Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r² = 0.30 and RMSE = 1.6 Mg ha^?1 in 2012 and r² = 0.37 and RMSE = 4.0 Mg ha^?1, but was strongly correlated for the high biomass sorghum in 2013 (r² = 0.72 and RMSE = 4.6 Mg ha^?1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.     

Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance

Bacterial leaf blight (BLB) is an important vascular disease of irrigated rice and serious infestations may cause a significant loss of yield. This study analyzed hyperspectral canopy reflectance spectra of two rice cultivars with different susceptibilities to BLB to establish spectral models for assessing disease severity for future site-specific management. The results indicated that wavebands from 757 to 1039 nm were the most sensitive region of the spectrum for the moderately susceptible cultivar TNG 67, whereas most narrow bands showed a significant relationship for the highly susceptible cultivar TCS 10. All the spectral indices (SIs) calculated had significant relationships with proportions of infested area in cultivar TCS 10, but only two SIs correlated significantly with cultivar TNG 67. The relation between the severity of the disease and spectral reflectance for the less susceptible cultivar TNG 67 can be improved by using a multiple linear regression approach.     

Ground-level hyperspectral imagery for detecting weeds in wheat fields

Site-specific weed management can allow more efficient weed control from both an environmental and an economic perspective. Spectral differences between plant species may lead to the ability to separate wheat from weeds. The study used ground-level image spectroscopy data, with high spectral and spatial resolutions, for detecting annual grasses and broadleaf weeds in wheat fields. The image pixels were used to cross-validate partial least squares discriminant analysis classification models. The best model was chosen by comparing the cross-validation confusion matrices in terms of their variances and Cohen’s Kappa values. This best model used four classes: broadleaf, grass weeds, soil and wheat and resulted in Kappa of 0.79 and total accuracy of 85 %. Each of the classes contains both sunlit and shaded data. The variable importance in projection method was applied in order to locate the most important spectral regions for each of the classes. It was found that the red-edge is the most important region for the vegetation classes. Ground truth pixels were randomly selected and their confusion matrix resulted in a Kappa of 0.63 and total accuracy of 72 %. The results obtained were reasonable although the model used wheat and weeds from different growth stages, acquisition dates and fields. It was concluded that high spectral and spatial resolutions can provide separation between wheat and weeds based on their spectral data. The results show feasibility for up-scaling the spectral methods to air or spaceborne sensors as well as developing ground-level application.     

基于光谱指数的冬小麦变量施肥冠层光谱特征研究及产量分析

 【目的】由适时获得的高光谱数据代替传统繁琐的实验室土壤养分测定数据来进行变量施肥，实现冬小麦高产优质的目标。【方法】本研究利用冬小麦起身期和拔节期冠层光谱数据，选用反映冬小麦长势信息的优化土壤调节植被指数（OSAVI，optimization of soil-adjusted vegetation index）和变量施肥模型进行变量施肥管理（变量区），以相邻地块常规非变量（均一）施肥区（对照区）为对照，研究了不同氮肥处理冬小麦冠层光谱特征及其施肥效应。【结果】变量施肥之后两种氮肥处理在敏感波段670 nm和760～900 nm处反射率差异明显，而670nm和760～900nm是氮素和冠层的敏感波段，说明进行变量施肥时，利用基于这两个波段组合的光谱指数OSAVI优于其它波段组合的光谱指数；SAVI不同生育时期的变化情况，反映了变量施肥在调控作物长势及群体结构上的优势；与对照区相比变量区提高产量达378.72 kg•ha-1，并降低了各小区产量之间的变异,变量区土壤硝态氮浓度降低，氮肥利用率提高，生态效益较为明显。【结论】该技术通过改善冬小麦群体质量，延缓了植株衰老，促进干物质和氮积累，增加冬小麦产量和氮肥利用率。     

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R₇₀₀/R₆₇₀) and 57% (R₄₁₈/R₄₀₅), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R₇₆₃/R₇₆₁), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R₃₇₀ and R₄₀₀ as either simple ratio or a normalized difference index performed most consistently in both experimental (R ² = 0.58) and farmers’ fields (R ² = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.     

Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r ² = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r ² = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r ² = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.     

Digital infrared thermography for monitoring canopy health of wheat

Computer-aided diagnosis and prognosis models have been used for management decisions in crop protection. Initial infection rate, temperature and leaf wetness are important parameters in disease epidemiology and for decision support models. So far, in-field variability and variability between fields have not been taken into account for management decisions in disease control. This study aimed at testing the use of an imaging IR thermography system as a tool for monitoring the microclimatic conditions promoting incidence and severity of diseases within wheat fields with a high spatial resolution. Experiments were conducted on the detection and differentiation of leaf wetness on a single leaf scale and a crop canopy scale (1 m²) under controlled conditions. Field studies focused on comparing ground-based and air-borne thermographic data and linking these to ground-truth data.     

