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。     

高光谱与叶绿素计快速测定大麦氮素营养状况研究

为了寻找一种简便、快速、非接触性的方法测定作物氮素营养状况,通过田间试验,利用光谱仪和叶绿素计测量了不同氮素水平及不同时期大麦（秀麦3号、浙农大3号）的冠层光谱及叶片SPAD值,在此基础上分析了大麦冠层光谱特性及一阶导数光谱、红边、叶片SPAD值与氮素水平之间的相关性。结果表明,大麦冠层光谱及其一阶导数光谱和红边与氮素水平存在显著相关,不同供氮水平下叶片的SPAD值具有显著性差异,叶片SPAD值与氮素水平之间的相关系数在孕穗期均达到0.75以上,相关达0.01显著水平,从而说明可以通过光谱来测定大麦的氮素水平。     

Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.     

基于无人机多光谱遥感的冬小麦冠层叶绿素含量估测研究

为探讨利用无人机多光谱影像监测冬小麦叶绿素含量的可行性,基于北京市大兴区中国水科院试验基地的2019年冬小麦无人机多光谱影像和田间实测冠层叶绿素含量数据,选取16种光谱植被指数,确定对冬小麦冠层叶绿素含量显著相关的植被指数,采用一元二次线性回归和逐步回归分析方法建立各生育时期及全生育期的SPAD值估测模型,通过精度检验确定对冬小麦冠层叶绿素含量监测的最优模型。结果表明,两种分析方法中逐步回归建模效果最佳。拔节期选取4个植被指数(MSR、CARI、NGBDI、TVI)建模效果最好,模型率定的决定系数(r~2)为0.73,模型验证的r~2、相对误差(RE)和均方根误差(RMSE)分别为0.63、2.83%、1.68;抽穗期选取3个植被指数(GNDVI、GOSAVI、CARI)建模效果最好,模型率定的r~2为0.81,模型验证的r~2、RE、RMSE分别为0.63、2.83%、1.68;灌浆期选取2个植被指数(MSR、NGBDI)建模效果最好,模型率定的r~2为0.67,模型验证的r~2、RE、RMSE分别为0.65、2.83%、1.88。因此,无人机多光谱影像结合逐步回归模型可以很好地监测冬小麦SPAD值动态变化。     

基于遥感影像植被指数变化量分析的冬小麦长势动态监测

为进一步探究利用中低分辨率影像监测小麦苗情的机理,丰富小麦长势动态监测的模式,结合2017-2018年定点观测试验,以GF-WFV数据为遥感影像源,研究了孕穗-开花期冬小麦主要长势变化量参数和产量及其与植被指数变化量间的定量关系,以逐步回归方法筛选目标长势变化量参数,分别构建及评价基于GF-WFV影像遥感植被指数变化量的孕穗-开花期叶片含氮量变化量和叶绿素含量变化量监测模型。结果表明,冬小麦叶片含氮量变化量（ΔLNC）和叶绿素含量变化量（ΔCHL）与产量密切相关,而孕穗-开花期的归一化植被指数变化量（ΔNDVI）、比值植被指数变化量（ΔRVI）分别与ΔLNC和ΔCHL相关性最好,因此选择这两个植被指数变化量作为敏感参量构建冬小麦长势监测模型。经验证,基于ΔNDVI和ΔRVI构建的长势线性模型可靠且精度高,其决定系数分别为0.70和0.64,均方根误差分别为0.39%和0.08 mg·L^-1FW。基于预测模型和实测数据分级量化表达冬小麦长势的空间分布状况,能够很好实现了基于GF-WFV时相影像长势不同等级的遥感监测。     

