滨海盐土土壤水分的高光谱参数及估测模型

基于滨海盐土5个试验点的土壤含水量和室内土壤表面高光谱反射率,综合分析了350~2500 nm波段范围内土壤含水量与土壤光谱之间的关系,并基于比值光谱指数(RSI)、归一化光谱指数(NDSI)和差值光谱指数(DI)确定了光谱参数,进而构建土壤含水量估测定量模型.结果表明:滨海盐土原始光谱反射率与土壤含水量呈显著负相关关系,且最大负相关出现在1930 nm(r=0.86)附近.对RSI、NDSI和DI的直线回归方程、幂函数回归方程进行对比,以RSI(R_(1407),R_(1459))为自变量构建的土壤含水量指数函数线性回归方程决定系数最大(0.780),标准误较小(0.016),拟合方程为y=0.00001e~(9.72053 x).估测模型能够更好地监测滨海盐土土壤水分状况.基于RSI(R_(1407),R_(1459))构建的模型可实现对江苏省滨海盐土土壤水分的精确监测.     

滨海盐土土壤水分的高光谱参数及估测模型

基于滨海盐土5个试验点的土壤含水量和室内土壤表面高光谱反射率,综合分析了350～2500 nm波段范围内土壤含水量与土壤光谱之间的关系,并基于比值光谱指数(RSI)、归一化光谱指数(NDSI)和差值光谱指数(DI)确定了光谱参数,进而构建土壤含水量估测定量模型.结果表明： 滨海盐土原始光谱反射率与土壤含水量呈显著负相关关系,且最大负相关出现在1930 nm(r=0.86)附近.对RSI、NDSI和DI的直线回归方程、幂函数回归方程进行对比,以RSI(R₁₄₀₇,R₁₄₅₉)为自变量构建的土壤含水量指数函数线性回归方程决定系数最大(0.780),标准误较小(0.016),拟合方程为y=0.00001e^9.72053x.估测模型能够更好地监测滨海盐土土壤水分状况.基于RSI(R₁₄₀₇,R₁₄₅₉)构建的模型可实现对江苏省滨海盐土土壤水分的精确监测.     

基于分数阶微分优化光谱指数的土壤电导率高光谱估算

土壤电导率与含盐量具有高度相关性,精准的土壤电导率监测有助于了解区域土壤的盐渍化程度,对区域盐渍化防治与调控,农业可持续发展以及生态文明建设具有重要意义。为寻求预测土壤电导率的最佳高光谱参数,实现土壤盐分信息的高效监测,本研究对土壤样品进行室内高光谱和电导率测定,利用两波段优化算法对简化光谱指数(nitrogen planar domain index, NPDI)进行波段优化,筛选不同高光谱数据(原始高光谱反射率及其对应的5种数学变换)运算下的最敏感高光谱参数,从而建立土壤电导率高光谱估算模型。结果表明:1)NPDIs与土壤电导率之间的相关性显著,在原数据及其平方根、倒数、对数倒数、1.6阶微分变换形式下,优化光谱指数对土壤电导率的敏感程度更强,相关系数绝对值均超过0.80,且基于1.6阶微分变换的(R_(2020nm)+R_(1893 nm))/R_(1893 nm)波段组合相关系数绝对值最高,达到0.888。2)基于1.6阶微分波段优化的预测模型效果最佳,预测精度为R■=0.84,RMSE_(Pre)=2.07mS/cm,RPD=2.94,AIC=158.11。因此,对高光谱数据的适当数学变换有利于优化光谱指数更好地估算土壤电导率,进一步实现土壤盐渍化高精度动态监测。     

