Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data

Leaf chlorophyll content(LCC)is an important physiological indicator of the actual health status of individual plants.An accurate estimation of LCC can therefore provide valuable information for precision field management.Red-edge information from hyperspectral data has been widely used to estimate crop LCC.However,after the advent of red-edge bands in satellite imagery,no systematic evaluation of the performance of satellite data has been conducted.Toward this end,we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites(RapidEye,Sentinel-2 and EnMAP)and their new red-edge bands by using partial least squares regression(PLSR)and a vegetation-indexbased approach.These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution.The results showed:1)This study confirmed that RapidEye,Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval.For the PLSR approach,Sentinel-2 data provided more accurate estimates of LCC(R2=0.755,0.844,0.805 for 2002,2010,and 2002+2010)than do RapidEye data(R2=0.689,0.710,0.707 for 2002,2010,and 2002+2010)and EnMAP data(R2=0.735,0.867,0.771 for 2002,2010,and 2002+2010).For index-based approaches,the MERIS terrestrial chlorophyll index,which is a vegetation index with two red-edge bands,was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data(R2≥0.628),and the indices(NDRE1,SRRE1 and CIRE1)with a single red-edge band were the most sensitive and robust indices for the RapidEye data(R2≥0.420);2)According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval,the short-wavelength red-edge bands(from 699 to 734 nm)provided more accurate predictions when using the PLSR approach,whereas the long-wavelength red-edge bands(740 to 783 nm)gave more accurate predictions when using the vegetation indice(VI)approach.In addition,the prediction accuracy of RapidEye,Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution;VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands,but for normalized difference vegetation index(NDVI)-,simple ratio(SR)-and chlorophyll index(CI)-like index,two red-edge bands index didn’t significantly improve the predictive accuracy of LCC than those indices with a single red-edge band.Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC,the level of this improvement remains insufficient because of higher spectral resolution,which results in a worse signal-to-noise ratio.The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.

Effect of Rhizobium Inoculation and Supplementation with Phosphorus and Potassium on Growth and Total Leaf Chlorophyll (Chl) Content of Bush Bean <i>Phaseolus vulgaris</i>, L.

The study was conducted to investigate the effect of Rhizobium inoculation and supplementation of phosphorus and potassium on growth and total leaf chlorophyll content to the three released bush bean varieties in northern Tanzania. To achieve this aim, the glasshouse experiment was conducted at Nelson Mandela African Institution of Science and Technology while field experiment were carried out at Tanzania Coffee Research Institute, in Kilimanjaro, Tanzania between April-July 2014. The experiment was laid out in factorial arrangement. Factor I comprised of three bush bean varieties. Factor II involved two inoculation treatments viz 1) inoculation with Rhizobium spp. and 2) without inoculation. Factor III included four fertilizer levels (0 Kg·ha-1 20 Kg K ha-1, 30 Kg P ha-1 and 20 kg·K + 30 Kg P ha-1). Both screen house and field experiments were replicated four times. Plant growth parameters (plant height (cm), number leaves per plant, stem girth (mm)) were measured at 2, 4 and 6 weaks after planting (WAP). The chlorophyll was extracted by using Dimethylsulphoxide (DMSO) and absorbance was determined at 645 and 663nm using UV/Visible spectrophotometer. Results showed that Rhizobium application significantly improved the number of leaves per plant, plant height, pant girth and total leaf chlorophyll content. Furthermore, compared with the zero treatment control, potassium fertilization significantly increased the number of leaves per plant, plant height, pant girth and total leaf chlorophyll content of the three varieties. In general, these parameters were significantly increased with phosphorus supplied at 30 kg/ha. The combination of these supplies at different levels resulted in significant interactions in some parameters and thus indicating need for these inputs in the study area.

Leaf Gas Exchange,Chlorophyll Fluorescence,and Fruit Yield in Hot Pepper (Capsicum anmuum L.) Grown Under Different Shade and Soil Moisture During the Fruit Growth Stage

