基于主成分分析的植被含水率模型

为了对岷江上游“生态水”的估测提供有效的数据源和方法,利用高光谱遥感技术定量研究了植被反射光谱与植被含水率的关系,测定了研究区多个采样点棕榈叶片的反射光谱和对应的含水率,通过二者的相关分析和逐步回归的方法提取敏感波段;为避免敏感波段之间相关性影响,采用主成分分析法提取主成分,建立主成分与含水率的定量分析模型,并建立主成分与标准自变量的回归方程,然后建立各个标准变量与原始自变量(反射光谱敏感波段)的回归方程,最终转换为植被含水率与反射光谱之间的模型.结果表明:棕榈叶片反射光谱在454 nm,668 nm,1 466 nm,1 664 nm和1 924 nm波段处与含水率显著相关;采用主成分定量分析模型的估算值与实测值相关系数为0.92,均方根误差为0.06.     

植被冠层光谱和叶片光谱的尺度转换

叶片光谱是估算植被生化参数的重要依据。然而,遥感影像获取的光谱为像元及冠层光谱,因此,在进行植被生化参数的遥感定量估算时,需将冠层光谱转化到叶片尺度。根据几何光学模型原理,推导出植被冠层光谱和叶片光谱的尺度转换函数,将冠层光谱转换到叶片尺度。首先,采用叶片光谱模拟模型PROSPECT模拟出叶片水平的光谱;其次,在几何光学模型4-scale模型中,通过改变叶片光谱和叶面积指数（leaf area index,LAI）,模拟出不同叶片特征下的冠层光谱。最后,通过LAI建立两个查找表,一个是传感器观测到树冠光照面和背景光照面概率的查找表,另一个是多次散射因子M的查找表,从而实现冠层光谱和叶片光谱的转化。结果表明,利用4-scale模型能实现冠层光谱与叶片光谱的尺度转换,此方法有很好的适用性。     

水稻叶片最优叶肉结构参数估算

叶片作为植物冠层的基本组成元素,其自身的光学特性直接影响着遥感所能获得的植物冠层反射光谱。从原理上讲,叶片的光学特性不仅取决于其内部生化组分含量的多少,还与其物理结构密切相关。因此对叶片内部物理结构进行估算有助于分离其对叶片光谱的影响,从而提高叶片生化信息反演的精度。在基于叶片内部辐射传输过程的PROSPECT模型中,叶片内部结构用一个假想的叶肉结构参数N来描述。PROSPECT模型模拟光谱发现,N对叶片反射率和透过率均影响显著,且影响范围涵盖400—2500nm的全部波段。本文利用水稻叶片实测光谱和生化数据尝试了3种N的估算方法,包括两种经验方法和一种模型反演方法,并对其进行比较。结果表明,由于两种经验方法都基于N和表观叶面积（SLA）之间的非线性经验公式,因此两者具有内在的数学关系。运用模型反演方法估算的N可在实测水稻光谱和模型模拟光谱间得到最小RMSE,且其在数值上小于两种经验方法的估算值。以N为因变量,叶片光谱反射率为自变量,运用逐步线性回归分析建立了N的光谱估算模型,550nm,816nm,1210nm和1722nm四个波段被选入模型,回归效果较好,为N的估算提供了一种新的经验方法。     

叶片表面蜡质层对植被光谱反射率的影响

首先获取叶片去除表面蜡质层前后光谱反射率,比较分析叶片表面蜡质层的光谱特征,探究叶片去除蜡质层前后叶片反射率的变化.结果表明:叶片去除蜡质层后在400～2500 nm光谱区间反射率发生较明显改变;去除蜡质层对植被红边参数没有影响,并不会导致"红边"移动,叶片表面的蜡质并不影响绿色植被所特有的反射特征;不同叶片蜡质层对不同植被影响不同,叶片反射曲线不是叶片表面蜡质反射曲线和经去蜡质处理的叶片反射曲线简单的线性叠加.植被叶片的光谱定量分析可为公路植被遥感环境评价提供支持.     