基于成像高光谱仪的大豆叶面积指数反演研究

高光谱遥感能连续获取地物光谱图像,这一技术能大大提高估算叶面积指数的水平。利用无人机搭载成像高光谱仪获取作物光谱信息反演叶面积指数对精准农业生产与管理意义重大。通过灰色关联度排序、赤池信息量准则和偏最小二乘法(GRA-PLS-AIC)选择了三角植被指数(TVI)、比值植被指数(RVI)、红边植被指数(NDVI705)、归一化植被指数(NDVI)和重归一化植被指数(RDVI)5种植被指数,结合田间实测的叶面积指数数据,采用经验模型构建多指数反演模型。通过无人机为平台同步搭载数码相机和成像高光谱仪,在山东省嘉祥县一带获取了大豆生殖生长期内的遥感影像,同时利用LAI-2200C植物冠层分析仪进行叶面积指数测定,将获取到的遥感影像和地面实测数据进行叶面积指数的反演。结果表明:在大豆生殖生长期内建多指数模型,建模结果的预测值和实测值的R~2和RMSE分别为0.701和0.672,验证结果的R~2和RMSE分别为0.695和0.534,预测模型有比较高的精度和可靠性,利用该模型来反演LAI是准确的,生成的大豆LAI分布图能反映当地当时大豆的真实长势情况。因此,以多旋翼无人机为平台同步搭载高清数码相机和成像高光谱仪组成的无人机农情监测系统对研究大豆叶面积指数反演是可行性,构建的多指数模型适用于大豆生殖生长期。     

Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars

Several sensor systems are available for ground-based remote sensing in crop management. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This work describes a study comparing active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors, including the Crop Circle, GreenSeeker, and an active flash sensor (AFS), were tested for their ability to assess six destructively determined crop parameters. Over 2 years, seven wheat (Triticum aestivum L.) cultivars were grown with nitrogen supplies varying from 0 to 220 kg ha⁻¹. At three developmental stages, the crop reflectance was recorded and sensor-specific indices were calculated and related to N levels and the crop parameters, fresh weight, dry weight, dry matter content, as percent of dry weight to fresh weight, N content, aboveground N uptake, and the nitrogen nutrition index. The majority of the tested indices, based on different combinations of wavelengths in the visible and near infrared spectral ranges, showed high r²-values when correlated with the crop parameters. However, the accuracy of discriminating the influence of varying N levels on various crop parameters differed between sensors and showed an interaction with growing seasons and developmental stage. Visible- and red light-based indices, such as the NDVI, simple ratio (R₇₈₀/R₆₇₀), and related indices tended to saturate with increasing crop stand density due to a decreased sensitivity of the spectral signal. Among the destructively assessed biomass parameters, the best relationships were found for N-related parameters, with r²-values of up to 0.96. The near infrared-based index R₇₆₀/R₇₃₀ was the most powerful and temporarily stable index indicating the N status of wheat. This index was delivered by the BDR, Crop Circle, and AFS. Active spectral remote sensing is more flexible in terms of timeliness and illumination conditions, but to date, it is bound to a limited number of indices. At present, the broad spectral information from bi-directional passive sensors offers enhanced options for the future development of crop- or cultivar-specific algorithms.     

基于倾斜遥感观测的小麦白粉病胁迫下叶绿素含量监测

为了探讨多角度遥感在白粉病胁迫下监测小麦叶绿素含量的适宜角度,以易感白粉病品种偃展4110和中感白粉病品种国麦301为试验材料,获取三种不同生长环境(病圃田、接种田和自然感病田)下抽穗至灌浆期小麦冠层多角度反射光谱及叶绿素含量,分析不同时期叶绿素含量变化及其与多角度反射率的关系,建立白粉病胁迫下小麦叶绿素含量监测模型。结果表明,由红边波段构建的光谱参数对白粉病胁迫下叶绿素含量变化反应敏感。优化筛选出的植被指数与叶绿素含量之间的相关性在前向角度观测时优于垂直角度观测,而垂直观测角度好于后向角度观测,整体上以前向20°最佳。植被指数中,光谱参数RES(红边对称度)表现较好,在前向20°下的监测精度达0.725。因此,在前向20℃观察条件下可用RES对白粉病危害后小麦冠层叶绿素含量变化进行有效监测。     

Correlation of Chlorophyll Meter Readings with Gas exchange and Chlorophyll Fluorescence in Flag Leaves of Rice (Oryza sativa L.) Plants