Relative leaf chlorophyll （Chl.） content, leaf gas exchange, Chl. fluorescence, plant biological biomass, and fruit yield were evaluated in growing hot pepper （Capsicum annuum L.） during the fruit-growing stages in hot summer under three shade levels （un-shade, 30% shade, and 70% shade） and four soil water contents （SWC） of 40-55%, 55-70%, 70-85%, and 85- 100% of field moisture capacity （FMC）. Hot pepper crops were more affected by light irradiance than by soil moisture and by their interaction during the whole observed periods. Hot pepper attained greatest relative leaf Chl. content （expressed as SPAD value） and photosynthetic activity when cultivated with 30% shade, resulting in the highest plant biological biomass and fruit yield. Although 70% shade improved leaf photosynthetic efficiency （expressed as Fv/Fm or Fv＇/Fm＇）, crops obtained the lowest photosynthetic rate, photochemical quenching coefficient （qP）, and non-photochemical quenching coefficient （NPQ）. This showed that light irradiance was insufficiency in S70% （70% shade） treatment. The leaf net photosynthetic rates （PN）, Fc/Fm, and fruit yield increased gradually as SWC levels increased from 40-55% to 70- 85% FMC, but decreased as SWC was higher than 70-85% FMC. The water consumption increased progressively with SWC levels, but water-use efficiency （WUE） was the highest when soil moisture was 55-70% FMC. Interaction of shade and soil moisture had significant effects on PN and FJFm, but not on other parameters. Under drought stress （40-55% and 55-70% FMC）, 30% shade could relieve the droughty damage of crops and improve photosynthetic capacity and WUE, but 70% shade could not, oppositely, aggravate the damage. The positive correlation （r2 =0.72） between leaf PN and fruit yield was existent. This indicated that improvement of leaf photosynthesis would increase potentially marketable yield in hot pepper crops during the full fruit-growing stages. For agricultural purposes, approximately S30% （30% shade） with 70- 85% FMC is suggested to cultivate hot pepper during the fruit growth stage in hot summer months.

Chlorophyll Fluorescence, Photosynthetic and Morphological Characteristics of Waterlogged Sesame Seedlings

Two sesame accessions, ZZM2541 and Ezhi-2, with different tolerance to waterlogging were selected, and the seedlings at the 4-true leaf stage were treated by waterlogging for 48 h. Growth parameters were evaluated 1 d before the beginning of waterlogging and on the 3^(rd)day after the removal of waterlogging(DARW). Morphological characteristics of root and chlorophyll fuorescence were measured on the 3^(rd)DARW and leaf gas exchange was measured on the zero, 3^(rd)and 15^(th) DARW. Results showed that sesame accessions ZZM2541 and Ezhi-2 responded to waterlogging in considerably different performance. The stress induced leaf chlorosis and abscission and slowed growth of plant height in both accessions, but symptom occurred seriously in the susceptive Ezhi-2. In the more tolerant ZZM2541, plentiful of adventitious roots formed above the level.All of the average values of maximum fuorescence yield(F_m), quantum efficiency of open PSII centres(F_v/F_m), initial fluorescence(F_v/F_0) and chlorophyll content(Chl) decreased at the 4-true leaf stage in both accessions after suffering to the stress.The decreases of F_m, F_v/F_0 and Chl were more pronounced in Ezhi-2 than in ZZM2541. Less reductions of mean photosynthetic rate(Pn), transpiration rate(Tr), and stomatal conductance(gs) were observed in the leaves of waterlogged ZZM2541 than in waterlogged Ezhi-2(compared to controls), and the leaves of Ezhi-2 showed a higher water use efficiency(WUE) after the removal of waterlogging. Based on the results, it was concluded that the tolerance to waterlogging of ZZM2541 appears to depend on a combination of photosynthetic characteristics responses and morphological adaption.

不同频率氮添加对内蒙古典型草原植物叶绿素的影响

大气氮沉降会影响植物功能性状的变异和进化,进而作用于植物个体和生态系统功能。研究草地生态系统植物功能性状在不同氮添加模式下的响应差异,对更准确地评估植物对环境变化的适应性至关重要。基于内蒙古草原野外长期氮沉降模拟实验平台,研究氮添加频率对优势物种羊草和冰草叶绿素含量的影响,结果表明每年一次氮添加使羊草叶绿素含量增加最多(15.21%),而每月一次氮添加对冰草叶绿素含量影响最大(增加了14.74%)。氮添加尤其是每年一次氮添加显著增加了土壤铵态氮、硝态氮和无机氮含量,并使土壤pH显著降低。这些结果表明:羊草叶绿素含量对低频率氮添加响应更明显,而高频率氮添加对冰草叶绿素含量的影响更显著,这两类物种间养分吸收策略存在明显差异。启示低频率氮添加可能高估了氮沉降对羊草叶绿素含量的影响,而低估了对冰草叶绿素含量的影响,这对准确预测植物叶片功能性状对大气氮沉降的变异具有重要意义,并将有助于应用到植物功能性状预测生态系统功能和过程响应未来全球变化的模型中。

