Retrieval of Winter Wheat Canopy Carotenoid Content with Ground-and Airborne-Based Hyperspectral Data

Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the overlap of absorption peaks of carotenoid(C_(x+c))and chlorophyll(C_(a+b)),accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging.The objective of present study was to assess CC_(x+c)C in winter wheat(Triticum aestivum L.)with ground-and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum.In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.At the canopy level,a remarkable linear relationship(R^(2)=0.95,p<0.001)existed between C_(x+c) and Ca+b,and correlation between CC_(x+c)C and wavelengths within 400 to 1000 nm range indicated that CC_(x+c)C could be estimated using reflectance ranging from visible to near-infrared wavebands.Results of Cx+c assessment based on chlorophyll and carotenoid indices showed that red edge chlorophyll index(CI red edge)performed with the highest accuracy(R^(2)=0.77,RMSE=22.27μg/cm^(2),MAE=4.97μg/cm^(2)).Applying partial least square regression(PLSR)in CC_(x+c)C retrieval emphasized the significance of reflectance within 700 to 750 nm range in CC_(x+c)C assessment.Based on CI red edge index,use of airborne hyperspectral imagery achieved satisfactory results in mapping the spatial distribution of CC_(x+c)C.This study demonstrates that it is feasible to accurately assess CC_(x+c)C in winter wheat with red edge chlorophyll index provided that C_(x+c) correlated well with C_(a+b) at the canopy scale.it is therefore a promising method for CC_(x+c)C retrieval at regional scale from aerial hyperspectral imagery.

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.

Assessing Reclamation Levels of Coastal Saline Lands with Integrated Stepwise Discriminant Analysis and Laboratory Hyperspectral Data

At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content. These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488, 530, 670, 880, 1400, and 1900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%. Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.

基于人工神经网络的透射水体亚硝酸盐含量模拟估计

亚硝酸盐是水体的重要测试指标,对水体质量的评估有着重要意义。采用透射高光谱结合人工神经网络(ANN)建立水体亚硝酸盐含量估算模型。首先采用试剂配制10种浓度的亚硝酸氮标准溶液(0.02、0.04、0.06、0.08、0.10、0.12、0.14、0.16、0.18和0.20 mg/L),并使用OCEAN-HDX-XR微型光纤光谱仪扫描10次各浓度亚硝酸盐溶液在181.1~1023.1 nm的透射光谱,取平均值作为各浓度亚硝酸盐溶液原始透射光谱,分别使用最大最小均一化(MMN)、标准正态变化(SNV)、多元散射校正(MSC)、以及二阶差分(SOD)四种光谱预处理方法,并结合ANN方法建立水体亚硝酸盐含量估算模型,通过比较模型的精度来选择最优的模型进行水体亚硝酸盐含量的估计。结果显示,基于二阶差分预处理下的BP-ANN神经网络预测模型中的均方根误差RMSE为0.032367,平均绝对误差MAE为0.016895,决定系数R^(2)为0.987403,与二次有理高斯过程回归(QR-GPR)和二次支持向量机(Q-SVM)预测模型相比,该模型的拟合效果更好,精确度更高。提出了反向传播人工神经网络(BP-ANN)高光谱水质亚硝酸盐参数的反演方法,为水质亚硝酸盐参数动态检测提供了新方法。

基于高光谱技术的滇池流域湿地植物氮含量估算模型构建

以滇池流域为研究区,利用S-R3500光谱仪采集芦苇、菰、香蒲和风车草4种典型滇池湿地植物的光谱数据,应用A33型连续流动分析仪测定湿地植物叶片氮含量,筛选出氮含量最敏感的光谱特征波段和变换形式参量,建立湿地植物叶片高光谱氮含量估算模型。结果表明:4种湿地植物的光谱曲线与健康绿色植被的光谱曲线趋势一致,但在800~1350 nm和1650~1800 nm波段存在差异,一阶微分、二阶微分和“三边”参数光谱变换可以更加突出4种湿地植物的光谱特征;芦苇二阶微分1836 nm、香蒲二阶微分881 nm、菰一阶微分1264 nm、风车草二阶微分1665 nm波长和氮含量相关系数绝对值最大;菰的“三边”参数红边面积和蓝边面积的比值、红边面积和蓝边面积的归一化值,香蒲的红边幅值、红边面积、“绿峰”反射率和“红谷”反射率比值、红边面积和蓝边面积比值和氮含量相关系数绝对值最大,且呈极显著相关(P<0.01);4种湿地植物基于光谱二阶微分构建的氮含量估算模型精度比一阶微分高,基于光谱“三边”参数构建的氮含量的估算模型精度最高且误差最小;4种湿地植物基于“三边”参数构建的氮含量估算多元逐步回归模型估测效果较好。基于高光谱技术的湿地植物养分含量估算模型技术可以长时序快速地监测大面积湿地植被养分含量的时空动态变化,为湿地植物远程管理提供基础数据和技术支持。

