基于日光诱导叶绿素荧光与反射率光谱的小麦条锈病探测研究

综合利用反射率光谱在作物生化参数探测的优势和叶绿素荧光在光合生理诊断的优势,开展了日光诱导叶绿素荧光(SIF)和反射率光谱指数协同的小麦条锈病光谱探测研究,以期提高小麦条锈病病情严重度的预测精度。利用O_2-A波段(760 nm)的SIF信号和对小麦条锈病病情严重度敏感的7种反射率光谱指数,基于支持向量机(SVM)、逐步回归(SR)以及神经网络(BP)算法,定量分析了反射率光谱指数和反射率光谱指数与SIF协同的小麦条锈病病情严重度(DI)光谱探测模型的预测精度。结果表明:①SIF与小麦条锈病病情严重度之间存在极显著的负相关关系,SIF与DI间的响应能有效地应用于小麦条锈病的遥感探测;②SIF结合反射率光谱指数的小麦条锈病病情严重度光谱模型探测精度均高于反射率光谱指数模型,SIF能够显著提高小麦条锈病病情严重度的光谱探测精度;③无论是利用反射率光谱指数还是SIF结合反射率光谱指数作为小麦条锈病病情严重度预测模型的输入参数,训练模型以BP模型的预测精度最高,但验证结果表明SVM与SR方法构建的病情严重度预测模型效果较优。     

多源数据小麦条锈病预测研究

小麦条锈病是导致小麦大规模减产的气传性病害,其传播扩散过程受多种因素影响,常用的作物病害气象预测模型难以准确模拟。为实现小麦条锈病发病率的精准预测,提出一种基于气象和遥感数据建立的SEIR-StripeRust动态预测模型。以甘肃省陇南地区为研究区,首先基于气象数据和MODIS遥感数据分别构建气象因子和植被指数,然后与发病率进行相关性分析筛选敏感因子并耦合基本感染率,进而建立SEIR-StripeRust模型,最后采用后向传播神经网络（BPNN）、支持向量回归（SVR）和多元线性回归（MLR）模型对比验证SEIR-StripeRust模型的有效性。结果表明：平均气温、相对湿度和归一化植被指数与小麦条锈病发病率显著相关,其建立的SEIR-StripeRust模型预测精度最高,决定系数R² 达到0.79,均方根误差RMSE为0.10,平均绝对误差MAE为0.09,均优于相同特征变量下的BPNN、SVR和MLR模型。研究结果表明SEIR-StripeRust模型能够有效预测小麦条锈病发病率,并为县域尺度的小麦条锈病预测和精确防控提供技术支持。     

基于植被指数的叶绿素密度遥感反演建模与适用性研究

利用遥感数据反演叶绿素密度是对作物长势进行评估的有效手段.本文利用实测冬小麦和夏玉米两种作物、不同生育期的冠层光谱和叶片叶绿素含量数据,收集了14种光谱指数,分析各种光谱指数的叶绿素密度遥感模型的精度.优选了其中的8种植被光谱指数,建立了植被指数与叶绿素密度之间的回归模型,并利用不同生育期小麦数据和玉米数据对各模型进行验证,分析评价它们对不同生育期、不同作物类型的适用性.研究发现:利用SRI、RVI I、R-M和MTCI 4种植被指数所建模型对冬小麦不同生育期数据适用性较好,各生育期冠层叶绿素密度反演相对误差优于27％.其中,MTCI模型对不同作物类型的适用性最好,冠层叶绿素密度反演相对误差优于35％.     

基于719 nm水汽吸收波段的日光诱导叶绿素荧光反演研究

日光诱导叶绿素荧光(SIF)是一种植物光合作用直接探测新方法。目前O_2-A和O_2-B吸收线波段的叶绿素荧光填充效应被广泛应用于探测近红外(760 nm)和红光波段(687 nm)的植被冠层SIF信号。SIF光谱范围为650～800 nm,虽然水吸收波段(719 nm)介于叶绿素荧光发射峰值690 nm和740 nm之间,且具备较强的光谱吸收特征,但该水汽吸收光谱特征尚未应用于冠层SIF探测,因此,基于模型模拟和野外实验观测数据,使用夫琅禾费暗线SIF反演法,评价了基于719 nm波段水吸收波段的SIF反演潜力,其中野外光谱数据采用ASD FieldSpec Pro便携式地物光谱仪(3 nm分辨率)测量。首先,利用FLD、3FLD、iFLD等3种经典的SIF反演方法,检验和对比分析了719水汽吸收波段的SIF反演性能,结果表明使用水吸收线比使用O_2-B吸收线表现更优,反演RMSE为0.154 W/m~2/μm/sr。其次,定量计算了水汽和氧气吸收波段SIF反演的敏感度和不确定性,结果表明,719水汽吸收波段与O_2-B吸收线相比,其吸收线内外的反射率和荧光比值估算误差对SIF反演误差的贡献更小,但是显大于比02-A波段。最后,利用野外多角度和日变化观测实验数据,检验和分析了三个大气吸收波段的SIF反演结果,发现719 nm水吸收波段的冠层SIF与O_2-A和O_2-B氧气吸收波段具有相似的角度变化和日变化特征,表现为后视和热点方向的SIF高、前视和暗点方向的SIF低,以及中午SIF高、早晚SIF低。研究表明利用719 nm波段的水汽吸收波段的光谱信息,可以准确反演近地面冠层SIF信号,研究结果为近地面冠层SIF观测提供了一个新的波段。     

