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1.
WANG Jinlin 《干旱区科学》2021,13(12):1287-1298
Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines. However, the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies. The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area. Specifically, we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements (Dataset I) and field measurements (Dataset II). In situ field measurements were acquired to verify the laboratory measurements. Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples. In addition, we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples. The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces. It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content. Based on sample data analysis, the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content, with R2 values of 0.45 and 0.40 corresponding to datasets I and II, respectively. Hence, the modified normalized difference index (the wavelengths of 2220, 2290, and 2370 nm) identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas. The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field. 相似文献
2.
基于野外实测高光谱数据的干旱区耕作土壤含水量反演研究 总被引：6，自引：0，他引：6
利用2011年3月野外实地采集的不同含水量土壤的高光谱数据,研究了南疆地区耕作土壤草甸土含水量与高光谱反射率之间的定量关系,构建了一元线性回归与多元逐步回归的土壤含水量预测模型.结果表明,土壤含水量在380～ 1080 nm波段与反射率呈负相关关系;反射率经倒数(1/R)、对数(logR)、一阶微分(R’)变换后可提高其与含水量的相关性;以50个建模样本所建立模型的相关系数均达到极显著水平,所有模型通过对37验证样本进行预测,比较决定系数、均方根误差、相对误差后,表明多元逐步回归模型的预测能力要优于一元线性回归模型,从所有模型中优选出以698、702、703、746、747 nm波段反射率倒数(1/R)建立的多元逐步回归模型为最优模型,该模型实测值与预测值之间的R2为0.9199,RMSE为1.6026,RE为0.6517,可用于基于野外高光谱数据的土壤含水量的估测. 相似文献
3.
Soil salinization is a serious ecological and environmental problem because it adversely affects sustainable development worldwide, especially in arid and semi-arid regions. It is crucial and urgent that advanced technologies are used to efficiently and accurately assess the status of salinization processes. Case studies to determine the relations between particular types of salinization and their spectral reflectances are essential because of the distinctive characteristics of the reflectance spectra of particular salts. During April 2015 we collected surface soil samples(0–10 cm depth) at 64 field sites in the downstream area of Minqin Oasis in Northwest China, an area that is undergoing serious salinization. We developed a linear model for determination of salt content in soil from hyperspectral data as follows. First, we undertook chemical analysis of the soil samples to determine their soluble salt contents. We then measured the reflectance spectra of the soil samples, which we post-processed using a continuum-removed reflectance algorithm to enhance the absorption features and better discriminate subtle differences in spectral features. We applied a normalized difference salinity index to the continuum-removed hyperspectral data to obtain all possible waveband pairs. Correlation of the indices obtained for all of the waveband pairs with the wavebands corresponding to measured soil salinities showed that two wavebands centred at wavelengths of 1358 and 2382 nm had the highest sensitivity to salinity. We then applied the linear regression modelling to the data from half of the soil samples to develop a soil salinity index for the relationships between wavebands and laboratory measured soluble salt content. We used the hyperspectral data from the remaining samples to validate the model. The salt content in soil from Minqin Oasis were well produced by the model. Our results indicate that wavelengths at 1358 and 2382 nm are the optimal wavebands for monitoring the concentrations of chlorine and sulphate compounds, the predominant salts at Minqin Oasis. Our modelling provides a reference for future case studies on the use of hyperspectral data for predictive quantitative estimation of salt content in soils in arid regions. Further research is warranted on the application of this method to remotely sensed hyperspectral data to investigate its potential use for large-scale mapping of the extent and severity of soil salinity. 相似文献
4.
基于HJ1A-HSI反演松嫩平原土壤盐分含量 总被引：1，自引：0，他引：1
以HSI高光谱遥感影像为数据源,利用地理信息系统和偏最小二乘回归（PLSR）分析方法,结合实地采样的盐分离子、EC值、pH的化验数据,分析盐分离子在HSI数据中的光谱特征,建立土壤盐分与高光谱数据的偏最小二乘回归模型,实现对松嫩平原土壤中主要盐分参数的反演实验。结果表明：偏最小二乘回归分析方法在保证信息量最大的前提下,降低了光谱数据维数,提高了分析的效率。利用偏最小二乘回归建立的预测模型,对全盐、[WTBX]EC[WTBZ]值、Na++K+、Cl-、HCO-3有较好的反演精度,模型的判定系数分别为0.799、0.879、0.772、0.791和0.694。在土壤含盐量的定量反演方面,探索了使用HSI影像作为新的数据源,为松嫩平原土壤盐分含量的精确、定量、快速获取及盐碱化防治等提供参考。 相似文献
5.
