共查询到20条相似文献，搜索用时 31 毫秒
1.
Estimation of vertical distribution of chlorophyll concentration by bi-directional canopy reflectance spectra in winter wheat 总被引：1，自引：0，他引：1
Wenjiang Huang Zhijie Wang Linsheng Huang David W. Lamb Zhihong Ma Jincheng Zhang Jihua Wang Chunjiang Zhao 《Precision Agriculture》2011,12(2):165-178
An effective technique to measure foliage chlorophyll concentration (Chl) at a large scale and within a short time could be
a powerful tool to determine fertilization amount for crop management. The objective of this study was to investigate the
inversion of foliage Chl vertical-layer distribution by bi-directional reflectance difference function (BRDF) data, so as
to provide a theoretical basis for monitoring the growth and development of winter wheat and for providing guidance on the
application of fertilizer. Remote sensing could provide a powerful tool for large-area estimation of Chl. Because of the vertical
distribution of leaves in a wheat stem, Chl vertical distribution characteristics show an obvious decreasing trend from the
top of the canopy to the ground surface. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized
soil adjusted vegetation index (OSAVI) was called the canopy chlorophyll inversion index (CCII) in this study. The value of
CCII at nadir, ±20 and ±30°, at nadir, ±30 and ±40°, and at nadir, ±50 and ±60° view angles were selected and assembled as
bottom-layer Chl inversion index (BLCI), middle-layer Chl inversion index (MLCI), and upper-layer Chl inversion index (ULCI),
respectively, for the inversion of Chl at the vertical bottom layer, middle layer, and upper layer. The root mean squared
error (RMSE) between BLCI-, MLCI-, and ULCI-derived and laboratory-measured Chl were 0.7841, 0.9426, and 1.7398, respectively.
The vertical foliage Chl inversion could be used to monitor the crop growth status and to guide fertilizer and irrigation
management. The results suggested that vegetation indices derived from bi-directional reflectance spectra (e.g., BLCI, ULCI,
and MLCI) were satisfactory for inversion of the Chl vertical distribution. 相似文献
2.
基于新型植被指数的冬小麦LAI高光谱反演 总被引：8，自引：1，他引：7
【目的】本研究旨在分析冠层叶片水分含量对作物冠层光谱的影响,构建新型光谱指数来提高作物叶面积指数高光谱反演的精度。【方法】在冬小麦水肥交叉试验的支持下,分析不同筋性品种、施氮量、灌溉量处理下的冬小麦叶面积指数冠层光谱响应特征,并分析标准化差分红边指数(NDRE)、水分敏感指数(WI)与叶面积指数的相关性,据此构建一个新型的植被指数——红边抗水植被指数(red-edge resistance water vegetable index,RRWVI)。选取常用的植被指数作为参照,分析RRWVI对于冬小麦多个关键生育期叶面积指数的诊断能力,随机选取约2/3的实测样本建立基于各种植被指数的叶面积指数高光谱响应模型,未参与建模的样本用于评价模型精度。【结果】研究结果表明,随着生育期的推进,冬小麦的叶面积指数呈先增加后降低的变化趋势,不同的水肥处理对冬小麦叶面积指数具有较大影响。开花期之后冬小麦LAI显著下降,强筋小麦(藁优2018)在整个生育期叶面积指数均高于中筋小麦(济麦22);不同氮水平下冬小麦冠层光谱反射率在近红外波段(720—1 350 nm)随着施氮量的增加而增大,与氮肥梯度完全一致,其中2倍氮肥处理的近红外反射率达到最高;不同生育期下冬小麦冠层光谱反射率变化波形大体一致;各个关键生育期的NDRE和WI均存在较高的相关性,而NDRE与LAI的相关性明显优于WI,新构建的植被指数RRWVI与LAI的相关性均优于NDRE、WI;虽然8个常用的植被指数均与LAI存在显著相关,但RRWVI与LAI相关性达到最大,其拟合曲线的决定系数R2为0.86。【结论】通过分析各种指数所构建的冬小麦叶面积指数高光谱反演模型,新构建的RRWVI取得了比NDRE、NDVI等常用植被指数更为可靠的反演效果,说明本研究新构建的红边抗水植被指数可有效提高冬小麦叶面积指数的精度。 相似文献
3.
A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in
planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical
slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance
(i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included
those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance
index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position).
Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition
to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity
of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation
index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the
TCARI/OSAVI ratio for some soil conditions. 相似文献
4.