Abstract

The objective of this study was to establish the correlation of the chlorophyll meter (SPAD) readings with the contents of chlorophyll (Chl) and ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the gross photosynthetic rate (P_g), and the maximum quantum yield of photosystem II (PSII) (F_v/F_m) in flag leaves of rice (Oryza sativa L.) in ripening stage. The SPAD readings significantly correlated with the Chl content, the Rubisco content, P_g and F_v/F_m (R² = 0.848, 0.648, 0.671 and 0.712, respectively), which suggests that the SPAD meter has the potential to estimate the photosynthetic capacity of the flag leaves. However, both P_g and F_v/F_m had a stronger relationship with the Rubisco content than the SPAD readings, indicating that the PSII photochemical and CO₂ assimilation capacities are strongly influenced by the Rubisco content. Therefore, accurate calibration would be indispensable to obtain the physiological information from the SPAD readings of flag leaves.     

Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy

Given the importance of potassium (K) and phosphorus (P) contents to wheat yield and grain quality, and the very little experience that has been gained on nutritional monitoring of other than nitrogen using remotely sensed technologies, a study was undertaken to explore the possibility of identifying these mineral stresses using spectral data. Canopy spectra and biophysical data were collected from commercial and experimental fields in India and Israel. Traditional and newly developed vegetation indices, together with Partial Least Squares (PLS) regression models, were calculated in order to predict potassium and phosphorus contents from the wheat canopy spectral data. Results show that the application of PLS and specific narrow bands vegetation indices reached significant levels of accuracy in the retrieval of K and P levels, in comparison to traditional broad band indices. Additionally, it was observed that a significant improvement is obtained when the mineral total content is considered instead of the relative content. Therefore it was suggested that the biomass should also be retrieved from the spectral data. Finally, as very different crop conditions were included in this study, it was possible to confirm that the level of accuracy in the retrieval of K and P levels is related to the quality and variability of the data used for calibrating the models.     

Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Plant water status information of potato (Solanum tuberosum L. cv. Russet Burbank) is needed at the farm level for irrigation scheduling. This research investigated the feasibility of using a 5-megapixel digital camera to determine the leaf water potential (Ψ_L) of potato plants by capturing red, green, blue (RGB) digital images in the visible region of the electromagnetic spectrum. A greenhouse experiment was conducted in containerized cv. Russet Burbank potato plants subjected to five soil nitrate-nitrogen (N) levels and four soil water content levels. An artificial neural network (ANN) model, built with RGB images, RGB image transformations, RGB vegetation indices, and principal components analysis, found that for the validation data set, the measured Ψ_L and predicted Ψ_L results were from common populations. Other results showed: (1) a linear trend between soil nitrate-N levels and leaf reflectance in the G image band, (2) that the RG image bands were more suitable than the B image band for classifying leaf pigment from leaf shadow and leaf damage, (3) soil nitrate-N interacted with leaf greenness, affecting Ψ_L prediction, and (4) some image variables were more important than others in the ANN model. Although this greenhouse research shows promise, further field-based research is required to validate the selection of input neurons used and also validate the use of ANN modelling to determine Ψ_L at the plant canopy level with cv. Russet Burbank and other cultivars. In addition, an image acquisition method needs to be developed to obtain periodic representative sample coverage over a field.     

遥感光谱指标和神经网络结合的冬小麦地上部生物量估测

为探讨基于神经网络对小麦地上部生物量(aboveground biomass, AGB)进行遥感估测的可行性,在江苏省泰州泰兴市、盐城大丰区和宿迁沭阳县布设冬小麦大田试验,在对冬小麦近红外波段反射率(near-infrared band reflectance, REF_nir)、红光波段反射率(red band reflectance, REF_red)、归一化差值植被指数(normalized difference vegetation index, NDVI)、差值植被指数(difference vegetation index, DVI)、比值植被指数(ratio vegetation index, RVI)、土壤调节植被指数(soil adjusted vegetation index, SAVI)和优化土壤调节植被指数(optimized soil adjusted vegetation index, OSAVI)等7个遥感光谱指标与冬小麦生长指标(LAI和AGB)进行相关性分析基础上,构建基于BP神经网络的冬小麦AGB估测模型,并与...     