基于高光谱数据的土壤有机质反演是土壤遥感及精准农业的重要研究内容,然而不同的光谱处理及建模方法使得模型的估算能力及精度差异明显,限制了模型之间的通用性。为了构建陕西省土壤有机质含量估算的最优模型,以陕西省9种主要土壤类型的216个土样的光谱反射曲线和土壤有机质含量为数据基础,将光谱反射曲线进行一阶微分d(R)、倒数对数log(1/R)、倒数对数一阶微分d[log(1/R)]和包络线去除N(R)4种变换,结合一元线性回归(SLR)、偏最小二乘回归(PLSR)和支持向量机回归(SVR)3种建模方法构建了不同的土壤有机质含量估算模型。结果显示:不同类型土壤的反射光谱曲线总体态势基本一致,吸收特征位置基本相同,且土壤有机质含量与光谱反射率呈负相关态势;基于d [log(1/R)]光谱变换构建的SVR估算模型精度最高,建模集和验证集的判断系数(R~2)分别为0.9210、0.8874,验证均方根误差(RMSE)为2.18,相对分析误差(RPD)达到2.8751,是估算陕西省土壤有机质含量的最优模型,PLSR次之,SLR最差。 相似文献
6.
Visible and near-infrared (vis-NIR) spectroscopy technique allows for fast and efficient determination of soil organic matter (SOM). However, a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information. Therefore, this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM. The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017. Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test. The successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths. Finally, partial least squares regression (PLSR) and random forest (RF) models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content. The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features (i.e., 1400.0, 1900.0, and 2200.0 nm), and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0-510.0 nm. Both models can achieve a more satisfactory prediction of the SOM content, and the RF model had better accuracy than the PLSR model. The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination (R2) of 0.78 and the residual prediction deviation (RPD) of 2.38. The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content. Therefore, combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land. 相似文献
7.
分析2006年栾城试验站不同氮素水平下冬小麦的多时相的群体光谱测量数据和相应叶片叶绿素密度的测量数据，发现：冬小麦的群体光谱的导数光谱数据、红边光谱数据，归一化植被指数NDVI和比值植被指数RVI与叶绿素密度具有很好的相关关系，并且选取样本建立了相应的回归方程。以回归方程作为叶绿素高光谱估算模型，并利用检验样本对估算模型进行检验，结果表明，以745nm处一阶导数光谱值、733nm处二阶导数光谱值和红边振幅为变量的模型可以较好的估算叶绿素密度。 相似文献
8.
为系统地研究干旱半干旱区植被盖度提取方法,比较了目前常用的几种高光谱影像植被盖度提取技术。结果表明:采用NDVI建立的像元二分模型对植被盖度的估测能力低于直接采用NDVI建立的回归模型;4种处理的PCR模型植被盖度估测的建模精度由高到低为:CR>NO>FD>SD;4种处理的PLSR模型植被盖度估测的建模精度由高到低为:FD>NO>SD>CR;FCLS分解的结果明显优于LS模型;综合分析后,认为基于FD的PLSR模型对研究区植被盖度估测效果最佳。文中研究旨在为干旱半干旱区植被盖度的更深入研究提供参考。 相似文献
9.
针对宁夏银北地区大面积土壤盐碱化监测的需要,利用实测植被冠层光谱与Landsat 8 OLI影像相结合进行土壤含盐量和pH值估测研究。对实测植被冠层高光谱与影像多光谱反射率进行倒数、对数、三角函数及其一阶微分等一系列变换,确定最佳光谱变换形式,筛选敏感植被指数和敏感波段,分别建立基于实测植被光谱与Landsat 8 OLI影像光谱的土壤含盐量与pH值估测模型,并基于高光谱数据模型对影像盐分和pH值模型进行校正,以提高影像估测土壤盐碱化的精度。结果表明:经倒数对数变换建立的实测植被高光谱EVI模型和经平滑后敏感波段建立的实测植被高光谱模型对土壤pH值的估测精度较高,模型决定系数分别为0.6257和0.5975;基于实测高光谱植被指数和敏感波段分别对Landsat 8 OLI影像含盐量、pH值估测模型进行校正,影像敏感植被指数和敏感波段含盐量模型决定系数分别提高了0.3207和0.3762,pH值估测模型决定系数分别提高了0.2065和0.2487。采用敏感植被指数和敏感波段同时估测土壤含盐量和pH值,实现了从实地测量高光谱向遥感多光谱尺度的转换。 相似文献
10.