夏玉米光谱特征对其不同色素含量的响应差异 总被引：1，自引：0，他引：1
在不同施氮水平夏玉米的6个典型生育期,采用化学方法测定冠层叶绿素含量,利用叶绿素计测定的叶绿素读数以及光谱反射率,系统分析了单波段反射率、可见光和近红外波段组合而成的归一化植被指数(NDVI)、比值植被指数(RVI)等8种常见植被指数与相应时期2种方法测定的叶绿素含量的相关性。结果表明,随着施氮量的增加,叶绿素含量和冠层近红外波段反射率都随之增加;整个生育期中孕穗期在近红外区域反射率最高,与可见光波段反射率相差最大;6个生育期单波段510~1 100 nm反射率、NDVI、RVI等植被指数与叶绿素含量的2种测定结果显著相关或极显著相关,植被指数的表现较单波段更好,且从苗期到乳熟期,各波段反射率与叶绿素的相关性逐渐增强。整体来讲,可见光中560、660 nm和近红外760、810、590和1 300 nm组合的NDVI在各生育期与2个农学指标的相关性较好,选择NDVI(560,760)可以准确拟合夏玉米叶片叶绿素含量,其对化学方法测定的叶绿素含量拟合效果较佳。 相似文献
5.
有效的监测作物氮素营养水平及土壤供氮能力可以为合理施用氮肥提供重要依据。本文以2 年3 点不同氮素水平下不同小麦品种的田间试验数据为基础,运用植被指数和偏最小二乘回归法,比较和分析小麦冠层光谱与叶片氮含量及土壤氮含量的关系。结果表明：小麦冠层光谱与叶片氮含量的相关性分析在可见光波段存在显著负相关,在近红外波段呈显著正相关,而与土壤氮含量的相关性呈相反趋势。基于光谱参数ND705 和GNDVI所建叶片氮含量估算模型的决定系数分别达到0.827 和0.826。基于光谱参数VOG2 所建土壤氮含量估算模型的决定系数达到0.646;与植被指数所建模型相比,综合350~1350 nm光谱波段反射率分别与小麦叶片氮含量、土壤氮含量建立偏最小二乘回归模型的预测精
度均有所提高,决定系数分别达到0.842 和0.654。本研究结果可为小麦氮素营养及土壤供氮水平的诊断监测与合理施肥管理提供了理论依据和技术支持。 相似文献
6.
【目的】筛选相关性好的植被指数构建马铃薯叶片叶绿素a、叶绿素b估测模型,为科学、无损地进行马铃薯叶片叶绿素含量估算提供技术支撑。【方法】采用便携式高光谱地物波谱仪,获取不同施氮水平下不同生育时期的马铃薯植株叶片光谱反射率,提取植被指数,测定马铃薯叶片叶绿素a、叶绿素b含量,并研究叶绿素含量与植被指数的相关性。【结果】12个植被指数与叶绿素a、叶绿素b含量相关性较好,其中修正归一化差异指数(mND_(705))、修正简单比值指数(mSR_(705))、地面叶绿素指数(MTCI)、修改叶绿素吸收反射指数(MCARI)与叶绿素a、叶绿素b含量相关性最好。基于这4个植被指数建立的估测模型中,MTCI构建的乘幂模型估测叶绿素a含量的效果最佳,mND_(705)构建的指数模型估测叶绿素b含量的效果最佳。【结论】MTCI构建的乘幂模型能较为精确地估测叶绿素a含量,mND_(705)构建的指数模型能较为精确地估测叶绿素b含量;这2种模型可用于间接监测马铃薯植株的氮营养亏缺状态。 相似文献
7.
基于数码相机的玉米冠层SPAD遥感估算 总被引：1，自引：0，他引：1
【目的】叶绿素是植物光合作用中重要的色素。利用作物光谱信息对叶绿素含量进行反演,为作物的实时监测和生长状态诊断提供重要依据。【方法】以大田环境下不同氮肥水平(0,50%和100%)的开花期玉米为研究对象,利用轻小型无人机搭载数码相机,获取试验区RGB影像。使用土壤调整植被指数(soil adjusted vegetation index,SAVIgreen)对图像进行分割,基于分割前后的影像分别提取15种常见的可见光植被指数,综合分析指数与玉米冠层叶绿素相对含量SPAD值的相关关系。采用单变量回归模型、多元逐步回归模型和随机森林(random forest,RF)回归算法构建玉米SPAD值的遥感估算模型,通过模型精度评价指标决定系数(coefficient of determination,R2)、均方根误差(root mean square error,RMSE)、平均相对误差(mean relative error,MRE)和显著性检验水平(P0.01),确定最佳指标和最优模型。【结果】基于分割前后的数码影像提取的VIplot和VIplant植被指数与玉米冠层SPAD值之间具有显著的相关关系,其中VIplant中的红光标准化值(NRI)、归一化叶绿素比值植被指数(NPCI)、蓝红比值指数(BRRI)、差值植被指数(DVI)与SPAD值的相关性在0.77以上;以相关性高于0.77的VIplant指数NRI、NPCI、BRRI、DVI构建的线性、指数、对数、二次多项式、幂函数的单变量回归模型中,NRI指数构建的二次多项式模型效果最好,决定系数R2为0.7976,RMSE为4.31,MRE为5.91%。在VIplant指数NRI、NPCI、BRRI、DVI参与建立的多变量SPAD反演模型中,使用随机森林方法的模型精度最高,决定系数R2为0.8682,RMSE为3.92,MRE为4.98%,而多元逐步回归模型的精度高于任意单变量回归模型,决定系数R2为0.819,RMSE为4,MRE为5.67%;对数码影像结合各模型制作的SPAD分布图进行精度分析,使用随机森林回归模型对SPAD的估测值与实测值最为接近,具有最佳的预测效果,R2为0.8247,RMSE为4.3,MRE为5.36%,可以作为玉米冠层叶绿素信息监测的主要方法。【结论】本研究证明将数码相机影像提取的可见光植被指数应用于玉米叶绿素相对含量的估测是可行的,这也为无人机遥感系统在农业方面的应用增添了新的手段和经验。 相似文献
8.