小麦冠层理化参量的高光谱遥感反演试验研究

以国产成像光谱仪所获高光谱遥感数据为基础，根据田间同步采样数据建立的基于反射光谱特征的小麦冠层生物物理和生物化学估计模型，实现了用航空高光谱遥感数据对田间小麦冠层理化参量的整体反演。结果表明：用高光谱遥感方法估计小麦冠层理化参量是可行的；以理化参数为“波段”的数字图像及其处理，为农学家以理化参量的空间分布及其差异解释作物产量空间分布差异和研究作物生态生理机理提供了新的手段。     

基于光谱吸收深度分析的冬小麦生物量估算模型的建立

准确的估算作物的生物量,对作物长势监测具有重要的意义。利用高光谱仪获取的冬小麦高光谱实测数据,通过植被参数分析、植被光谱吸收特征挖掘,构建了冬小麦生物量的高光谱估算模型。结果表明,基于光谱深度分析与偏最小二乘方法建立的估算模型的R2值为0.86,RMSE为0.0397kg/m~2,较基于植被参数的生物量估算模型,模型精度得到了大幅的提高。本研究证实了利用光谱深度技术可以准确地挖掘光谱数据的"红谷"波段与生物量之间的关系,从而实现冬小麦生物量估算精度的提高。     

开封市郊冬小麦冠层反射光谱特征分析

通过对开封市郊冬小麦整个生育阶段反射光谱的测量，分析了不同生育阶段、播种垄向及土壤背景对冠层光谱反射的影响。结果表明：不同生育阶段的冬小麦反射光谱特性总体趋势符合植被的反射光谱特性，但是又有一些差异；不同垄向冬小麦的反射光谱也不一样，南北垄向的光谱反射率高于东西垄向光谱的反射率；不同土壤背景的冬小麦反射光谱也存在差异。     

模拟酸雨对水稻叶片反射光谱特性影响的初步研究

本文研究了模拟酸雨对水稻叶片反射光谱特性的影响。结果表明：模拟酸雨会引起水稻叶片反射光谱的可见光区和中红外区的反射率升高，近红外区的反射率降低，相应的反射率比值也随之变化，一阶和二阶微分光谱蓝移，且上述变化的程度与酸雨的酸度、水稻的品种和生育期有关。这一结果也表明遥感技术监测酸雨污染作物是可行的。     

土壤中镉、铜伤害对水稻光谱特性的影响

本文讨论了水稻受重金属镉和铜污染伤害后的光谱反射特性的变化,为遥感监测污染提供基本依据。使用高分辨率光谱辐射计在自然状态下实地测量了受污染的水稻光谱特性,比采集叶片在室内测量更接近于实际情况,便于结合遥感图像进行定性和定量分析研究。 结果表明,镉和铜拌土生长的水稻在分蘖期受到的影响最明显,无论是在生理上还是在反射光谱方面变化都比较显著。因此,对水稻受重金属污染的遥感监测最佳时间为分蘖期,有效波段为0.54—0.58,0.64—0.69,0.74—0.80微米。综合对水稻光谱的各种分析方法,如波形分析,微分光谱,绿度指数和主成分变换等技术,水稻在分蘖期,对高浓度的监测效果较好,而对低浓度效果不甚明显。     

松嫩平原典型土壤高光谱定量遥感研究

为实现松嫩平原典型土壤理化参数时空信息的快速获取,为定量遥感、精准农业等相关研究服务,以松嫩平原典型土壤的高光谱反射率为研究对象,分析土壤反射光谱特征及其与土壤理化参数的关系,建立基于反射光谱指数的土壤理化参数遥感估算模型;提取黑土光谱特征点,建立黑土反射光谱曲线模拟函数.结果表明:松嫩平原不同土壤光谱特征差异主要在450-600,600-800 nm两个吸收谷部分,土壤有机质是黑土反射光谱特征的决定因素;不同于南方土壤,铁对松嫩平原典型土壤反射光谱特征的影响较小;随着含水量的增加,土壤水分对土壤光谱反射率的作用过程可以用三次方程定量描述;基于土壤反射率及反射光谱特征的土壤理化参数光谱预测模型可以用于土壤相关理化参数的快速测定;基于光谱特征点的黑土反射光谱曲线模拟函数可以准确描述黑土的反射光谱特征,这一方法可以用于高光谱数据压缩和基于多光谱数据的高光谱反射率重建.     