典型龟裂碱土土壤水分光谱特征及预测

以不同含水量的宁夏典型龟裂碱土为研究对象,系统分析了土壤光谱与土壤含水量的相关性,并建立了含水量预测模型.结果表明:随着含水量的增加,土壤光谱反射率逐渐降低,当土壤含水量高于田间持水量时,土壤光谱反射率随着含水量的增加呈增加趋势.土壤光谱反射率原始数据(r)、平滑后的反射率(R)和反射率对数(lgR)与龟裂碱土水分含量呈极显著负相关关系,整个波段R与土壤水分含量的相关系数平均比r和lgR分别高0.0013和0.0397;反射率倒数(1/R)和反射率倒数的对数[lg(1/R)]2种变换形式与龟裂碱土水分含量呈正相关关系,在950~1000 nm的相关系数平均比400~950 nm高0.2350;3种一阶微分变换形式与土壤水分的相关性不稳定.基于r、lg(1/R)、反射率的一阶微分R’和反射率对数的一阶微分(lgR)’采用不同回归模式建立的龟裂碱土含水量预测模型平均决定系数分别为0.7610、0.8184、0.8524和0.8255,其中R’的幂函数模式决定系数高达0.9447,该模型预测的土壤含水量与室内实测值拟合度为0.8279,说明该模型预测精度最高,采用r建立的模型预测精度最低.研究结果可为龟裂碱土含水量预测和当地农田灌溉提供科学依据.     

基于减量精细采样法估算小麦叶片氮积累量的最佳归一化光谱指数

基于6个小麦品种、5个施氮水平、4年田间试验条件下不同生育时期的小麦叶片高光谱反射率和相应的氮含量及生物量,采用减量精细采样法,系统构建了350～2500 nm范围内所有两两波段组成的归一化光谱指数［NDSI（i, j）］,综合分析了小麦叶片氮积累量(LNA, g N·m^-2)与NDSI（i, j）的定量关系,确定了估算叶片氮积累量的新高光谱特征波段和光谱指数,进而建立了小麦叶片氮积累量监测模型.结果表明：估算小麦叶片氮积累量的敏感波段主要存在于可见光区和近红外区,最佳特征波段组合为720 nm和860 nm;基于NDSI(860,720)的叶片氮积累量监测模型为LNA=26.34×［NDSI(860,720)］^1.887（R²=0.900,SE=1.327）.利用独立试验资料的检验结果表明,基于NDSI(860,720)建立的回归模型对小麦叶片氮积累量的估测精度为0.823,RMSE为0.991 g N·m^-2,模型预测值与观察值之间的符合度较高.可利用新的归一化高光谱参数NDSI(860,720)来估算小麦叶片氮积累量.     

不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系

研究了不同土壤水氮条件下水稻 (Oryzasativa) 冠层光谱反射特征和植株水分状况的量化关系。结果表明, 水稻冠层近红外光谱反射率随土壤含水量的降低而降低, 短波红外光谱反射率随土壤含水量的降低而升高。相同土壤水分条件下, 高氮水稻的冠层含水率高于低氮水稻的冠层含水率 ;同一水分条件下, 高氮处理的可见光区和短波红外波段光谱反射率低于低氮处理, 近红外波段光谱反射率高于低氮处理。发现拔节后比值植被指数 (R810 /R460 ) 与水稻叶片含水率和植株含水率呈极显著的线性相关, 模型的检验误差 (RootmeansquareError, RMSE) 分别为 0.93和 1.5 0。表明比值植被指数R810 /R460 可以较好地监测不同生育期水稻叶片和植株含水率。     

辽西不同针叶被害率的油松冠层光谱特征

通过对辽宁西部大面积油松冠层反射光谱的测定,分析了不同针叶被害率的油松冠层光谱反射率的差异.结果表明:在可见光波段,健康植被和不同针叶被害率的油松冠层光谱均符合绿色植物的光谱特征,但针叶被害率大于60％的油松冠层的红谷不十分明显;在近红外波段,随着针叶被害率的减少,780～1350 nm波段范围的光谱反射率增大,1450～1800和1950 ～2350 nm波段范围的光谱反射率下降.随着针叶被害率的增加,红边拐点波长位置向短波方向移动,即出现“蓝移”现象.不同针叶被害率与红边特征参数和多种植被指数均具有显著或极显著的相关关系,其中,以DVI(1470,860)为参数所建模型能更好地监测油松冠层针叶被害率.     