不同抗感水稻品种对褐飞虱胁迫的高光谱响应特征

【目的】探究不同抗感水稻受褐飞虱胁迫的高光谱反射率曲线变化及敏感光谱差异,研究水稻植株不同部位的高光谱变化。在此基础上,采用机器学习技术建立水稻褐飞虱抗性鉴定模型,为下一步开发智能化褐飞虱抗性鉴定技术提供重要基础资料。【方法】以三个具不同褐飞虱抗感特征的水稻品种(TN1、Mudgo、RHT)为对象,分析其光谱、植被指数差异并建立抗性级别预测的随机森林模型。【结果】研究发现,褐飞虱胁迫天数与光谱反射率显著相关的波段数及差异显著的波段数随着水稻的抗性水平的上升而减少。同时,在680 nm左右,三个品种的光谱反射率与褐飞虫胁迫时间的相关性最好。植被指数分析表明,SIPI、SR605/760和PSNDb与抗性级别的相关系数绝对值要高于680 nm的结果。感虫品种TN1差异最早体现,中抗品种Mudgo其次,高抗品种RHT最后。不同部位的差异首先出现在第1叶叶片和第1叶叶鞘,然后依次为第2叶叶片和第2叶叶鞘以及第3叶叶片和第3叶叶鞘。预测模型的结果表明,全波段作为输入的模型效果比以单一植被指数SIPI构建的随机森林模型更好,模型准确率达到85.9%。【结论】本研究反映了不同抗感水稻品种受褐飞虱危害后的高光谱变化规律与不同抗感品种和不同部位的差异,并证实了机器学习技术对水稻抗性级别的分类能力。

基于高光谱成像技术的棉花杂质检测

棉花杂质含量是棉花定级和定价的主要指标之一,实现棉花杂质的快速、无损检测具有重要的现实意义和应用价值。对原棉进行除杂,并分离得到纯棉、棉枝叶、破籽和泥石4类物质,在高光谱成像系统中采集这4类物质高光谱图像,提取并分析他们的高光谱数据及特征,采用多元散射校正、标准正态变量变换和一阶导数作为数据预处理方法,选用主成分分析进行数据降维及可区分性研究,结合主成分个数的优选建立判别分析模型,建立了原棉杂质检测模型,并对比分析所建立的不同检测模型的分类效果。结果表明:多元散射校正、标准正态变量变换和一阶导数3种方法均能有效消除光谱曲线的基线漂移现象,使原有特征峰更加明显;检测模型的主成分个数优选为8;对应原始光谱、多元散射校正、标准正态变量变换和一阶导数预处理的判别分析模型测试集准确率分别为100%、90%、93%和100%,证实了利用高光谱成像技术能够对原棉杂质进行有效检测识别。

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

空地协同模式在城市水环境监测中的应用研究

传统水质监测方法耗时且费力,而基于高光谱的水质监测技术可实现对水质的快速、直观、原位监测。以扬州古运河三湾段为研究区域,基于无人机高光谱成像仪与水质走航监测船相配合,采用空地协同模式和偏最小二乘回归算法对总磷(TP)、总氮(TN)、氨氮(NH_(3)-N)、高锰酸盐指数(IMn)4个水质参数进行定量反演研究。结果表明:该反演模型的决定系数(R^(2))为91%~97%;拟合效果依次为NH_(3)-N>I_(Mn)>TN>TP;各指标反演误差绝对值为0.2%~4%。该方法具有较好的反演效果,可快速、准确地获取城市河道水质分布情况,适用于城市水环境监测。

基于高光谱数据的淤泥质岸滩信息提取

淤泥质岸滩作为沿海关键生态系统,在维持生态平衡、防止海岸侵蚀、提供重要经济资源等方面发挥着重要作用。自2019年发射以来,ZY1-02D卫星在海岸带生态环境监测领域展现出广阔的应用前景,该卫星搭载的AHSI传感器能够获取高分辨率的光谱图像,为海岸带生态环境的遥感监测提供强有力的技术支持。文章利用2023年7月2日获取的河北沧州沿海区域的ZY1-02D AHSI卫星数据,结合六类样本数据,借助随机森林分类算法,构建了基于ZY1-02D AHSI卫星数据的淤泥质岸滩遥感提取分类模型,并利用该模型对沧州沿海地区的淤泥质岸滩分布进行了信息提取。研究结果显示,利用该分类模型提取的沧州沿海淤泥质岸滩的总体分类精度为87.5%,Kappa系数为88.16%。该研究成果可为分析沧州沿海淤泥质岸滩的分布演变规律,以及进一步治理沿海生态环境提供技术指引。

Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra

Fast identification of pesticide residue level in lettuce leaves plays a key role in the test of food safety.In order to identify the different concentrations pesticide residues of lettuce leaves in a fast and nondestructive way,the hyperspectra coupled with chlorophyll fluorescence spectra was used in this research.Transmission electron microscopy(TEM)was used to identify the microstructure changes of lettuce leaves under different concentrations of dimethoate residue.Besides,a method involving wavelet transform and MD-MCCV algorithm(WT-MD-MCCV)was developed for identifying the optimal wavelengths of the spectral data.The hyperspectra and chlorophyll fluorescence spectra data of 150 lettuce leaf samples at five different concentrations of pesticide residues were obtained using hyperspectral data acquisition device and Cary Eclipse Fluorescence Spectrophotometer.The combination of Savitzky-Golay(SG)algorithm and SNV algorithm(SG-SNV)preprocessing algorithms was used to preprocess the raw spectra.In addition,Principal Component Analysis(PCA),Successive Projections Algorithm(SPA)and wavelet transform coupled to MD-MCCV algorithm(WT-MD-MCCV)were applied to identify the optimal wavelengths of raw spectra including hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled with chlorophyll fluorescence spectra data.Support vector regression(SVR)was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths coupled with different spectral data.The results showed that with the increase of the concentration of dimethoate pesticide spraying,lettuce chloroplast number of osmiophilic particles increased and the starch granules decreased.Besides,the intercellular space of lettuce leaves increased gradually,with the increase of dimethoate concentration.Different concentrations of pesticide residues of lettuce in the near infrared and fluorescence spectrum have a certain difference.In addition,the related parameters of the three preferably prediction models were Rp 2=0.956 and RMSEP=0.018,Rp 2=0.937 and RMSEP=0.161,Rp 2=0.987 and RMSEP=0.005,respectively,using WT-MD-MCCV algorithm combined with hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled to chlorophyll fluorescence spectra data.WT-MD-MCCV algorithm combined with hyperspectra and chlorophyll fluorescence spectra data performed best among the nine SVR models and the hyperspectra coupled with chlorophyll fluorescence spectra can be used to identify the pesticide residue level in lettuce leaves.

基于高光谱遥感图像识别的输电线路巡检机器人路径自动化控制系统

输电线路巡检机器人路径自动控制的合理性,是保障巡检机器人能够自主完成巡检任务,提高巡检效率的关键。该文设计基于高光谱遥感图像识别的输电线路巡检机器人路径自动化控制系统。聚类输电线路高光谱遥感图像的像元,识别高光谱遥感图像中的输电线路。依据输电线路识别结果,利用双蚁群算法确定最佳巡检路径。通过模糊PID控制器控制巡检机器人的伺服电机,沿着最优巡检路径移动,实现巡检机器人的路径自动化控制。系统测试结果表明,所设计系统能够依据高光谱遥感图像的输电线路识别结果,规划巡检机器人的巡检路径,巡检路径拐点较少。

基于多源环境变量的渭-库绿洲土壤颗粒含量预测研究

本文以渭干河–库车河绿洲(简称渭–库绿洲)土壤颗粒为研究对象,采集了绿洲内50个典型表层(0~10 cm)土壤样本,通过相关软件,提取到遥感指数变量、地形和气候等环境变量,经过相关性分析确定环境变量和预测目标间的关系,使用R语言构建了预测土壤颗粒含量的随机森林(random forest,RF)模型和极端梯度提升(extreme gradient boosting,XGBoost)模型。研究结果表明:XGBoost模型的预测结果整体好于RF模型,其中相关系数介于0.39~0.78;土壤pH、高程及衍生变量、光谱变换变量均是两个模型预测土壤颗粒含量的重要因子;将模型预测结果、实测数据和世界土壤数据库(HWSD)中的3种土壤颗粒数据作对比分析,结果表现出模型预测数据的误差小于HWSD与实测数据的误差。综上所述,通过筛选环境变量建立的XGBoost模型,是预测渭–库绿洲土壤颗粒含量的有效方法。

基于高光谱的长江中下游沿江地区土壤化学元素含量反演研究

为探究高光谱遥感技术在土壤元素快速定量化检测中的应用,解决传统土壤元素含量测定效率低下的问题,本文以长江中下游沿江芜湖段为典型研究区,实地采集615个土壤表层样品(0~15cm),经实验室化学成分测定和光谱采集,利用多种数据预处理方法和回归建模方法,建立铬(Cr)、镉(Cd)、钾(K)、磷(P)、氮(N)、有机质含量的高光谱估算模型。结果表明:经对原始光谱进行预处理以及多种光谱数学变换,元素含量与光谱的相关性均有不同程度的提高,不同元素具有差异性,总体建模结果均大于通过原始光谱直接建模效果。各元素的预测模型精度均大于86%,模型的预测效果较好,可为土壤元素快速定量化检测提供技术参考。