基于超分辨率重建与机器学习的油菜苗情监测方法

为优化养分管理和确保植株正常生长,以无人机遥感技术高效且非破坏采集田间作物苗情信息,监测油菜苗期的叶面积指数(LAI)与叶绿素相对含量(SPAD)。针对无人机因飞行高度与图像分辨率相互制约,监测效率与监测精度难以兼顾的问题,采用超分辨率重建方法,融合较低飞行高度拍摄高分辨率影像,重建较高飞行高度拍摄影像,建模完成后可通过拍摄飞行影像监测LAI和SPAD。试验设置3个氮肥梯度、3个播期、3个种植密度处理,在苗期利用无人机分别采集20m及40m 2个飞行高度的油菜苗影像,采用SRRestnet方法,对40m影像进行超分辨率重建。基于20m、40m及40m重建影像中提取的3种特征组合,利用偏最小二乘回归(PLSR)、随机森林(RF)、支持向量回归(SVR)3种机器学习方法对LAI和SPAD进行监测。结果表明,超分辨率重建后的图像在表型苗情监测中表现出良好效果,PLSR监测LAI、RF监测SPAD的监测精度最高,且40m重建图像的作业效率相比于20m图像提高48.6%。

叶片多理化参数的高光谱遥感与深度学习估算

准确的植物叶片理化参数对于监测植物生长状况至关重要。随着深度学习技术的迅速应用,结合深度学习和高光谱遥感技术的植物叶片理化参数分析应用潜力巨大;然而,现阶段结合深度学习和高光谱遥感技术在植物多叶片理化参数的联合估算研究尚少。该研究旨在挖掘结合高光谱遥感技术和深度学习技术开展高精度的多植被叶片理化参数(叶绿素含量、类胡萝卜素含量、水分含量、蛋白质含量和碳基成分含量)联合估算的潜力。首先,通过利用新型PROSPECT-PRO辐射传输模型模拟分析,确定了多个植被叶片理化参数的敏感光谱区域,并设计了LeafTraitNet模型;然后,基于Lopex93数据开展LeafTr aitNet模型训练和验证,取得了高精度的叶片参数估算结果。得到以下结论:(1)基于PROSPECT-PRO辐射传输模型辅助实测开展植被光谱特征选择十分必要;叶绿素(约434和约676 nm)和类胡萝卜素(约445 nm)两类色素的光谱吸收峰均主要位于可见光区域;然而,其与叶片光谱的相关系数绝对值最大的点却不是各自的吸收峰位置,这可能是因为叶绿素和类胡萝卜素对光谱吸收的相互影响。(2)水分的吸收峰主要位于950~2500 nm范围内,这与叶片蛋白质和碳基成分的吸收区域重叠,因此削弱了后者的高光谱遥感估算精度。基于PROSPECT-PRO辐射传输模型和Lopex93数据集的叶片参数相关性分析结果表明,叶片水分含量与950~2500 nm范围内叶片光谱反射率相关系数绝对值接近1,而叶片蛋白质和碳基成分含量与950~2500 nm范围内光谱反射率相关系数较低。(3)三种传统统计回归方法和LeafTraitNet模型的叶片理化参数估算精度可以基于其估算总nRMSE而排序为:Le afTraitNet(总nRMSE=0.84)<RF(总nRMSE=1.59)<MLP(总nRMSE=1.73)<MLR(总nRMS E=1.74)。研究显示基于深度学习的LeafTraitNet模型有潜力提供远高于传统统计回归模型的植物叶片理化参数估算结果,但其在冠层尺度的研究仍需要更多的实验来进一步验证。

Effects of Shading on Photosynthetic Properties of Canopy and Single Leaf of Winter Wheat

