用高光谱植被指数估算水稻乳熟后叶片和穗的色素含量

 分析测定了大田条件下2个水稻品种在3个氮素水平下的剑叶和穗从乳熟期至收割的光谱反射率(350~2500 nm)及对应剑叶和穗的叶绿素（Chl）、类胡萝卜素（Car）含量,并利用相关分析研究了11个植被指数与剑叶、穗的叶绿素含量之间的关系。mSR705、mND705在试验范围各叶绿素含量水平下，都表现极显著的相关性。mSR705、mND705与叶片、穗叶绿素含量进行线性回归，两者拟合R2分别为0.9319和0.9488（n＝48）。植被指数与类胡萝卜素、Car/Chl间的相关性分析表明，光化学反射指数（PRI）与剑叶、穗Car/Chl都有很好的负相关（R2=0.7745，n=48）, 可以用来预测不同植被结构的Car/Chl;R760/R500与剑叶Car/Chl和穗Car含量也具有较好的相关性。结果表明，mSR705、mND705和PRI等指数可用于估算叶片、穗的色素含量，作为水稻成熟度的监测指标。     

大豆叶片叶绿素含量与光谱的特征分析

用Unispec光谱分析仪和SPAD叶绿素仪测定了2个品种从初花期到成熟期的反射光谱和叶绿素含量.用SPAD叶绿素仪测定结果表明:每个时期均为合交98-1667的叶绿素含量高于合丰55号;用Unispec光谱分析仪测定结果显示:合丰55号大豆的光谱反射率高于合交98-1667,并且发现在可见光510 nm～610 nm处有一个叶绿素反射吸收峰,此吸收峰是大豆叶片上特有的一个特征值.数据统计分析表明:叶片叶绿素含量与光谱植被指数mSR705、mND705,和PSSRc具有极显著相关性.说明可以通过测量叶片光谱的方法来监测大豆叶片叶绿素含量.     

不同氮肥水平下大豆叶片光谱反射率与叶绿素含量的相关性研究

用Unispec光谱分析仪和SPAD-502叶绿素仪测定不同生育时期不同氮肥水平大豆叶片光谱反射率及叶绿素含量,并分析了光谱植被指数与叶绿素含量的相关性。结果表明:不施氮肥处理光谱反射率高于施氮处理,随着施氮量的增加,大豆叶片光谱反射率下降,并初步断定结荚期是大豆氮素光谱营养诊断的敏感时期;随着氮肥水平的提高叶绿素含量增加;整个生育时期,除鼓粒期不施氮处理外其它处理的植被指数mND705与叶绿素含量均呈极显著正相关;在花期和结荚期,各处理的mSR705与叶绿素含量呈极显著正相关,PSSRc与叶绿素含量呈极显著负相关。     

小麦叠加叶片的叶绿素含量光谱反演研究

为了给田间冠层水平叶绿素含量高光谱反演研究提供参考,研究了小麦单层及叠加叶片不同波长光谱反射率及几种常用植被指数对叶绿素含量的响应特征。结果表明,可见光波段的绿光到红光波段范围内叶片光谱反射率与叶绿素含量存在良好的相关关系,其中在绿光反射峰550 nm附近和红边区域的705 nm附近反射率都可以用来预测叶绿素含量。红谷吸收表现为随叶绿素含量提高而蓝移的特征。常用植被指数NDVI在本研究中对小麦叶片的叶绿素含量的监测效果并不理想。SR705虽然与单层叶片叶绿素含量相关性较好,但是对叠加多层叶片的叶绿素含量反演效果不好。光谱参数中TCARI对单层叶片和不同叠加层数的叶片均有最好的预测能力,因此可以利用TCARI监测小麦叶绿素含量,进而用于评价其光合特性。     