一种基于空间连续性的高光谱图像分类方法

利用地物在空间上分布连续性这一信息 ,提出了一种高光谱图像先分块、再分类的分类方法 ,改进了通常意义下的分类只考虑光谱信息这一缺陷 ,通过对 2 0 0 0年我们在日本长野南牧村 ,采用上海技术物理研究所研制的推扫式成像光谱仪 (PHI)获得了 80个波段的高光谱数据进行实验 。

基于高光谱图像特征提取与凸面几何体投影变换的目标探测

在介绍凸面几何体概念和高光谱图像光谱线性模型的基础上,探讨了凸面几何体和高光谱图像的关系.提出了在光谱特性提取的基础上利用凸面几何体投影变换进行高光谱图像目标探测的方法,并成功地应用于亚运村建材市场屋顶板材和亚运村中心地区真假草坪的自动识别和探测中.试验证明该方法不需要探测目标的任何先验知识就能达到比较好的目标探测效果.同时试验还表明,由于高光谱图像波谱范围宽,特别是有短波红外、中红外和热红外范围的图像,不仅增加了地物的可分性,而且为探测地物物理性质提供了可能.

基于反射光谱特征的土壤盐碱化评价

以吉林省西部盐碱土及其光谱反射率为研究对象，采用一阶微分、去包络线方法建立各种土壤光谱指数，分析并确定反映盐碱化程度的最佳波段与土壤光谱指数，建立盐碱化土壤参数的高光谱定量模型用于评价土壤盐碱化程度，结果表明：盐碱土在400～2500nm的范围内主要有五个明显的吸收谷，碱斑土壤由于富含盐分在五个吸收谷范围的吸收特征均显著强于其他土壤；不同地域土壤属性间相关关系存在共性，pH值与电导率正相关，二者都与有机质负相关；微分、去包络线方法得到的光谱指数与土壤参数的相关系数显著优于反射率，去包络线方法得到的土壤光谱指数普适性更强；基于土壤光谱指数的高光谱模型可以准确预测盐碱土pH值，可以用于土壤的盐碱化程度评价。

基于高光谱图像主成分分量的小目标检测算法研究

提出了一种基于图像主成分分量的高光谱小目标检测算法 .作为一种多元数据集合 ,通常高光谱数据形成的几何体是一个超平面 .主成分分析能有效估计这一几何体的本征维数 .显著特征值对应的主成分体现了几何体大部分信息 ;而不显著特征值对应的主成分则代表了正交于几何体的信息 ,而这些信息中则包含了重要的内容 ,例如目标特性 .文中提出的方法就是利用这些不显著的主成分分量来进行小目标检测 .该方法减少了对先验光谱信息的依赖 。

利用Hyperion星载高光谱传感器监测太湖水质的研究

通过分析Hyperion星载高光谱传感器的数据特征和水质参数监测精度,确定并详细讨论了波段比值、差值和NDVI算法与叶绿素、悬浮物浓度的相关性差异和敏感波段分布,建立并验证了水质参数高光谱遥感反演模型,初步评价了太湖水体富营养化状况.利用模拟环境小卫星的波段设置和反射率光谱,对比分析了Hyperion和环境小卫星高光谱监测太湖水质参数的反演精度,展示了星载高光谱监测内陆水体水质的应用潜力,有力地促进了我国环境小卫星高光谱数据在水体生态遥感监测中的预研工作.

基于高光谱的水稻叶片含水量监测研究

【目的】建立快速、无损诊断水稻叶片含水量的估测模型,为水稻水分精确管理提供依据。【方法】基于2年不同土壤水分处理和水稻品种的池栽试验,于水稻主要生育时期同步测定顶部4张叶片的光谱反射率和含水量,系统分析350—2 500 nm波段范围内任意两波段组合而成的比值(RSI)、归一化差值(NDSI)及差值(DSI)光谱指数,并分析其与叶片含水量的量化关系。【结果】不同土壤水分处理和叶位间,叶片反射光谱具有显著的时空变化特征,叶片含水量的敏感光谱波段主要位于近红外及短波红外区域;RSI(R1402,R2272)及NDSI(R1402,R2272)光谱指数与叶片含水量呈现良好的线性相关,线性拟合R2均达到0.80。基于独立试验资料对所建模型进行测试检验也显示,预测值和观察值的拟合R2也均达到0.86。【结论】RSI(R1402,R2272)、NDSI(R1402,R2272)均可用于水稻叶片含水量的定量监测。