利用光谱特征参数估算病害胁迫下杉木叶绿素含量 总被引：2，自引：0，他引：2
为了探索建立炭疽病胁迫下杉木叶绿素含量的高光谱估算模型,促进遥感技术在森林病虫害监测中的应用,通过获取不同发病程度的杉木冠层光谱及相应的叶绿素含量,将冠层光谱数据、一阶微分数据与相应的叶绿素含量分别进行了相关分析。采用逐步回归、主成分回归及偏最小二乘回归方法构建叶绿素含量的估算模型。叶绿素含量与原始光谱在可见光(614～698nm)和近红外区(724nm之后)达到极显著相关,且在近红外区基本趋于稳定;与一阶微分光谱在424～486nm、514～532nm、552～682nm、698～755nm和762～772nm波段全部达到极显著相关;3种建模方法均消除了参数间多重共线性的影响,模型的决定系数全部达到极显著水平,其中逐步回归模型精度最高,相对误差和均方根误差分别为10.71%和0.194。研究表明受到不同程度炭疽病胁迫的杉木冠层光谱反射差异较大,可利用高光谱信息定量估算病害胁迫下的杉木叶绿素含量,且估算精度较高。 相似文献
11.
为实现干旱区绿洲土壤含水量的快速、准确监测,利用采集自渭干河-库车河绿洲的84个表层(0~10cm)土壤样本,通过利用电磁感应仪(EM38)将所测解译后数据代替实测土壤含水量数据,将高光谱反射率重采样为Landsat8卫星遥感波段反射率,在选取光谱特征参数、提取敏感波段的基础上,利用偏最小二乘回归(PLSR)方法建立土壤含水量模型,将最优估算模型应用于遥感影像,实现研究区土壤含水量遥感反演。研究结果表明:(1)利用EM38所测水平模式土壤表观电导率与土壤含水量拟合效果最优,能够代替实测土壤含水量进行后续建模分析。(2)相比3种单一的光谱特征指数,利用多种光谱特征指数所建土壤含水量估算模型的建模效果更优,其干、湿各季建模集决定系数R~2大于0.7,均方根误差(RMSE)均小于0.5%,RPD均大于2,能够作为有效手段估算干旱区绿洲土壤含水量。(3)不同季节土壤含水量遥感反演值与实测值决定系数R~2均大于0.6,均方根误差(RMSE)均小于0.6%,显示了较高的预测精度,证明利用电磁感应技术与高光谱相结合能够实现对干旱区绿洲土壤含水量的精准、高效监测。 相似文献
12.
查干湖透明度高光谱估测模型研究 总被引：2，自引：0，他引：2
湖水透明度能直观反映湖水清澈和混浊程度,是水体能见程度的一个量度,同时也是评价湖泊富营养化,衡量水质优劣的一个重要指标。传统地表水透明度观测主要采用塞克盘(Secchi Disk)法,这种方法不仅费时费力,而且只具有局部的代表意义。遥感技术具有快速、大面积和周期性的特点,可以有效地解决这种局限性。该文通过查干湖高光谱数据,建立透明度(Secchi Disk Depth)单波段和比值高光谱估测遥感模型,并进行验证。结果表明:利用高光谱遥感监测模型对查干湖透明度进行估算和监测,能够获取较为准确的评价结果,相对于传统监测方法具有省时省力的特点。通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果好于比值模型,而对数比值模型又强于单纯的比值模型。查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义。 相似文献
13.