[Objective] The aim was to study on the effects of long-term low radiation on canopy apparent photosynthetic rate （CAP） and photosynthetic properties of top three leaves of winter wheat, especially the flag leaf. [Method] Two winter wheat （Triticum aestivum L） cultivars, Yangmai 158 and Yangmai 11, which differed in low radiation resistance, were used to measure the CAP, photosynthetic rate （Pn） of the top three leaves, chlorophyll content of flag leaf and grain yield. In the test, three treatments were designed from jointing to maturity, as follows： control without shad- ing （So）, S1 and S2 treatments with 22% and 33% sunshine shaded. [Result] CAP of Yangmai 158 and Yangmai 11 declined with shading. Under low radiation, Pn of flag leaf declined significantly, while no obvious effects were found on Pn of the 2nd leaf, and Pn of the 3rn leaf increased significantly, which partially compensated the decrease of Pn of the flag leaf. In addition, the compensation effect differed in shading and cultivars： compensation effect in S1 group was higher than that of S2 and effect of Yangmai 158 was higher than that of Yangmai 11. During the period of high Pn. shading decreased the content of total chlorophyll, chlorophyll a, b, and a/b. [Conclusion] Under low radiation, the decrease of chlorophyll a and a/b led to substantial declining of Pn. and CAP declined accordingly, finally resulting in drop of grain yield. The research provides important theoretical basis for wheat production in the middle and lower reaches of Yangtze River.

4种忍冬属树种物候与叶生长研究

以长白忍冬Lonicera ruprechtiana、蓝叶忍冬L.korolkowii、早花忍冬L.praeflorens和金银忍冬L.maackii为研究对象,采用树木物候观测法对4种忍冬属树种的物候期进行观测记录,同时对其叶片的长度、宽度、叶面积和叶生物量进行了连续4个月的测量。结果显示:(1)4种忍冬属树种的叶期为长白忍冬(169d)>蓝叶忍冬(165d)>金银忍冬(161 d)>早花忍冬(159 d);花期为早花忍冬(18 d)>蓝叶忍冬(14 d)>金银忍冬(13 d)>长白忍冬(9 d);果实宿存为金银忍冬(198 d)>长白忍冬(50 d)>蓝叶忍冬(39 d)>早花忍冬(9 d)。结果表明:长白忍冬叶期最长,早花忍冬花期最长,金银忍冬果实宿存期最长。(2)叶片长度、宽度和叶面积变化趋势较一致。长白忍冬的叶片长度、宽度和叶面积在6月上旬达到最大,蓝叶忍冬和早花忍冬在7月上旬达到最大,金银忍冬在7月下旬达到最大。(3)4种忍冬属树种比叶面积从大到小为金银忍冬(197.88 cm2·g-1)>蓝叶忍冬(173.54 cm2·g-1)>早花忍冬(164.21 cm2·g-1)>长白忍冬(163.88 cm2·g-1),表明长白忍冬比其它3种忍冬属树种更能适应资源贫瘠和干旱的生境。在园林绿化中应充分考虑4种忍冬属树种的物候特征和生态学特性进行合理优化配置,以达到较高的观赏和应用价值。

甘蓝型油菜叶片叶绿素含量测定方法的优化及不同时期叶绿素含量比较

为准确、高效地获取批量材料叶片叶绿素含量,以春性甘蓝型油菜为材料,设置不同测定方法(研磨法和浸提法),探究其对叶片叶绿素含量测定的影响。结果表明:研磨法过程繁琐,容易造成误差,不适宜大量样本的叶绿素含量测定;而浸提法操作相对简便,丙酮无水乙醇(1︰1)浸提液在浸提17 h时提取效果较好。同时,比较苗期和花期叶片叶绿素含量发现,相同材料花期叶片叶绿素含量显著高于苗期。本研究优化了甘蓝型油菜叶片叶绿素的提取方法,为后续研究提供了技术支持。

A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

蒙古黄芪叶片不同表型叶形状差异比较分析

蒙古黄芪是具有多种研究价值的豆科多年生草本植物,为探究蒙古黄芪不同叶色叶型各项指标差异,选取6种田间常见叶片颜色及叶片形状,对其叶色、复叶、小叶、叶绿素等形态指标进行分析。结果表明,叶片颜色与叶绿素含量存在显著正相关,与小叶数呈显著负相关,叶色深绿色叶绿素总含量最高,为4.07 mg/g,小叶数最少,为34片。叶片表型与小叶数量、叶面积、叶绿素含量紧密相关。为进一步研究叶片形态和蒙古黄芪药材产量质量的关系、改善和提高蒙古黄芪药材产量品质、挖掘蒙古黄芪植株饲用价值和优良种质的培育提供理论依据。

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.