基于多光谱遥感影像的表层土壤有机质空间格局反演

利用多光谱LandSat TM遥感影像反演辽宁省阜新镇表层土壤有机质的空间格局,筛选出与土壤有机质分布相关的TM波段,分析并确定表层土壤有机质含量与TM1、TM2、TM3、TM4、TM5、TM6、TM7波段亮度值(digital number,DN)的相关关系,建立了土壤有机质含量的光谱预测模型.结果表明:研究区表层土壤有机质含量与TM4、TM5波段DN值呈极显著的负相关关系(r分别为-0.617和-0.623,P0.001),与TM3、TM5波段DN值之间满足负二次多项式回归关系(R2=0.9134,P0.001);基于TM3、TM5波段DN值的回归模型对研究区表层土壤有机质含量的预测结果可靠(R2=0.9151,P0.001).研究区表层土壤有机质含量10g·kg-1的农田主要分布在山地边缘地带,而平坦地区农田表层土壤有机质含量一般10g·kg-1,部分达到15～20g·kg-1.     

基于高光谱的苹果花磷素含量监测模型

在室内条件下,利用ASD FieldSpec 3地物光谱仪,测定了盛花期苹果花的高光谱反射率,在分析苹果花原始光谱反射率及其一阶导数特征的基础上,分析了高光谱反射率与磷素含量间的相关关系,确定其敏感波段,构建了特征光谱参数,并建立了苹果花磷素含量的监测模型.结果表明：苹果花磷素含量与350～370 nm、670～1385 nm、1620～1760 nm波段的原始光谱反射率以及波段500～520 nm光谱反射率的一阶导数呈极显著正相关,与波段670～730 nm一阶导数呈极显著负相关;光谱参数DVI(936,676)、DVI(977,676)、NDVI(936,676)、NDVI(977,676)与苹果花磷素含量的相关性较好,其相关系数均达到0.77以上,由此建立了以4种光谱参数为自变量的磷素含量监测模型,其中,以NDVI(936,676)为自变量构建的监测模型具有最大的决定系数（R²=0.9385）、最小的均方根误差（RMSE=0.6883）和最小的相对误差（RE=7.6%）,预测精度达到92.4%,为最佳监测模型.     

闽江河口湿地土壤全磷高光谱遥感估算

磷是湿地生态系统必需和限制性元素,利用高光谱遥感数据对其进行估算对实现湿地土壤磷素快速和准确定量具有重要意义。选取闽江河口湿地作为研究区,于2013年5月,采集16个土壤剖面80个样本作为估算与验证模型样本;基于光谱指数建立土壤全磷(TP)含量估算模型,其中光谱指数包括原始光谱反射率(R)、比值土壤指数(RSI)、归一化土壤指数(NDSI)和有机质诊断指数(OII)。此外进一步分析反射光谱与不同形态磷,TP与有机质之间关系,以期初步揭示河口湿地土壤TP估算的机理。研究结果表明,闽江河口湿地土壤TP含量与R相关系数较高的区域分布在360-560 nm,并在406 nm处达到最大值-0.816;光谱指数RSI(R_(430),R_(830))、RSI(R_(460),R_(810))、RSI(R_(560),R_(580))、NDSI(R_(430),R_(830))、NDSI(R_(460),R_(830))、NDSI(R_(560),R_(580))和OII(R_(446))与土壤TP含量均有较高的相关系数,能较好的用于TP含量的估算;各估算模型决定系数(r~2)和均方根误差(RMSE)分别在0.657-0.805和0.052-0.067之间;验证模型r~2和RMSE分别在0.606-0.893和0.037-0.044之间。分潮滩建立TP含量估算模型是可行的,并且能提高部分光谱指数的估算精度。土壤TP含量的估算精度与磷素的组成有关,其中与铁吸附态磷关系较为密切,钙吸附态和铝吸附态磷关系较弱。土壤TP与有机质和氧化还原环境的存在密切关系可能是湿地土壤TP含量估算的重要机理。     