不同钾素处理春玉米叶片营养元素含量变化及其光谱响应

目的是研究不同钾营养水平春玉米典型生育期叶片的光谱响应,探索叶片内营养成分与叶片光谱反射率的相关性。方法是设置了不同梯度钾处理的盆栽试验,按玉米生育期进行光谱测定和取样分析。结果,通过对不同钾处理间玉米叶片养分含量的差异性分析表明,随着施钾的提高,叶片钾含量差异性达到显著水平。分析不同钾营养水平不同生育时期春玉米叶片光谱反射率与叶片钾含量的相关关系,并建立了喇叭口期利用叶片光谱反射率估测叶片钾含量的数学模型;以及分析了该处理下喇叭口期叶片内水分、叶绿素、氮、磷、钙、镁、锌、锰、铜、铁含量与叶片光谱反射率的相关性。结果表明：不同生育时期叶片钾含量与其光谱反射率的相关关系在光谱维方向存在明显差别,730—930nm和960—1100nm两波段为春玉米喇叭口期评价钾营养状况的敏感波段,光谱变量R767＋R1057,（R767＋R1057） /（logR767＋logR1057）和（R767-R1057） /（logR767-logR1057）均能很好的预测喇叭口期叶片钾含量;该时期叶片内不同成分与光谱反射率相关分析表明：550nm,710nm,950nm三波段处是各个相关曲线的突变点;叶片内各成分间高度相关的,它们的光谱相关曲线趋势也极为一致或对称。     

基于稳健估计的白桦叶片铜元素含量反演

根据阔叶叶片模型（a model of leaf optical properties spectra,PROSPECT）叶片辐射传输模型机理,利用一次范数稳健估计估算叶片结构参数N和铜元素的吸收系数k_Cu。选取黑龙江呼玛地区作为研究区,利用美国ASD公司的FieldSpec 3 Hi-Res光谱仪野外测定白桦叶片的反射光谱,实验室测定相应叶片的铜含量,利用改进的PROSPECT-Cu模型进行白桦叶片铜元素含量反演。通过与野外样品测定值和反演值进行比较分析,决定系数为0.963。研究结果表明,反演结果得到的叶片Cu含量是准确的,反演方法是可行的。     

Studies on spectral reflectance under normal and nitrogen,phosphorus and pest and disease stress condition in soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L.)

The results emerged out of the studies on spectral reflectance under normal and nitrogen and phosphorus stress condition in soybean (Glycine max L.) conducted at Marathwada Agricultural University experimental farm, Parbhani duringkharif 2004–05 showed that crop growth and bio-physiological parameters viz., Height, chlorophyll, leaf area index and total biomass influenced by pest and disease and nutrient stress resulted in detectable spectral reflectance variation. Poor crop growth, reduced canopy cover, chlorophyll content and biomass production are the effects observed in nutrient deficient crops. These above changes in soybean crop were related to spectral indices (RVI and NDVI) that are resulted in discrimination of stressed and normal (non-stressed) soybean crop.     

Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation

Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season – due to its phenology – and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent.We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs.Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

Use of high spectral resolution remote sensing to determine leaf palatability of eucalypt trees for folivorous marsupials

In this study we assess the feasibility of remotely measuring canopy biochemistry, and thus the potential for conducting large-scale mapping of habitat quality. A number of studies have found nutrient composition of eucalypt foliage to be a major determinant of the distribution of folivorous marsupials. More recently it has been demonstrated that a specific group of secondary plant chemicals, the diformylphloroglucinols (DFPs), are the most important feeding deterrents, and are thus vital determinants of habitat quality. We report on the use of laboratory spectroscopy to attempt to identify one such DFP, sideroxylonal-A, in the foliage of Eucalyptus melliodora, one of the few eucalypt species browsed by folivorous marsupials. Reflectance spectra were obtained for freeze-dried, ground leaves using near infrared spectroscopy (NIRS) and for both oven-dried and fresh whole leaves using a laboratory-based (FieldSpec) spectroradiometer. Modified partial least squares (MPLS) regression was used to develop calibration equations for sideroxylonal-A concentration based on the reflectance spectra transformed as both the first and second difference of absorbance (Log 1/R). The predictive ability of the calibration equations was assessed using the standard error of calibration statistic (SECV). Coefficients of determination (r²) were highest for the ground leaf spectra (0.98), followed by the fresh leaf and dry leaf spectra (0.94 and 0.87, respectively). When applied to independent validation sub-sets, sideroxylonal-A was most accurately predicted from the ground leaf spectra (r² = 0.94), followed by the dry leaf and fresh leaf spectra (0.72 and 0.53, respectively). Two spectral regions, centred on 674 nm and 1394 nm, were found to be highly correlated with sideroxylonal-A concentration for each of the three spectral data sets studied. Results from this study suggest that calibration equations derived from modified partial least squares regression may be used to predict sideroxylonal-A concentration, and hence leaf palatability, of Eucalyptus melliodora trees, thereby indicating that the remote estimation of habitat quality of eucalypt forests for marsupial folivores is feasible.     

Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.