二向反射和方向半球反射光谱差异及其对小麦叶片叶绿素含量反演的影响

二向反射率因子(bidirectional reflectance factor,BRF)和方向半球反射率因子(directional-hemispherical reflectance factor,DHRF)是反射光谱的两种形式,但在生化参数监测过程中,多数研究忽略了BRF和DHRF光谱的差异及其对叶片叶绿素含量(leaf chlorophyll content,LCC)反演的影响。本研究以不同品种、密度及氮素处理的小麦田间小区试验为基础,在叶片尺度获取了BRF和DHRF光谱,并计算相应的植被指数和小波系数,建立了基于植被指数和小波系数的LCC监测模型,定量分析BRF和DHRF光谱、植被指数和小波系数的差异及其对LCC反演的影响。结果表明,1)BRF和DHRF随LCC变化趋势一致,但两种光谱存在显著差异,且BRF光谱值高于DHRF光谱值;2)在一定程度上,应用植被指数和小波系数均可消除BRF和DHRF光谱差异的影响,其中归一化红边植被指数(normalized differential red edge vegetation index,NDRE)和红边叶绿素指数(red edge chlorophyll index,C_(Ired-edge))可以消除BRF和DHRF光谱差异的影响(R^(2)=0.930),但小波系数的性能要优于植被指数(R^(2)=0.995);3)基于DHRF光谱的植被指数和小波系数对LCC的估测能力优于BRF光谱,所有植被指数中NDRE反演效果最好(DHRF:R^(2)=0.957;BRF:R^(2)=0.938;All:R^(2)=0.892),第4尺度765 nm处的小波系数WF(4,675)反演LCC的效果优于NDRE(DHRF:R^(2)=0.985;BRF:R^(2)=0.971;All:R^(2)=0.973),且WF(4,675)消除BRF和DHRF光谱差异对LCC反演的影响能力强于NDRE(WF(4,675):R^(2)=0.973;NDRE:R^(2)=0.892)。综上所述,BRF和DHRF光谱存在差异,且这种差异不能直接忽略。研究明确了BRF和DHRF光谱的差异,为构建基于BRF和DHRF光谱的统一模型及提升冠层尺度LCC精确反演提供理论基础。

3个紫薇品种形态和生理特性对干旱胁迫的响应

【目的】探明干旱胁迫对紫薇品种形态特征和生理特性的影响,为鉴选耐旱紫薇品种提供参考依据。【方法】以睡美人、莎拉的最爱、纳齐兹3个紫薇典型品种为材料,采用持续干旱和梯度干旱(盆苗浇水量100%;浇水量75%,轻度干旱;浇水量50%,中度干旱;浇水量25%,重度干旱;浇水量0%,极度干旱)胁迫法进行处理,通过测定植株形态、盆苗日失水量、叶片含水量、复水成活率和叶绿素荧光等指标变化,对3个紫薇品种的耐旱性开展综合分析。【结果】持续干旱试验中,紫薇植株形态评分依次为睡美人>莎拉的最爱>纳齐兹;平均盆苗日失水量/水盘日蒸发量比值依次为睡美人<莎拉的最爱<纳齐兹;睡美人、莎拉的最爱复水成活率均为80%,纳齐兹为60%。梯度干旱试验中,紫薇品种植株形态得分、叶片含水量、最大荧光(F_(m))、最大光化学效率(F_(v)/F_(m))均随干旱程度的加深而降低,初始荧光(F_(0))则随之增大,各指标变化幅度3个紫薇品种间表现为睡美人<莎拉的最爱<纳齐兹;睡美人盆苗浇水量0%处理、莎拉的最爱盆苗浇水量0%和25%处理、纳齐兹盆苗浇水量25%和50%处理下快速叶绿素荧光诱导曲线(OJIP曲线)在300μs出现显著峰值(K-band),说明干旱造成PSII供体侧的放氧复合体(OEC)受损。使用隶属函数法对3个紫薇品种的复水成活率、盆苗日失水量/水盘日蒸发量、植株形态、叶片含水量、PSⅡ初始荧光、PSⅡ最大荧光、PSⅡ最大光化学效率7个测量指标进行综合评价,分值依次为睡美人(0.934)>莎拉的最爱(0.366)>纳齐兹(0.116)。【结论】3个紫薇品种的耐旱能力依次为睡美人>莎拉的最爱>纳齐兹。睡美人、莎拉的最爱适合在节水园林中应用。