不同土壤质地小麦叶片叶绿素的高光谱响应及估测模型

为给小麦生长过程中叶绿素的实时监测和氮肥调控提供参考,设置3种不同土壤质地(沙土、壤土和粘土)、5种不同施氮水平(0、120、225、330和435kg·hm-2)和3个河南省主栽小麦品种(矮抗58、周麦22和郑麦366),同步测定小麦主要生育时期冠层光谱反射率和叶绿素(Chla+b)含量,系统分析了3种土壤质地条件下小麦Chla+b含量与350~1 050nm波段范围内冠层光谱参数的相关关系。结果表明,3种土壤质地下小麦叶绿素的冠层光谱响应趋势基本一致。光谱指数REPIG和mND705对叶片Chla+b含量的监测效果较好,建模决定系数分别为0.76和0.75。利用独立样本数据对用于建模的此二光谱参数进行检验,其预测效果表现较为稳定,预测决定系数分别为0.87和0.85,均方根偏差分别为0.46和0.48。说明利用光谱指数REPIG和mND705为自变量建立的估测模型可以较好地预测当地生产条件下小麦叶片叶绿素,同时为氮肥施用及调控提供技术依据。     

光谱仪与SPAD测定马铃薯叶绿素含量的比较

通过Unispec-SC光谱仪和SPAD-502叶绿素计两种方法测定马铃薯叶片叶绿素相对含量的比较,结果表明:马铃薯在310～730 nm波段选取510 nm、650 nm和680 nm 3个波段的光谱反射率,在730～1130 nm波段,选取820 nm和940 nm 2个波段的光谱反应率,它们与叶绿素含量的相关性呈显著水平。在940 nm和510 nm组合的波段计算的植被指数与叶绿素相关性最好,并且以NDVI的相关系数最高,r=-0.9546,达到了极显著水平。Unispec-SC光谱仪比SPAD-502叶绿素计预测结果更为准确。     

光谱诊断马铃薯叶片氮素敏感波段的研究

利用Unispec-SC光谱仪测定马铃薯叶片光谱反射率,寻找马铃薯叶片氮含量的敏感波段并计算相应的植被指数。结果表明:在可见光波段范围内,550 nm和580 nm 2个波段的光谱反射率与氮含量的相关性较好;在近红外区域,820 nm、900 nm和1 005 nm 3个波段与氮含量的相关性达到了显著水平。利用上述波段计算植被指数,在1 005 nm和580 nm组合的波段,植被指数与氮含量相关性最好并且以DVI的相关系数最高,r=-0.8994,达到了显著水平。     

基于不同玉米品种叶片高光谱的SPAD值估测模型研究

通过研究不同玉米品种叶片SPAD值与高光谱参数的关系,建立玉米叶片SPAD值估测模型,并对模型进行品种间精度检验。通过两年试验,测定不同玉米品种的叶片SPAD值及其高光谱数据,综合分析叶片SPAD值与高光谱反射率、反射率一阶导数及其光谱参数的相关关系,对玉米叶片SPAD值估测模型进行构建。玉米叶片SPAD值与高光谱反射率最敏感波段在550和710 nm附近,反射率一阶导数最敏感波段出现在500~750 nm范围内。叶片SPAD值与单波段反射率的相关性要高于其一阶导数,以550 nm附近光谱反射率构建的模型对大多数品种的叶片SPAD预测值平均误差最小。     

基于高光谱指数的叶片尺度叶绿素荧光参数反演

在叶片尺度上,基于高光谱植被指数反演实际光合速率(Phi2)、非调节的光能耗散(PhiNO)、非光化学淬灭(PhiNPQ)、相对叶绿素含量(RC)4个叶绿素荧光参数,分析不同氮素处理下叶片光谱反射率和4个叶绿素荧光参数在不同时期的变化特征。结果表明,可见光波段在过量施氮下叶片反射率低于不施氮处理;在近红外波段,叶片光谱反射率随着施氮量的增大而增大。随着玉米的生长,不施氮处理下Phi2逐渐减少,PhiNPQ逐渐增加;过量施氮下Phi2先增加后减少,PhiNO和PhiNPQ先降低后增加。RC在不同施氮条件下均随着生育时期发展先增加后减少。Phi2和PhiNPQ与归一化植被指数(NDVI)的相关性最好,PhiNO与改进型叶绿素吸收比值指数(MCARI)的相关性最好,RC与红边植被指数(CIred edge)的相关性最好。     

