逐渐上升,叶片反射率在近红外平台表现出先增加后降低的趋势,其变化规律与蛋白质营养转运有着密切联系。对光谱指标和稻米蛋白质含量进行相关分析,T2时期的L2的光谱指数与蛋白质含量的相关性优于其他时期的叶片,其中T2时期L1叶ARI1指标（(1/R550)-(1/R700)）、L2叶CTR1指标（(R695/R420)）以及T3时期L3 叶Rg指标（绿光范围510～560 nm内的最大波段反射率）显示出与蛋白质含量良好的拟合关系,指标验证的P-k分别为0.01、0.01、0.03,RMSE分别为0.19、0.11、0.14,SMAPE分别为1.56%、1.24%、1.44%,其中以T2时期L2叶CTR1指标表现最优,蛋白质含量拟合方程R2为0.75。综上,借助CTR1指标能够实现快捷、无损和实时预测稻米蛋白质含量的目的,达到按质收获以及品质实时监测的要求,促进优质寒地水稻的可持续发展。     

基于冠层光谱特性的水稻叶片含水率模型

基于水稻叶片含水状况与冠层光谱反射率存在关联,尝试构建水稻叶片含水率模型。在水稻生长的孕穗期,同时测量室外水稻冠层光谱反射率和叶片含水率,依据水稻叶片含水率与各光谱波段反射率之间的相关性系数,选取高相关性系数对应的光谱特征波段。采用遗传算法对BP神经网络的初始权值进行优化处理。分别应用BP神经网络和GA-BP-Network、传统多元线性回归方法建立预测模型。试验表明,GA-BP-Network模型的预测含水率值与真实值平均误差率为3.9%,最大误差率为6.1%,均比BP神经网络、传统多元线性回归预测模型有了很大的改善,提高了预测水稻叶片含水率的准确性。     

Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples

This study aimed at assessing the potential of near-infrared reflectance spectroscopy (NIRS) for determining the distribution of soil organic matter (SOM) in particle size fractions, which has rarely been attempted. This was done on sandy soils from Burkina Faso (three sites) and Congo-Brazzaville (one site). Over the total sample set, NIRS accurately predicted carbon (C) and nitrogen (N) concentrations (g kg^?1 fraction) in the fraction <20 μm. When considering Burkina Faso only, predictions were improved in general; those of C and N amounts (g kg^?1 soil) became accurate for the fraction <20 μm but not for the coarser fractions, probably due to heterogeneous SOM repartition. However, most SOM being <20 μm in general, NIRS could be considered promising for determining SOM size distribution.     

不同氮浓度对温室番茄生长发育和叶片光谱特性的影响

营养液培养法研究不同氮浓度对温室番茄的生长发育及叶片光谱特性的影响。结果表明,在01~5 mmol/L范围内,随着营养液中氮浓度的增加,番茄的叶面积指数、株高、叶片数、花穗数、产量和叶片的吸光度都增大,但是超过一定的范围,植株的生长和发育均会受到抑制,说明适宜的氮素促进了植株的生长和发育。其中,处理N15的叶面积指数达到了3.0,叶片数和花穗数分别是26.7和7.0,均高于其它处理。不同处理间番茄产量均存在极显著的关系,以N15处理最高,N5的最低。从叶片的光谱图中可提取出与叶绿素有关的特征波段。在本试验条件下,番茄水培时营养液适宜的氮浓度为15 mmol/L。     

基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究

【目的】 近年来应用无人机进行作物生长、营养和植保信息的快速提取受到广泛关注,但其对作物全生育期营养状况的动态诊断需要明确适宜的色彩参数。本研究通过田间氮水平试验,以无人机为平台利用可见光光谱对夏玉米不同生育期的冠层氮素营养进行监测,对基于可见光RGB图像的色彩参数与传统氮素诊断指标的相关性进行分析,并比较色彩参数的变异系数以探明夏玉米不同生育时期氮素营养诊断的最佳色彩参数。 【方法】 于2015年6—10月,在河北省中国农业大学曲周试验基地设置不同氮水平田间试验,以夏玉米郑单958为供试作物,设5个施氮水平:0、102、145、189和250 kg/hm2 (分别以CK、70%OptN、OptN、130%OptN、ConN表示),4次重复。分别在夏玉米六叶期 (V6)、十叶期 (V10)、吐丝期 (VT)、籽粒建成期 (R2)、乳熟期 (R4) 应用无人机可见光遥感技术获取夏玉米冠层图像,采用Adobe Photoshop软件经过一些图像处理后选用直方图程序提取图像的红光值R、绿光值G、蓝光值B、亮度值L,研究由此计算的12个色彩参数与传统氮素诊断指标 (植株氮浓度、生物量和吸氮量) 的相关性,结合相关系数和变异系数的大小综合分析筛选夏玉米不同生育时期氮素营养诊断的最佳色彩参数。 【结果】 红光值 (R)、绿光值 (G)、亮度值 (L)、绿光标准化值[G/(R + G + B)]、蓝光标准化值[B/(R + G + B)]、绿光与红光的比值 (G/R)、绿光与蓝光的比值 (G/B)、绿光与亮度的比值 (G/L)、红绿蓝植被指数 (RGBVI) 等在不同生育时期均与夏玉米的植株氮浓度、生物量和吸氮量有很好且一致的相关性,结合图像色彩参数的变异系数综合分析后,G/(R + G + B)、G/L在各生育时期与夏玉米常规的氮营养诊断指标有极显著的相关性 (P < 0.01),相关系数介于0.641～0.944之间,且变异系数小而稳定,介于0.93%～4.30%之间,优于其他光谱参数,可作为基于无人机可见光技术用于各时期氮素营养动态诊断的最佳色彩参数。 【结论】 应用无人机可见光遥感进行夏玉米氮素营养动态诊断具有结果可靠、便捷、高效、非破坏性的优点,本研究结果对应用该技术进行较大区域的作物氮素营养动态诊断提供了科学依据。      