土壤盐分对棉花功能叶气体交换参数和叶绿素荧光参数日变化的影响

2007—2008年在南京农业大学牌楼试验站进行盆栽试验,选择耐盐品种中棉所44和盐敏感品种苏棉12号为材料,试验设置5个土壤盐分水平(0、0.35%、0.60%、0.85%和1.00%),研究土壤盐分对棉花功能叶气体交换参数和叶绿素荧光参数日变化的影响.结果表明:随土壤盐分水平的升高,棉花功能叶中Na+、C l-和Mg2+含量升高,K+和Ca2+含量降低.低于0.35%盐分处理对叶片气体交换参数和叶绿素荧光参数的影响较小,高于0.35%的盐分处理显著降低了棉花功能叶的净光合速率(Pn),提高了棉花功能叶对日间光辐射强度和温度的敏感程度,导致光温抑制现象加重,并改变了Pn和气孔导度(Gs)的日变化趋势,使其由单峰曲线逐渐变为持续下降趋势.随日间光辐射强度和温度的变化,棉花叶片最大光化学效率(Fv/Fm)、光系统Ⅱ(PSⅡ)量子产量(ΦPSⅡ)和光化学猝灭系数(qP)的日变化趋势呈V型曲线,最低值出现在12:00—13:00,非光化学猝灭系数(qN)的日变化趋势呈单峰曲线;盐分处理降低了棉花功能叶Fv/Fm、ΦPSⅡ和qP,提高了qN,且增大了其变化幅度.耐盐品种中棉所44功能叶片中较低的Na+、C l-含量及...     

Spectral response of cotton aphid- (Homoptera: Aphididae) and spider mite- (Acari: Tetranychidae) infested cotton: controlled studies

Remote sensing is a precision tool that can detect plant health. Ground-based methods in small-scale experiments were used to explore the applicability of this technology for detection of arthropod-damaged cotton and to find useful indices or wavelengths for detecting arthropod-damaged cotton. Individual leaves of greenhouse-grown cotton plants and cotton plants in the field were infested with populations of cotton aphids, spider mites, and aphids + mites. Several sets of reflectance measurements were collected from the adaxial surface of the leaves at various intervals after infestation using a portable hyperspectral spectrometer with an integrating sphere or a contact probe. Vegetation indices were calculated from the reflectance values; these indices and the raw reflectance values, represented by narrow wavelength bands, were tested to see if arthropod damaged cotton could be distinguished from healthy cotton. Results indicated that it was possible to detect cotton aphid- and spider mite-damaged leaves by tracking the spectral changes in the leaf, although the damage type of each arthropod could not be distinguished spectrally. In addition, spider mite- and aphid-infested cotton leaves increased reflectance in the near infrared wavelength at approximately 850 nm in comparison to uninfested leaves.     

Senescence and hyperspectral reflectance of cotton leaves exposed to ultraviolet-B radiation and carbon dioxide

The objectives of this study were to determine the effects of UV-B radiation and atmospheric carbon dioxide concentrations ([CO(2)]) on leaf senescence of cotton by measuring leaf photosynthesis and chlorophyll content and to identify changes in leaf hyperspectral reflectance occurring due to senescence and UV-B radiation. Plants were grown in controlled-environment growth chambers at two [CO(2)] (360 and 720 micro mol mol(-1)) and three levels of UV-B radiation (0, 7.7 and 15.1 kJ m(-2) day(-1)). Photosynthesis, chlorophyll, carotenoids and phenolic compounds along with leaf hyperspectral reflectance were measured on three leaves aged 12, 21 and 30 days in each of the treatments. No interaction was detected between [CO(2)] and UV-B for any of the measured parameters. Significant interactions were observed between UV-B and leaf age for photosynthesis and stomatal conductance. Elevated [CO(2)] enhanced leaf photosynthesis by 32%. On exposure to 0, 7.7 and 15.1 kJ of UV-B, the photosynthetic rates of 30-day-old leaves compared with 12-day-old leaves were reduced by 52, 76 and 86%, respectively. Chlorophyll pigments were not affected by leaf age at UV-B radiation of 0 and 7.7 kJ, but UV-B of 15.1 kJ reduced the chlorophylls by 20, 60 and 80% in 12, 21 and 30-day-old leaves, respectively. The hyperspectral reflectance between 726 and 1142 nm showed interaction for UV-B radiation and leaf age. In cotton, leaf photosynthesis can be used as an indicator of leaf senescence, as it is more sensitive than photosynthetic pigments on exposure to UV-B radiation. This study revealed that, cotton leaves senesced early on exposure to UV-B radiation as indicated by leaf photosynthesis, and leaf hyperspectral reflectance can be used to detect changes caused by UV-B and leaf ageing.     