《Computers and Electronics in Agriculture》2008,60(1-2):56-65
Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha−1 for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard. 相似文献
9.
HUANG Wen-jiang WANG Ji-hua LIU Liang-yun WANG Zhi-jie TAN Chang-wei SONG Xiao-yu WANG Jing-di 《中国农业科学(英文版)》2004,3(11)
Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of'nitrogen and irrigation treatments.Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, etc. Secondly,total nitrogen content around winter wheat anthesis stage was proved to be significantly correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established to forecast grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum. 相似文献
10.
Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice andWheat 
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下载免费PDF全文 WANG Wei YAO Xia TIAN Yong-chao LIU Xiao-jun NI Jun CAO Wei-xing ZHU Yan 《农业科学学报》2012,12(12):2001-2012
Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat. 相似文献
11.
对水稻微分光谱和植被指数的探讨 总被引：3，自引：0，他引：3
通过不同供氮水平的田间试验，分析了微分光谱对消除水稻冠层光谱的背景影响和植被指数在农学参数测定中的作用。结果表明：由微分光谱所得的红边位置、红边斜率与盖度、叶面积指数及供氮水平之间有一定的相关性；水稻多光谱植被指数RVI、NDVI与叶面积指数LAI及其地上部生物量之间有极显著相关性；高光谱植被指数及其变量与植被盖度、供氮水平之间存在相关性。这些表明，用微分光谱技术与植被指数方法监测水稻的田间供氮水平和长势似乎是可行的。 相似文献
12.
基于植被指数的马尾松叶绿素含量估算模型 总被引：5，自引：0，他引：5
利用高光谱技术,探索马尾松反射光谱组成的植被指数与其叶绿素含量之间的关系。采用美国ASD公司生产的手持式野外光谱辐射仪测量马尾松冠层光谱,对观测叶片进行同步叶绿素含量测定,并利用统计学分析方法,分析马尾松冠层光谱组成的植被指数与叶绿素含量之间的相关关系,并建立相应的估算模型。结果表明:叶绿素含量与植被指数进行相关性分析,相关性最好的为TCARI;通过建立TCARI与叶绿素含量之间的估算模型并检验其精度,得出了叶绿素含量估算的高光谱模型:y=exp(0.686+(-2.765)×x)。说明利用高光谱数据可以估测马尾松的叶绿素含量。 相似文献
13.
14.
以陕西省扶风县马席村、巨良农场和杨凌区揉谷乡种植的大田玉米为试验材料,分别测定玉米抽雄期、灌浆期和乳熟期的冠层光谱反射率和叶片叶绿素含量,分析冠层各光谱植被指数与叶片叶绿素含量之间的相关关系,建立玉米叶绿素含量估测模型。结果表明,以单变量光谱植被指数估算叶绿素含量,抽雄期的最佳模型由修正叶绿素吸收反射率指数(Modified chlorophyll absorption reflectivity index,MCARI)建立,灌浆期最佳模型由垂直植被指数(Perpendicular vegetation index,PVI)建立,乳熟期最佳模型由植被衰老反射率指数(Plant senescence reflectance index,PSRI)建立。随着玉米生长期的推进,叶片衰老,用PSRI所建立的模型来监测玉米叶绿素含量的效果较好,可为高光谱遥感在玉米长势监测提供理论依据和技术支持。 相似文献
15.
An assessment of the sensitivity at the canopy scale to leaf chlorophyll concentration of the broad-band chlorophyll vegetation
index (CVI) is carried out for a wide range of soils and crops conditions and for different sun zenith angles by the analysis
of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT + SAILH leaf and canopy reflectance
model. An optimized version (OCVI) of the CVI is proposed. A single correction factor is incorporated in the OCVI algorithm
to take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun
zenith angle. An estimate of the value of the correction factor and of the minimum leaf area index (LAI) value of applicability
are given for each considered condition. The results of the analysis of the synthetic dataset indicated that the broad-band
CVI index could be used as a leaf chlorophyll estimator for planophile crops in most soil conditions. Results indicated as
well that, in principle, a single correction factor incorporated in the OCVI could take into account the different spectral
behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle. 相似文献
16.