反射光谱法估计小麦叶片表皮蜡质含量的初步研究

为了探讨利用冠层反射光谱技术估计小麦叶片表皮蜡质含量的可行性,以小麦高叶片表皮蜡质含量材料2912与低叶片表皮蜡质含量品种普冰201和晋麦47及其杂交构建的F2:3株系为材料,通过氯仿提取称重法测定了小麦抽穗期的旗叶表皮蜡质含量,并采用FieldSpec 3测定了冠层反射光谱,分析小麦冠层反射光谱与叶片表皮蜡质含量之间的关系。结果表明,三个亲本以及株系间蜡质含量差异显著。高蜡质材料的可见光波段反射率整体高于低蜡质材料,短波长波段光谱反射率与叶片表皮蜡质含量相关性较高。以550和675nm波长的反射光谱为基础的单波/差值指数[R550/(R550-R675)]能较好地反映小麦叶片蜡质含量,两F2:3群体拟合模型的r2值分别为0.761和0.679,回归方程分别为y=0.07x-0.575和y=0.088x-1.481。     

基于高光谱遥感的小麦黄花叶病害等级监测研究

为了构建小麦黄花叶病的遥感监测技术,在小麦返青期、拔节前期和拔节后期测定了不同黄花叶病等级下的冠层反射率,并同步调查与病害等级相关的小麦株高、含水量、氮含量、色素含量等农学参数,筛选出适宜监测小麦黄花叶病的植被指数,并构建病害等级监测模型。结果表明,小麦黄花叶病的反射光谱敏感波段在返青期和拔节前期集中于560～720 nm范围,而拔节后期则集中于800～900 nm区域。随病害等级的增加,光谱反射率在可见光波段逐渐增加,而在近红外波段区域降低。植被指数与病害等级相关性在不同生育时期间存在显著差异,整体上以拔节前期最好,决定系数(r)为0.72～0.82,而拔节后期模型精度急剧下降(r=0.26～0.72)。在植被指数中,整体上以表征色素变化的mND705模型预测精度最好,r和RMSE分别为 0.59～0.68和0.79～0.98。采用偏最小二乘回归(PLSR)建立黄花叶病害分级模型,三个时期的模型精度均高于植被指数模型,且整体上以返青期和拔节期前期估算效果较好,模型验证r为0.93～0.97,RMSE为0.24～0.32。因此,利用PLSR模型可以准确评价返青至拔节期前期小麦黄花叶病害等级。     

基于主成分分析和随机森林回归的冬小麦冠层叶绿素含量估算

为提高冬小麦冠层光谱对叶绿素含量的估算精度,以陕西省乾县冬小麦为研究对象,利用SVC-1024i光谱仪和SPAD-502型叶绿素仪实测了冬小麦冠层反射率和叶绿素含量,分析了一阶导数光谱、10种特征参数和9种植被指数与叶绿素含量的相关性,并利用主成分分析(PCA)对叶绿素敏感的可见光波段(390～780 nm)一阶导数光谱进行降维,将特征值大于1的主分量结合特征参数和植被指数形成不同的输入变量,用偏最小二乘回归和随机森林回归构建冬小麦冠层叶绿素估算模型,并利用独立样本对模型进行验证。结果表明,小麦冠层叶绿素含量与一阶导数光谱在751 nm处的相关性最高(r=0.71),特征参数中红边蓝边归一化(SDr-SDb)/(SDr+SDb)与叶绿素含量的相关性最高(r=0.66),植被指数(VI)中修正归一化差异指数(mND705)相关性最高(r=0.74)。在输入变量相同的情况下,基于随机森林(RF)回归的预测模型均优于偏最小二乘回归(PLSR)模型,其中PCA-VI-RF模型的各精度指标均达到最优(r²=0.94,RMSE=1.05,RPD=3.70),是冬小麦冠层叶绿素...     

Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance

Non-destructive and quick assessment of leaf nitrogen (N) status is important for dynamic management of nitrogen nutrition and productivity forecast in crop production. This research was undertaken to make a systematic analysis on the quantitative relationship of leaf nitrogen concentrations (LNCs) to different hyperspectral vegetation indices with multiple field experiments under varied nitrogen rates and varied types in rice (Oryza sativa L.). The results showed that some published indices had good relations with LNC such as two-band indices, R₇₅₀/R₇₁₀ (ZM), Gitelson and Merzlyak index two (GM-2), R₇₃₅/R₇₂₀ (RI-1dB), R₇₃₈/R₇₂₀ (RI-2dB) and the normalized difference red edge index (NDRE), three-band indices, adjusted normalized index 705 (mND705), physiological reflectance index c (PRI_c), terrestrial chlorophyll index (MTCI), and red edge position derived with four point linear interpolation (REP_LI). Three-band indices performed better than two-band indices, with MTCI exhibiting the best logarithmic relation to LNC in rice. Then, hyper-spectral vegetation indices computed with random two bands (λ₁ and λ₂) from 400 to 2500 nm range were related to LNC of rice. The results indicated that two-band indices combined with bands of 550–600 nm and 500–550 nm in green region had good relationships with LNC, and simple ratio index SR(533,565) performed the best in all two-band indices, similar to the published three-band indices (mND705, PRI_c and MTCI). New three-band indices R₄₃₄/(R₄₉₆ + R₄₀₁) and R₇₀₅/(R₇₁₇ + R₄₉₁) were proposed for prediction of LNC with improved ability over the SR(533,565) and published spectral indices. Moreover, R₇₀₅/(R₇₁₇ + R₄₉₁) performed well in all the data from ground spectra, modeled AVIRIS and Hyperion spectra, and acquired Hyperion image hyperspectra. The R₄₃₄/(R₄₉₆ + R₄₀₁) also exhibited well in both ground and modeled AVIRIS and Hyperion image spectra, but could not be tested with the acquired Hyperion image because of the absence in radiometric calibration of the bands less than 416 nm. Overall, the newly developed three-band spectral index R₇₀₅/(R₇₁₇ + R₄₉₁) should be a good indicator of LNC at ground and space scales in rice. Yet, these new indices still need to be tested with more remote sensors based on ground, airborne and spaceborne, and verified widely in other ecological locations involving different cultivars and production systems.     

Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice

Abstract

Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-¹ DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760?1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460?710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.     

有机肥化肥配施的双季晚稻群体冠层光谱特征研究

以不同施肥模式为基础,分析了晚稻群体冠层光谱反射率、一阶微分光谱和归一化光谱特征,并对叶片氮含量、氮积累量、产量、叶面积指数和叶干物质积累进行了相关性分析,构建了以高光谱特征参数为自变量的水稻氮素营养诊断模型。结果表明,叶片氮素含量与665 nm处冠层光谱反射率呈极显著相关性(p0.001),与554 nm和672 nm处的一阶微分光谱也呈极显著相关性(p0.001);以λr构建的指数函数y=684.91e0.028x,决定系数(R2)为0.90、(SDr-SDb)/(SDr+SDb)构建的指数函数y=0.66e0.11x,决定系数(R2)为0.88,均能很好地诊断在有机肥和无机肥配施模式下的水稻氮素营养。     

基于高光谱数据的晚籼稻品种剑叶SPAD值估测

本研究测定了长江中下游不同晚籼稻品种的剑叶SPAD值及反射光谱数据,分析了原始光谱及其变换数据与SPAD值的相关关系,建立了不同晚籼稻品种剑叶SPAD值估测模型,并采用平均偏差率对模型精度进行品种间验证.结果表明,水稻剑叶SPAD值与原始光谱的敏感波段位于710～720 nm之间,与一阶微分光谱的敏感波段为690～70...