全球日光诱导叶绿素荧光卫星遥感产品研究进展与展望

卫星反演的日光诱导叶绿素荧光（Solar-Induced chlorophyll Fluorescence, SIF）是全球植被生产力遥感监测的理想工具。现有的诸多大气成分探测卫星的高光谱载荷可以满足卫星平台SIF遥感探测的需求,中国和欧洲也计划发射专门的SIF卫星探测器。国内外学者生产了一系列全球SIF卫星遥感产品,并开展了SIF产品时空尺度扩展研究,为SIF应用研究提供了丰富的数据源,但现有的全球SIF产品仍然存在诸多局限性。系统梳理了现有和计划发射的SIF卫星载荷、目前公开发布的SIF卫星遥感产品、以及SIF时空尺度扩展产品,并从应用需求的角度出发,总结了现有全球SIF产品存在的问题和后续SIF卫星探测计划的发展方向,为现有SIF卫星产品的应用以及未来SIF探测卫星载荷方案的设计提供参考。     

大豆叶面积的高光谱模型

以ASD FieldSpec-Vnir光谱仪实测不同生长季大豆的冠层反射率,同期采集对应大豆LAI,然后逐波段分析冠层光谱反射率、导数光谱与大豆LAI的相关关系;并采用单变量线性回归逐波段分析了冠层光谱反射率、导数光谱与大豆LAI确定性系数随波长的变化趋势,建立了以近红外与可见光波段冠层光谱反射率的比值植被指数RVI与大豆LAI的高光谱遥感估算模型。结果表明,冠层光谱反射率在350￣680nm、760￣1050nm波谱区与大豆LAI相关性较大,而在红边区680￣760nm的相关性变化较大;导数光谱在红边区与大豆LAI相关程度高。通RVI方式建立的遥感估算模型能较为准确估算大豆LAI,通过对红外与蓝波段建立的RVI指数与大豆LAI的回归模型,表明其预测大豆LAI的能力较好,有进一步研究的必要;通过对比发现,神经网络模型可以大大提升高光谱反演大豆LAI的水平,模型的确定系数R2为0.9661,而总均方根误差RMSE仅为0.446m2.m-2。     

玉米叶片花青素相对含量高光谱遥感反演

针对传统植物叶片色素测量存在的问题|提出了一种采用高光谱遥感技术反演玉米叶片花青素相对含量的方法。以含花青素的玉米叶片为对象|获取玉米叶片的花青素相对含量及高光谱反射率|分析350～1 000 nm波段范围内玉米叶片的反射光谱特征|建立基于敏感波段、已有光谱指数、新光谱指数的玉米叶片花青素相对含量反演模型|并进行分析、比较。结果表明|玉米叶片花青素相对含量的敏感波段为548 nm；新光谱指数为521和698 nm组成的比值指数（RI(521,698)）、554和704 nm组成的差值指数（DI(554,704)）、557和701 nm组成的归一化指数（NI(557,701)）。基于RI(521,698)建立的一元线性和一元二次模型及DI(554,704)建立的一元二次模型的拟合R2均大于0.78|预测R2均大于或等于0.73|RMSE小于0.12|是进行玉米叶片花青素相对含量高光谱遥感反演的最优模型|说明在一定的精度范围内|新光谱指数具有无损反演玉米叶片花青素相对含量的潜力。     

数据驱动SIF反演算法在塔基观测中的应用效果评价

日光诱导叶绿素荧光（Solar-Induced Chlorophyll Fluorescence, SIF）与植被光合作用关系密切,能够揭示植被真实生理状况,准确获取SIF信息对陆地生态碳循环和全球植被监测具有重要意义。以3FLD算法的SIF反演结果和NIRvR为参考,开展了基于塔基平台的数据驱动SIF反演算法性能评价研究。首先利用塔基连续冠层光谱观测数据,分析了SVD算法在不同大气窗口的SIF反演效果;其次利用大气校正前后的实测数据,探究大气因素对SVD算法反演SIF的影响程度;最后,将实测数据按照光照条件区分,对比了天气稳定与天气波动条件下,基于SVD算法的SIF反演结果的稳定性。结果表明：（1）SVD算法在735～759 nm（不包含大气吸收波段）、745～780 nm（包含大气吸收波段）窗口的SIF反演精度较高;（2）SVD算法的SIF反演精度受大气影响远小于3FLD算法;（3）当光照条件剧烈变化时,使用SVD算法可以有效克服FLD类SIF反演算法对同步太阳光谱观测的依赖;即使光照迅速变化,基于SVD算法依旧能够得到稳定可靠的SIF反演结果。综上所述,SVD算法对于塔基SIF反...     