A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery 下载免费PDF全文
下载免费PDF全文 《农业科学学报》2023,22(7):2248-2270
The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field. 相似文献
17.
An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat 下载免费PDF全文
下载免费PDF全文 ZHAO Yu WANG Jian-wen CHEN Li-ping FU Yuan-yuan ZHU Hong-chun FENG Hai-kuan XU Xin-gang LI Zhen-hai 《农业科学学报》2021,20(9):2535-2551
The nitrogen nutrition index(NNI) is a reliable indicator for diagnosing crop nitrogen(N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1)) of the vegetation index and traditional indirect NNI inversion method(NNI_(T2)) by inverting intermediate variables including the aboveground dry biomass(AGB) and plant N concentration(PNC), this study proposed a new NNI remote sensing index(NNI_(RS)). A remote-sensing-based critical N dilution curve(Nc_(_RS)) was set up directly from two vegetation indices and then used to calculate NNI_(RS). Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1), 2013–2014(Exp. 2), 2014–2015(Exp. 3), 2015–2016(Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models(NNI_(T1), NNI_(T2) and NNI_(RS)). The results showed that:(1) the NNI_(RS) models were represented by the standardized leaf area index determining index(sLAIDI) and the red-edge chlorophyll index(CI_(red edge)) in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b), where "a" equals 2.06, 2.10, 2.08 and 2.02 and "b" equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively;(2) the NNI_(RS) models achieved better performance than the other two NNI revised methods, and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14, respectively;(3) when the remaining data were used for verification, the NNI_(RS) models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNI_(RS) method is promising for the remote assessment of crop N status. 相似文献
18.
Dong Rui Miao Yuxin Wang Xinbing Yuan Fei Kusnierek Krzysztof 《Precision Agriculture》2022,23(3):939-960
Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.
相似文献19.
Tools to quantify the nitrogen (N) status of a rice canopy during inter-nodal elongation (IE) would be valuable for mid-season
N management because N accounts for the largest input cost. The objective of this paper was to study canopy reflectance as
a potential tool for assessing the mid-season status of N in a rice crop. Three field plot experiments were conducted in 2002
and 2003 on cultivars Wells and Cocodrie to study the canopy reflectance response of rice to plant N accumulation (PNA) during
IE and to identify the wavelengths and vegetation indices that are good indicators of PNA. Each experiment included six pre-flood
N treatments of 0, 33.6, 67.2, 100.8, 133.4 and 168 kg N ha−1. Rice canopy reflectance, biomass, tissue N concentration and PNA were measured weekly during IE. The wavelengths most strongly
correlated to PNA at the beginning of IE were 937 and 718 nm. Several vegetation indices were examined to determine which
were strongly correlated (>0.7) with PNA at the beginning of IE. Multiple linear regression models of PNA on selected vegetation
indices explained 53–85% of the variation in PNA during the first week of IE. This study identifies the best combinations
of vegetation indices for estimating PNA in rice. 相似文献
20.
【目的】去除无人机多光谱遥感影像中的阴影,以提高苹果树冠层氮素含量反演模型精度。【方法】以山东省栖霞市苹果园为试验区,利用2019年6月采集的无人机多光谱影像,分别基于归一化阴影指数（normalized shaded vegetation index,NSVI）和归一化冠层阴影指数（normalized difference canopy shadow index,NDCSI）去除果树冠层多光谱影像中的阴影,提取非阴影区域果树冠层光谱信息;通过相关性分析方法,将基于原始光谱影像和基于NSVI、NDCSI去除阴影后提取的光谱数据与实测叶片氮素含量进行相关性分析,分别筛选氮素含量的敏感波段并构建光谱参量;采用偏最小二乘（partial least square,PLS）及支持向量机（support vector machine,SVM）方法构建果树冠层氮素含量反演模型并进行精度检验。【结果】绿光波段和红光波段为果树冠层氮素含量反演的敏感波段;阴影削弱了果树冠层的光谱信息,去除阴影前后,冠层多光谱各波段光谱差异较大,在红边波段及近红外波段尤为明显;基于2个阴影指数去除阴影后构建的氮素反演模型精度均有提升,最优模型为基于NDCSI去除阴影后构建的支持向量机氮素含量反演模型,该模型建模集R2和RPD分别为0.774、1.828;验证集R2和RPD分别为0.723、1.819。【结论】基于NDCSI可有效去除无人机多光谱果树冠层影像中的阴影,提高氮素含量反演精度,为果园氮素精准管理提供了有效参考。 相似文献