桉树叶片光合色素含量高光谱估算模型

色素在植物的生理生态过程中非常重要,利用高光谱数据,揭示光谱反射率上特征波段与光合色素含量间的关系将有助于理解光合色素光谱反射特征的规律,同时为利用高光谱遥感技术快速无损监测植物叶片光合色素提供了技术支持.利用野外采集的桉树叶片样本,在实验室内测定了叶片的高光谱反射率及对应的叶绿素、类胡萝卜素含量.利用光谱分析技术和统计学方法对光谱数据进行处理分析,提取了光谱特征参量,并建立叶绿素、类胡萝卜素含量与光谱特征参量间的估算模型.通过精度检验,研究结果表明以(SDr-SDb)/(SDr+SDb)为变量建立的指数模型估算效果最佳.     

Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data.     

棉花冠层高光谱参数与叶片氮含量的定量关系

建立棉花(Gossypium hirsutum)氮素状况的光谱监测技术对于棉花营养诊断和长势估测具有重要意义。该研究利用冠层高光谱反射率及演变的多种高光谱参数,分析了不同施氮水平下不同棉花品种叶片氮含量与冠层反射光谱的定量关系,建立了棉花叶片氮含量的敏感光谱参数及预测方程。结果显示,棉花叶片氮含量和冠层高光谱反射率随不同施氮水平呈显著变化。棉花叶片氮含量的敏感光谱波段为600~700 nm的可见光波段和750~900 nm的近红外波段,且叶片氮含量与比值植被指数RVI [average (760~850), 700]有密切的定量关系,4个品种的平均决定系数在0.70左右。进一步分析表明,可以用统一的回归方程来描述不同品种、不同生育时期和不同氮素水平下棉花叶片氮含量随反射光谱参数的变化模式,从而为棉花氮素营养的监测诊断与精确施肥提供技术依据。     

Distinguishing natural from anthropogenic stress in plants: physiology, fluorescence and hyperspectral reflectance

Background and Aims

Explosives released into the environment from munitions production, processing facilities, or buried unexploded ordnances can be absorbed by surrounding roots and induce toxic effects in leaves and stems. Research into the mechanisms with which explosives disrupt physiological processes could provide methods for discrimination of anthropogenic and natural stresses. Our objectives were to experimentally evaluate the effects of natural stress and explosives on plant physiology and to link differences among treatments to changes in hyperspectral reflectance for possible remote detection.

Methods

Photosynthesis, water relations, chlorophyll fluorescence, and hyperspectral reflectance were measured following four experimental treatments (drought, salinity, trinitrotoluene and hexahydro-1,3,5-trinitro-l,3,5-triazine) on two woody species. Principal Components Analyses of physiological and hyperspectral results were used to evaluate the differences among treatments.

Results

Explosives induced different physiological responses compared to natural stress responses. Stomatal regulation over photosynthesis occurred due to natural stress, influencing energy dissipation pathways of excess light. Photosynthetic declines in explosives were likely the result of metabolic dysfunction. Select hyperspectral indices could discriminate natural stressors from explosives using changes in the red and near-infrared spectral region.

Conclusions

These results show the possibility of using variations in energy dissipation and hyperspectral reflectance to detect plants exposed to explosives in a laboratory setting and are promising for field application using plants as phytosensors to detect explosives contamination in soil.