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction. The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield.     

基于近地高光谱遥感的小麦叶片生长参数动态模型研究

【目的】探讨利用遥感技术实时监测小麦叶片生长动态变化。【方法】以生产中大面积推广应用的小麦品种周麦27为试验材料,在2个试验地点布置水氮耦合处理试验,依据相关回归分析筛选出对叶片氮含量（LNC）和叶面积指数（LAI）反应敏感的高光谱植被指数,进而确立了不同产量层次的植被指数生育进程动态模型。【结果】LNC和LAI与近红外短波段735～1 075 nm呈显著正相关关系,而与可见光波段350～730 nm呈显著负相关关系。对LNC敏感的植被指数主要有AIVI、RES和mND924,而对LAI敏感的植被指数主要有ONLI、CI_green和MSR（800,670）,以上2类植被指数和籽粒产量间关系均密切,表现较好的时期主要为拔节期至灌浆中期阶段。采用双LOGISTIC模拟模型方法,优选的方程能够较好地模拟植被指数的生育进程动态轨迹,模型精度（R²）随着产量水平的逐渐提高而增加,低产水平的精度相对较差（0.627～0.703）,而高产及以上水平的R²较高（0.868～0.972）。【结论】高光谱植被指数AIVI和CI_gr...     

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

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.     

旱地冬小麦冠层温度和其产量以及产量构成因素之间的关系

对旱地小麦冠层温度和产量及产量构成因素的关系分析可知：冠层温度（CT）和产量之间的相关性均呈现出增强—减弱—增强的变化趋势,判定系数R2的最大值出现在5月22日（R2=0.710）。8个监测日冠层温度和产量之间有极显著的负相关,随着CT的降低,产量提高,冠层温度偏低的品种其产量高,而冠层温度偏高的品种其产量低。参试品种（系）中,冠层温度偏低型的定鉴3号和冠层温度偏高型的沧核038冠层温度差达到4~5℃,产量之间相差2.1t/hm2。冠层温度和产量构成因素当中,冠层温度和千粒重、小穗数都呈现出负相关。冠层温度和产量及产量构成因素之间的相关性由强到弱依次为：产量,千粒重,小穗数,穗粒数。     

结合纹理信息Hyperion高光谱影像分类

高光谱遥感光谱特征明显,单纯利用其光谱优势难以达到影像分类精度要求,特别是区分植被精细类别。为了进一步提高Hyperion高光谱影像分类精度,研究加入包含区域亮度变化及结构特征的纹理信息,试图提高分类精度。以杭州市余杭区百丈镇为试验区,首先提取研究区道路、建筑物、农田、毛竹Phyllostachysedulis林、马尾松Pinusmassoniana林和栎类Quercus等7种类型的端元光谱,然后对端元进行线性光谱分离,利用二阶概率矩阵对线性光谱分离出的8个波段提取纹理特征,最终结合线性分离后的端元光谱实现分类。结果表明：纹理信息融入后分类结果较单源信息光谱角制图和单源信息支持向量机方法有明显的改善,建筑物精度分别提高了34．13％和17．16％,农田提高了19．71％和9．24％,马尾松则改善了27．09％和5．42％,栎类精度提高了近3．00％和10．00％,且一定程度上避免了椒盐效应。采用光谱与纹理信息结合的方法对Hyperion高光谱影像分类是可行的。分类过程中端元的提取、纹理分析时特征向量的组合及纹理移动窗口大小的选择对分类结果起重要的作用。图6表1参19     

Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat

Nitrogen (N) content is an important factor that can affect wheat production. The non-destructive testing of wheat canopy leaf N content through multi-angle hyperspectral remote sensing is of great importance for wheat production and management. Based on a 2-year experiment for winter wheat in Lethbridge (Canada), Zhengzhou (China), and Kaifeng (China) growing under different cultivation practices, the authors studied the relationships between N content and wheat canopy spectral data in solar principal plane (SPP) and perpendicular plane (PP) at different observation angles. Modeling was conducted according to the spectrum index with the highest correlation coefficient and the corresponding observation angle. The results showed that correlation coefficient between the spectral index and canopy leaf N content at each observation angle of the SPP was significantly higher than that of the PP. Significant differences in the correlation coefficient were also observed at different observation angles of the same observation plane, and the correlation coefficients of angles of ?30° and ?40° were higher than others. A model fitted by a power function by using mND705 as independent variable at an angle of ?40° in the SPP showed the highest accuracy.     