基于Landsat 8TM卫星数据的玉米灌浆期旱情监测

以灌溉区大田玉米为研究对象,利用Landsat 8TM卫星数据把冠层含水量指数MSI2与温度指数LST结合构建冠层水分温度指数(CTWDI),对研究区2014~2016年大田玉米灌浆期旱情进行监测。结果表明,CTWDI与灌浆期玉米冠层含水量极显著相关(r=0.719**),可以较好地反应灌浆期高覆盖度玉米水分状况。基于CTWDI的灌浆期玉米干旱监测结果与实际情况相符,且Kappa系数均高于57%,改进了玉米生育中后期的旱情监测方法,可以利用CTWDI对灌浆期高覆盖度条件下的玉米进行干旱监测。     

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。     

河南省冬小麦长势遥感监测指标的适宜性

为了解不同监测指标在河南省各个监测地域分区中对小麦不同生育期长势监测的有效性和适宜性,以MODIS为主要数据源,在长势监测地域分区的基础上,从小麦实时监测和生长过程监测两个角度出发,对NDVI、LAI、NPP、NDWI和TCI等监测指标与长势等级和单产进行了相关分析。结果表明,在各长势监测地域分区中,NDVI、LAI和NPP都与长势等级呈负相关,与单产呈正相关,一般在抽穗期和开花期相关性最高。NDWI在各个监测分区都表现不稳定,且在不同生育期与长势等级和单产的相关性也较低。TCI在豫西山地丘陵区和豫南地区与长势等级呈正相关,与单产呈负相关,相关性在起身期和拔节期相对较高。说明,不同长势监测分区需要用不同监测指标进行长势监测,在同一长势监测分区不同生育期遥感监测的适宜指标也存在差别。     

不同地类春小麦叶片叶绿素含量高光谱植被指数估算模型研究

为给小麦长势的遥感监测提供依据,利用多种植被指数对比分析了水浇地和旱地春小麦不同生育期冠层光谱及叶绿素含量的变化,并建立了不同地类春小麦叶绿素含量的最佳估测模型。结果表明,春小麦叶绿素含量在整个生育期呈先升后降趋势,且水浇地高于旱地。春小麦冠层光谱在可见光波段表现为阳坡和双面坡地>阴坡地>水浇地,而在近红外区域反之。在起身期-乳熟期,春小麦叶绿素含量分别与二次修正土壤调节植被指数和植被衰老反射率指数的相关性最好;在拔节-扬花期,水浇地和阴坡地的叶绿素含量分别与绿度植被指数和修正归一化差异指数相关性最好,阳坡和双面坡地则与二次修正土壤调节植被指数的相关系数最大。利用相关性最好的植被指数模拟春小麦叶绿素含量,水浇地在起身-扬花期宜用抛物线模型,乳熟期则适合用乘幂模型,且各模型r和检验r均大于0.88,拟合程度较高;阴坡、阳坡和双面坡地起身期适用指数模型,其余时期适合抛物线模型。     

冬小麦花期生理形态指标与卫星遥感光谱特征的相关性分析

为提高冬小麦花期长势遥感监测的精确性与普适性,在集丘陵地、河滩地及平原地为一体的冬小麦种植区域设计了田间试验,利用卫星影像信息结合地面GPS定点试验数据,在分析利用遥感技术监测冬小麦花期LAI和生物量两个主要群体长势指标可行的基础上,对卫星遥感光谱信息与冬小麦花期的主要生理生化指标(叶片叶绿素含量、叶片类胡萝卜素含量、叶片水分含量和叶片氮素含量)进行了综合分析.结果表明,NDVI(归一化植被指数)与LAI、叶片氮素含量和叶片水分含量的相关性较好,RVI(比值植被指数)与生物量和叶片类胡萝卜素含量的相关性较好,GVI(绿度植被指数)与叶片叶绿素含量的相关性较好,且均达到显著水平.说明NDVI、RVI及GVI是较为理想的可用于监测冬小麦花期生理形态指标的敏感遥感指数.进一步以这些敏感遥感指数作为因变量建立了冬小麦花期生理形态指标的遥感监测模型.利用本模型可以反演多个生理形态指标,便于对冬小麦花期的长势情况进行综合分析与判断.     