利用红边参数估计叶片叶绿素含量

Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content (LCC). This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed (Brassica napus L.) and wheat (Triticum aestivum L.) crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge parameter, defined as red edge symmetry (RES). Compared to the commonly used red edge parameters (red edge position, red edge amplitude, and red edge area), RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm (R675 and R755) and reflectance of red edge center wavelength at 718 nm (R718), with the equation RES = (R718－R675)/( R755－R675). In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.     

利用光谱反射率预测烤烟叶片烟碱含量

以ASD Field spee FR 2500光谱仪测定了不同生育期不同氮钾处理的烤烟叶片光谱反射率,通过光谱反射率、一阶导数光谱和光谱特征变量与烟碱含量的相关分析和逐步回归模型筛选出特征波长,并对回归模型的预测效果进行检验.结果表明,一阶导数光谱回归方程预测效果最好、光谱反射率回归方程次之、特征变量回归方程最差.筛选出光谱反射率特征波长为1792 nm、一阶导数光谱特征波长为810 nm.光谱反射率和一阶导数光谱在427～712、741～810、1382～1879、1905～1969、2067～2338 nm范围内与烟碱含量相关性强.本研究为采用高光谱技术预测烟叶烟碱含量提供了理论参考.     

可见/近红外光谱技术无损检测果实坚实度的研究

该研究的目的是建立可见/近红外光谱与梨果实坚实度之间的数学模型,评价可见/近红外光谱技术无损测量梨果实坚实度的应用价值。在可见/近红外光谱区域(350～1800 nm),试验对比分析了不同测量部位、不同光谱预处理方法和不同校正建模算法的梨果实坚实度校正模型。结果表明：赤道部位吸光度一阶微分光谱的偏最小二乘回归所建梨果实坚实度校正模型的预测性能较优,其校正和预测相关系数分别为0.8779和0.8087,校正和预测均方误差分别为1.0804 N和1.4455 N。研究表明：可见/近红外光谱技术无损检测梨果实坚实度是可行的。     

Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits

The influence of parent and harvest year on the determination of oil, moisture, oleic acid, and linoleic acid contents in intact olive fruit was studied by near-infrared spectroscopy (NIRS). Spectral data from 400 to 1700 nm were recorded on 437 fruit samples collected in 1996 and 1997 from seedling plants derived from three different female parents. Partial least squares models were developed using samples for each year and for each female parent separately and were validated against the other groups. Calibration models were accurate enough to predict all constituents in new samples from a different female parent but were not transferable across years. However, a calibration equation of sufficient accuracy was obtained from the combined data set (r values of 0.94, 0.93, 0.84, and 0.88 and RMSECV values of 1.33, 1.88, 4.73, and 2.91 for oil, moisture, oleic acid, and linoleic acid contents, respectively). These results demonstrate the utility of NIRS as a selection tool in olive breeding programs.     

Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS)

Soil degradation processes have dramatically increased in their extent and intensity over the last decades. Progressively, actions have been taken in order to evaluate and reduce the major threats that have already wreaked havoc on soil conditions. Efficient and standardized monitoring of soil conditions is thus required but soil quality research is facing an important technological challenge because of the number of properties involved in soil quality. The objective of the present review is to examine critically the suitability of near-infrared reflectance spectroscopy (NIRS) as a tool for soil quality assessment. We first detail the soil quality-related parameters (chemical, physical and biological) that can be predicted with NIRS through laboratory measurements. The ability of imaging NIRS (airborne or satellite) for mapping a minimum data set of soil quality is also discussed. Then we review the most recent research using soil reflectance spectra as an integrated measure of soil quality, from global site classification to the prediction of specific soil quality indices. We conclude that imaging NIRS enables the direct mapping of some soil properties and soil threats, but that further developments to solve several technological limitations identified are needed before it can be used for soil quality assessment. The robustness of laboratory NIRS for soil quality assessment allows its implementation in soil monitoring networks. However, its routine use requires the development of international soil spectral libraries that should become a priority for soil quality research.     

Chlorophyll meter estimates leaf area‐based nitrogen concentration of rice

Abstract

The chlorophyll meter (SPAD‐502) can be used to diagnose the nitrogen (N) status of rice (Oryza sativa L) plants to determine the need for fertilizer‐N topdressing. However, accurate estimation of leaf N concentration on a dry weight basis (N_dw) by SPAD requires the adjustment of SPAD values for specific leaf weight (SLW). The objective of this study was to determine if SPAD estimates of leaf N concentration on a leaf area basis (Na) without correcting for SLW. SPAD values were measured from different genotypes at various growth stages with the chlorophyll meter. Leaf N concentration was determined by micro‐Kjeldahl procedure and expressed based on dry weight and leaf area. Specific leaf weight was calculated as the ratio of leaf dry weight to leaf area. At each growth stage, SPAD estimated Na better than Ndw, especially at the middle and later growth stages. When data for all stages were pooled, SPAD values also correlated with Na better than with Ndw (r value of 0.81 versus 0.43). This finding was also true across 80 genotypes. For estimation of N_a by SPAD, therefore, no adjustment of SPAD values for SLW is needed.