基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段,以此快速反演叶面积指数（Leaf Area Index, LAI）对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象,利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段,并以此计算6种植被指数（Vegetation Index,VI）,利用6种回归模型分别对单一特征波段和VI进行反演建模,以实测LAI评定模型精度。研究表明,光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感,以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58;RMSE为0.16~0.18,其中636 nm建立的模型(LAI=21.86exp(-29.47R₆₃₆))相比其他反演模型预测精度较高（R2=0.58,RMSE=0.16）;6种植被指数与LAI高度相关,相关性系数R ²为0.85~0.86,以此建立的反演模型相比单一特征波段反演模型精度有所提高,R2为0.66~0.72,RMSE为0.12~0.14;其中mNDVI构建的LAI估算模型（LAI=exp(2.76~1.77/mNDVI))精度最高（R2=0.72,RMSE=0.13）。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段,为指导精细化尺度作物管理提供依据。     

利用高光谱植被指数估算棉花干物质积累的模型研究

利用高光谱仪,对新疆棉花（2个品种4种配制方式）生物量和高光谱数据进行分析,经过多元统计分析与光谱微分处理,建立了基于比值植被指数（RVI）和归一化植被指数（NDVI）的5种函数形式的棉花干物质积累估测模型,相关系数均达到了极显著水平（a=1％,n=16）。基于RVI与NDVI构建的估测模型,后者比前者有更高的估测精度,指数函数、对数函数和双曲线函数的模型可以产生较高的估测精度;一阶微分光谱数据与棉花干物质积累量的逐步回归相关分析表明,相关系数的最高值发生在756nm（r=-0．6749,n=16）,由756nm波段处的微分数值建立的回归模型,估测精度较高,具有实际应用的潜力。     

Detection of Wheat Stripe Rust Using Solar-induced Chlorophyll Fluorescence and Reflectance Spectral Indices

Detection of wheat stripe rust is important for agriculture management and decision,this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly,the solar-induced chlorophyll fluorescence (SIF) at O₂-A band (760 nm) was calculated using the 3FLD algorithm,and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then,three classic statistical modelling methods,including Support Vector Machine (SVM),Stepwise Regression (SR) and BP neural network (BP),were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:(1) there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.(2) the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.(3) compared to the SVM and SR methods,the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However,the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.     

基于Cubist的中国植被区域叶绿素荧光数据重建

日光诱导叶绿素荧光(Solar-Induced chlorophyll Fluorescence,SIF)是植物在太阳光照条件下,在光合作用过程中发射出的光谱信号(650～800 nm),SIF相比于植被指数等参数更能直接地反映植被光合作用的相关信息,为大尺度GPP估算带来了新的途径.但目前卫星SIF数据或存在分辨率较...     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

基于TROPOMI叶绿素荧光遥感的冬小麦旱情监测

针对太阳诱导叶绿素荧光（Solar-Induced chlorophyll Fluorescence, SIF）可以有效指示陆表植被水分胁迫的特点,提出了归一化叶绿素荧光干旱指数（Normalized SIF Drought Index, NSDI）用于黄淮海地区冬小麦旱情监测。该方法首先基于哨兵-5p卫星（Sentinel-5p）对流层观测仪（Tropospheric Monitoring Instrument, TROPOMI）传感器反演得到的SIF原始产品集,通过0.1°等经纬步长栅格化处理为空间连续数据,然后基于时间序列分析进行了缺失值线性插补,再经过S-G滤波重建获得了高时空分辨率荧光数据集。以此数据集为基础,结合研究区冬小麦分布数据构建NSDI指数。通过选取典型旱情事件对比分析,NSDI指数与同期归一化植被指数（Normalized Difference Vegetation Index, NDVI）以及温度植被干旱指数（Temperature Vegetation Drought Index, TVDI）都有良好的相关性,其中与NDVI的R²为0.60,与TVDI的R²为0.41;NSDI指数与野外土壤水分调查结果也高度相关,其中河北样区R²为0.53,山东样区R²为0.54,整体R²为0.51;通过物联网监测数据分析显示,NSDI指数可以在优于2 d的滞后期内响应旱情的变化,其变化趋势与田间土壤水分保持高度相关。实验结果表明：NSDI指数可以在时空尺度上有效指示黄淮海地区冬小麦旱情。     