基于多载荷无人机遥感的大豆地上鲜生物量反演

以多载荷无人机获取数据和地面实测的数据为基础,将大豆生殖生长期分段建模,采用植被指数和光谱参数相结合再加上农学参数株高,通过最小二乘法建立多元线性回规模型的方法,来估算大豆开花期和结荚期的鲜生物量,采用高光谱植被指数法估算大豆鼓粒期和成熟期的鲜生物量。结果表明:在大豆开花期和结荚期内,采用混合法构建生物量反演模型利用交叉验证法,验证结果的R~2和RMSE分别为0.714和0.393;在大豆鼓粒期和成熟期内,采用高光谱植被指数法构建生物量反演模型,利用交叉验证法,验证结果的R~2和RMSE分别为0.697和0.386;大豆开花结荚期构建的模型和鼓粒成熟期构建的模型都有比较高的精度和可靠性,利用这两种模型完成了高光谱影像鲜生物量的遥感空间制图,能反映当地当时大豆的真实长势情况。     

多遥感光谱指标结合进行大田冬小麦叶片叶绿素含量估测研究

为解决大田冬小麦叶片叶绿素含量估测模型精度低、通用性弱的问题,在获取冬小麦拔节期和抽穗期冠层红光波段反射率(BR_red)和近红外波段反射率(BR_nir)的基础上,计算归一化差值植被指数(NDVI)、差值植被指数(DVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)、改进型比值植被指数(MSR)、重归一化植被指数(RDVI)、II型增强植被指数(EVI2)和非线性植被指数(NLI)等8个植被指数。经统计分析,选择与叶片叶绿素含量(SPAD值)相关性较好的5个遥感光谱指标(NDVI、MSR、NLI、BR_red和RVI)作为输入变量,建立了冬小麦叶片叶绿素含量的BP神经网络估测模型(WWLCC_BP),并对估测模型进行精度验证。结果表明,WWLCC_BP估测模型在拔节期估测的决定系数(r²)为0.84,均方根误差(RMSE)为5.39,平均相对误差(ARE)为9.87%。抽穗期的估测效果与拔节期较为一致。将WWLCC_BP和高分六号影像...     

Improvement of nitrogen management in rice paddy fields using chlorophyll meter (SPAD)

Optimum rate and timing application of nitrogen (N) fertilizer are most crucial in achieving high yield in irrigated lowland rice. In order to assess leaf N status, a semidwarf rice cultivar (Khazar) was grown with different N application treatments (0, 40, 80, and 120 kg N ha⁻¹ splited at transplanting, midtillering, and panicle initiation stages) in a sandy soil in Guilan Province, Iran, in 2003. The chlorophyll meter (SPAD 502) readings were recorded and leaf N concentrations were measured on the uppermost fully expanded leaf in rice plants at 10-day internals from 19 days after transplanting to grain maturity. Regression analysis showed that the SPAD readings predicted only 23% of changes in the leaf N concentration based on pooled data of leaf dry weight (N _dw) for all growth stages. However, adjusting the SPAD readings for specific leaf weight (SPAD/SLW) improved the estimation of N _dw, up to 88%. Specific leaf weight (SLW), SPAD readings, leaf area and weight as independent variables in a multiple regression analysis predicted 96% of the N _dw changes, while SPAD readings independently predicted about 80% of leaf N concentration changes on the basis of leaf area (N _a). It seems that chlorophyll meter provides a simple, rapid, and nondestructive method to estimate the leaf N concentration based on leaf area, and could be reliably exploited to predict the exact N fertilizer topdressing in rice.