‘清香’核桃叶片叶绿素含量高光谱估测模型研究

为实现快速、无损、精确地估测核桃叶片叶绿素含量,以山西威特核桃示范园的‘清香’核桃为试验对象,基于田间人工定量施肥试验,利用皮尔逊(Pearson)相关分析法筛选与果实不同生育期叶片绿色度(SPAD值)呈极显著相关的光谱特征参量,并以筛选出的光谱特征参量为自变量,采用回归分析构建‘清香’核桃果实不同生育期叶片SPAD值的估测模型。结果表明,核桃果实不同生育期叶片原始光谱曲线的总体变化趋势基本一致;不同生育期叶片SPAD值与原始光谱和一阶微分光谱的相关性差异显著,其中果实速生期叶片SPAD值与原始光谱的所有波段呈负相关关系,在波段705 nm处相关系数高达0.4565;红边蓝边面积的归一化值[(S_(Dr)-S_(Db))/(S_(Dr)+S_(Db))]可较好地估测速生期和脂化期叶片的SPAD值,在果实速生期(S_(Dr)-S_(Db))/(S_(Dr)+S_(Db))与叶片SPAD值的关系以指数模型为最佳,而在果实脂化期对叶片SPAD值估测以二次多项式模型最优,2个时期检验模型的均方根误差分别为0.4768、0.5323,相对误差分别为0.8862和0.7766,均较小;MERIS陆地叶绿素指数(M_(TCI))可较好地估测果实硬核期和成熟期叶片的SPAD值,其中果实硬核期以线性模型的检验精度更高,均方根误差和相对误差分别为0.5040和0.8442,而果实成熟期以M_(TCI)的二次多项式模型估测叶片SPAD值最优,检验模型决定系数(R^(2))为0.8211,均方根误差和相对误差分别为0.9576、1.4819,说明以上模型能较好地估测核桃叶片叶绿素含量,且不同生育期的适用模型有所不同。

香花油茶不同无性系叶表型性状及叶绿素含量的差异

【目的】为香花油茶良种选育和生态经济型品种筛选提供参考。【方法】以生长良好的30个香花油茶无性系叶为材料,使用叶面积仪和Dualex植物多酚-叶绿素仪对叶表型性状和叶绿素含量进行测定。【结果】叶水分含量特征:叶含水量为52.07%~62.44%,含水量高于55%的无性系占比70%以上。叶表型性状:15号无性系的叶长最大,29号无性系的叶宽、叶面积、叶周长、形状系数均最小;各无性系叶表型性状的变异系数为9.18%~23.07%,叶周长的变异系数最小(为9.18%),叶面积的变异系数最大(为23.07%);叶宽、叶面积与其他叶表型性状间均存在极显著相关性;在主成分分析中,前2个主成分的累计贡献率达到92.708%,说明这2个主成分概括了6个性状的主要信息。其中,叶面积的特征向量值最大,其次是叶宽,叶面积和叶宽是区分香花油茶无性系最主要的叶性状。经聚类分析,香花油茶各无性系可被划分为3个类群。叶绿素含量:香花油茶不同无性系间叶绿素含量存在变异,变异系数为8.48%;25号无性系叶基、叶中的叶绿素含量与其他无性系有显著差异,叶尖叶绿素含量除与18号无性系无显著差异外,与其他无性系均有显著差异。【结论】香花油茶叶表型性状的遗传差异较大;叶中的叶绿素含量与叶总体的叶绿素含量的相关性最高,即叶中部可作为测定叶片叶绿素含量的最佳部位。

基于叶片反射光谱和叶绿素荧光估测水稻叶片含水量

水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗灌浆期)动态监测顶1叶(L_(1))、顶2叶(L_(2))和顶3叶(L_(3))的光谱数据和叶绿素荧光参数,通过全光谱波段筛选出水分敏感波段,建立新型植被指数,结合叶绿素荧光参数,以期建立基于叶位组合的水稻冠层LWC精准监测模型。结果表明:水稻叶片水分敏感波段在近红外波段(1000~1400 nm),所构建新型植被指数NDSII_((1114,1387))较传统植被指数能更好地监测LWC;通过筛选与LWC有高相关性的荧光参数,基于实际光量子产量Y(Ⅱ)和植被指数NDSII_((1114,1387))的耦合监测模型较单一植被指数NDSII_((1114,1387))模型精度提高71.807%~83.976%。与单叶相比,L_(2)和L_(3)叶位组合的Y(Ⅱ)和植被指数NDSII_((1114,1387))耦合模型对水稻冠层LWC监测精度相较L_(2)、L_(3)分别显著(P<0.05)提高11.641%和23.029%。由此表明,基于叶位组合的叶片反射光谱与叶绿素荧光耦合可有效监测水稻冠层LWC,为光学仪器监测水稻LWC提供理论基础,并对未来利用反射光谱与荧光参数进行作物光合作用研究提供理论支持。