小麦叶部常见病害特征提取及识别技术研究

选取小麦叶部常见病害图像,利用图像处理技术进行病害种类的识别。将图像由RGB彩色空间转换到HSV颜色空间,提取相关的颜色特征（色相和饱和度）,接着提取几何形状特征（周长、面积、矩形度、似圆度、偏心率等）,通过分析样本图像得到每种病害的特征值范围,利用特征值对未知样本进行病害识别。系统以白粉病和锈病（叶锈病、条锈病和秆锈病）为研究对象,根据颜色特征对白粉病和锈病加以识别,然后根据几何形状特征对叶锈病、条锈病和秆锈病进行识别,操作简单方便,识别准确率达96%以上。实验结果表明,选取的颜色特征和几何形状特征对4种小麦叶部常见病害的识别是有效且可行的。     

基于高光谱植被指数的加工番茄生长状况监测研究

利用ASD地物非成像高光谱仪,获取2个加工番茄品种4水平施氮量和3种配置种植方式6个关键生育时期冠层的反射光谱数据,通过计算得到归一化植被指数(NDVI)、比值植被指数(RVI)、修改型二次土壤调节植被指数(MSAVI2)和红边归一化植被指数(RENDVI),并分别与其冠层叶绿素密度(CH.D)、叶面积指数(LAI)、地上鲜生物量(AFBM)和地上干生物量(ADBM)进行相关分析,经检验,相关系数均达到1%的极显著水平。其中RENDVI与CH.D的线性相关模型,RVI与LAI的幂指数函数模型的相关性最好(RRENDVI-CH.D=0.8034**,RRVI-LAI=0.8703**,n=54,α=1%),用上述2个相关模型方程分别估算加工番茄CH.D和LAI,实测值与估测值之间均呈极显著的线性相关关系(R实测CH.D-估测CH.D=0.8113**,R实测LAI-估测LAI=0.8546**,n=54,α=1%),估算精度分别为85.5%和86.3%。试验结果表明,用高光谱植被指数,可以对加工番茄冠层CH.D、LAI、AFBM和ADBM进行遥感估算,实现对加工番茄生长状况的实时、无损、非接触和定量的高光谱监测研究。     

Assessing the wavelength-dependent ability of solar-induced chlorophyll fluorescence to estimate the GPP of winter wheat at the canopy level

Recent studies have demonstrated that solar-induced chlorophyll fluorescence (SIF) can offer a new way for directly estimating the terrestrial gross primary production (GPP). The main objective of this study is to investigate whether the red or far-red SIF is a better indicator of GPP using both simulations by the SCOPE model (Soil Canopy Observation, Photochemistry and Energy fluxes) and the observations of winter wheat at the canopy level. The results showed that: (1) both far-red SIF and GPP increased with leaf area index (LAI), whereas the red SIF quickly reached its saturation with an LAI value of 2 due to the strong reabsorption effect; (2) the diurnal GPP could be robustly estimated from the SIF spectra for winter wheat at each growth stage, whereas the correlation weakened greatly at the red band if all the observations made at different growth stages or all the simulations with different LAI values were pooled together – a situation that did not occur at the far-red band; (3) the SIF-based GPP models derived from the 2016 observations were well validated using the data set from 2015, with a root mean square error (RMSE) value of 0.128 and 0.133 (mg m^?2 s^?1) at the oxygen-A (O₂-A) band and oxygen-B (O₂-B) band, respectively. Therefore, the far-red SIF may be more reliable for mapping GPP for remote-sensing applications with heterogeneous and diverse vegetation growth conditions.     

Remote sensing of sunlight-induced chlorophyll fluorescence and reflectance of Scots pine in the boreal forest during spring recovery

A measurement campaign to assess the feasibility of remote sensing of sunlight-induced chlorophyll fluorescence (ChlF) from a coniferous canopy was conducted in a boreal forest study site (Finland). A Passive Multi-wavelength Fluorescence Detector (PMFD) sensor, developed in the LURE laboratory, was used to obtain simultaneous measurements of ChlF in the oxygen absorption bands, at 687 and 760 nm, and a reflectance index, the PRI (Physiological Reflectance Index), for a month during spring recovery. When these data were compared with active fluorescence measurements performed on needles they revealed the same trend. During sunny days fluorescence and reflectance signals were found to be strongly influenced by shadows associated with the canopy structure. Moreover, chlorophyll fluorescence variations induced by rapid light changes (due to transient cloud shadows) were found to respond more quickly and with larger amplitude under summer conditions compared to those obtained under cold acclimation conditions. In addition, ChlF at 760 nm was observed to increase with the chlorophyll content. During this campaign, the CO₂ assimilation was measured at the forest canopy level and was found remarkably well correlated with the PRI index.