基于高光谱的大豆含杂率反演模型

针对大豆联合收割机械作业含杂率在线检测手段缺乏的问题,以亚丰4YZL-5S联合收获机机械化收获的大豆样本为研究对象,在室内测定大豆样本的含杂率;利用ASD FieldSpec 4 Wide-Res型地物光谱仪测量大豆样本的光谱数据,经数据预处理和数学变换后获得2种光谱指标,即原始光谱数据(REF)和原始光谱经倒数之对数预处理后的数据(LR),应用波段间自相关分析筛选出不同指标的大豆样本光谱的特征波长,并采用支持向量机回归分析构建基于不同指标的大豆样本含杂率的反演模型,在此基础上对反演结果进行精度验证和比较。试验结果表明:各预处理条件下的大豆含杂率敏感波段不同,其中REF的特征波段为512,738,851,1 104,2 003,2 179 nm;LR的特征波段为519,637,820,924,1 121,1 933,2 050,2 138 nm。本研究建立的含杂率反演模型的建模决定系数0.86,验证决定系数0.79,均方根误差0.32,相对分析误差1.7,表明模型具有较强的拟合效果和预测能力。相比较而言,利用REF建立的反演模型的反演效果略优于LR。本研究建立的大豆样本含杂率光谱反演模型能够实现含杂率的在线预测,为大豆机械化作业中含杂率的在线快速监测提供了新途径。     

Detection of common defects on oranges using hyperspectral reflectance imaging

To detect various common defects on oranges, a hyperspectral imaging system has been built for acquiring reflectance images from orange samples in the spectral region between 400 and 1000 nm. Oranges with insect damage, wind scarring, thrips scarring, scale infestation, canker spot, copper burn, phytotoxicity, heterochromatic stripe, and normal surface were studied. Hyperspectral images of samples were evaluated using principal component analysis (PCA) with the goal of selecting several wavelengths that could potentially be used in an in-line multispectral imaging system. The third principal component images using six wavelengths (630, 691, 769, 786, 810 and 875 nm) in the visible spectral (VIS) and near-infrared (NIR) regions, or the second principal component images using two wavelengths (691 and 769 nm) in VIS region gave better identification results under investigation. However, the stem-ends were easily confused with defective areas. In order to solve this problem, representative regions of interest (ROIs) reflectance spectra of samples with different types of skin conditions were visually analyzed. The researches revealed that a two-band ratio (R875/R691) image could be used to differentiate stem-ends from defects effectively. Finally, the detection algorithm of defects was developed based on PCA and band ratio coupled with a simple thresholding method. For the investigated independent test samples, accuracies of 91.5% and 93.7% with no false positives were achieved for both sets of selected wavelengths using proposed method, respectively. The disadvantage of this algorithm is that it could not discriminate between different types of defects.     

Effects of changing simulated sky cover on hyperspectral reflectance measurements for dry matter yield and forage quality prediction

Sensor based analysis methods to assess dry matter yield and nutritive values of legume-grass swards are time and labour saving and can facilitate a site-specific forage management. Nevertheless, in-field measurements, based on canopy reflectance are highly dependent on weather conditions, like, e.g. wind or clouds. This study was conducted to explore the potential of field spectral measurements for a non destructive prediction of dry matter yield (DM), metabolisable energy (ME), ash content (XA), and crude protein (XP), of a binary legume-grass mixture (Trifolium pratense L. and Lolium multiflorum L.) under changing weather conditions. Five different degrees of sky cover were simulated by shadowing measurement plots with layers of cotton to reduce incoming radiation at different growth stages (leaf developing to flowering). Additionally, a halogen lamp was established over the plots to examine the influence of an artificial light source on the spectral response under changing cloud stages. Modified partial least squares (MPLS) regression was used for analysis of the hyperspectral data set (350-2500 nm). Artificial illumination led to spectral interferences of solar radiation and additional light, and hence, partly reduced prediction accuracies. In contrast, prediction accuracy increased, when solar radiation was completely excluded. Coefficients of determination (RSQ_cal) range from 0.87 to 0.94 without artificial illumination and from 0.86 to 0.94 with artificial illumination for DM yield and nutritive values, respectively.     

确定监测区域建立森林郁闭度估测方程最优样地的研究

在对监测区域样地进行合理分类的基础上,需抽取一定数量能代表监测区域森林资源分布状况的最优样地,根据所抽样地对应影响郁闭度估测的主要遥感和GIS因子,可建立以样地为单位的郁闭度估测方程. 最优样地是指能代表监测区域森林种类及分布状况的样地,它包含样地的数量及样地的代表性两个方面的含义. 如何抽取最优样地属多目标优化问题. 该文在理论描述特定监测区域最优样地数学模型的基础上,通过实例进行了系统研究与分析,有效解决了最优样地的抽样问题,所得结果可用于指导生产实践.      

An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI

Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R²?=?0.762 and root mean square error (RMSE)?=?2.74 t/ha) with field-measured biomass.

     

小麦遥感估产研究现状及趋势分析

在分析国内外小麦遥感估产研究进展的基础上,指出目前小麦遥感估产存在的主要问题,并就小麦遥感估产精度的提高、遥感和模型结合的估产研究与应用、极端气候条件下的遥感估产以及遥感估产技术的信息集成化等方面进行了思考。