CUI Shichao 《干旱区科学》2021,13(11):1183-1198
With the increase of exploration depth, it is more and more difficult to find Au deposits. Due to the limitation of time and cost, traditional geological exploration methods are becoming increasingly difficult to be effectively applied. Thus, new methods and ideas are urgently needed. This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits. For this purpose, 48 plant (Seriphidium terrae-albae) and soil (aeolian gravel desert soil) samples were first collected along a sampling line that traverses an Au mineralization alteration zone (Aketasi mining region in an arid region of China) and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device (ASD) FieldSpec3 spectrometer. Then, the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated. Additionally, the characteristic bands were selected from plant spectra using four different methods, namely, genetic algorithm (GA), stepwise regression analysis (STE), competitive adaptive reweighted sampling (CARS), and correlation coefficient method (CC), and were then input into the partial least squares (PLS) method to construct a model for estimating the soil Au content. Finally, the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method. The results were compared with those of a model based on the full spectrum. The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information, and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil. The cross-validated coefficient of determination (R2) and the ratio of the performance to deviation (RPD) between the predicted value and the measured value reached the maximum of 0.8218 and 2.37, respectively, with a minimum value of 6.56 μg/kg for the root-mean-squared error (RMSE) in the full spectrum model. However, in the process of modeling, it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method. Compared with the GA, STE, and CC methods, CARS was the superior characteristic band screening method based on the accuracy and complexity of the model. When modeling with characteristic bands, the highest accuracy, R2 of 0.8016, RMSE of 7.07 μg/kg, and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra (1/lnR)' (where it represents the first derivative of the reciprocal of the logarithmic spectrum) of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil. Thus, characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content. Finally, this study proposes a method of using plant spectra to find concealed Au deposits, which may have promising application prospects because of its simplicity and rapidity. 相似文献
14.
新疆民丰县农田土壤微量营养元素含量及分布 总被引：1，自引：0，他引：1
基于新疆民丰县85组农田表层土壤样品中的6种微量营养元素(Fe、B、Mn、Cu、Zn、Mo)的实测含量,运用地统计学方法对研究区土壤中微量营养元素的含量及空间分布进行分析。结果表明:Cu、Fe、Mn元素的块金系数在25%~75%,为中等程度的空间自相关性,空间变异同时受到自然因素和人为因素的影响;土壤B、Zn、Mo元素的块金系数小于25%,具有强烈程度的空间自相关性,空间变异受到影响的因素主要有成土母质、气候等自然条件。Fe、B、Zn元素的平均含量处于较缺乏水平,Mn、Cu元素的平均含量处于中等水平,Mo元素的平均含量处于较丰富水平。研究区土壤中6种微量元素之间存在一定程度的相关性。土壤有机质和pH对土壤微量营养元素含量均有不同程度的影响。 相似文献
15.
本研究目的在于分析农药残留量(pesticide residue,PR)与高光谱中响应特征参数之间的关系,并利用筛选的光谱特征参数建立反演毒死蜱残留量的有效模型。首先采用ASD Fieldspec高光谱仪测得韭菜样本的光谱,通过气相色谱-质谱联用(GC-MS)法测得毒死蜱残留量(PR)值;分析样本光谱反射率值及其一阶微分值与毒死蜱残留量的相关性,计算33个高光谱特征参数与毒死蜱残留量的相关性;根据相关系数高低选择敏感的光谱特征参数;最后采用最佳相关系数下的光谱特征参数对毒死蜱残留量进行建模反演。相关性分析结果显示:近红外波段789~867 nm范围内一阶微分光谱值与PR值呈正相关,1 860 nm处一阶微分光谱值(first-order differential 1 860 nm,FD1860)与PR值紧密相关;在33个高光谱特征参数中,近红外一阶微分总和(the sum of first-order differential near infrared,SDnir)与PR值呈良好的正相关关系。基于此,文章以供试样本的FD1860和SDnir观测值为自变量,分别建立了3个预测毒死蜱残留量的模型,即线性、二次多项式及指数模型,并采用交叉验证测试方法检验了模型的合理性。对实验所得决定系数R2和预测均方根误差(RMSE)的评价结果表明,以SDnir为自变量构建的模型稳定性强,其二次多项式模型是最佳反演毒死蜱残留量的有效模型。因此,样本的高光谱特征参数SDnir的变化幅度直接反映了韭菜样本中毒死蜱残留量的变化,表明运用蔬菜的高光谱特征参数反演蔬菜中农药残留量的方法是可行的。 相似文献
16.
新疆民丰县农田土壤微量营养元素含量及分布 总被引：1，自引：0，他引：1
基于新疆民丰县85组农田表层土壤样品中的6种微量营养元素（Fe、B、Mn、Cu、Zn、Mo）的实测含量,运用地统计学方法对研究区土壤中微量营养元素的含量及空间分布进行分析。结果表明：Cu、Fe、Mn元素的块金系数在25%~75%,为中等程度的空间自相关性,空间变异同时受到自然因素和人为因素的影响;土壤B、Zn、Mo元素的块金系数小于25%,具有强烈程度的空间自相关性,空间变异受到影响的因素主要有成土母质、气候等自然条件。Fe、B、Zn元素的平均含量处于较缺乏水平,Mn、Cu元素的平均含量处于中等水平,Mo元素的平均含量处于较丰富水平。研究区土壤中6种微量元素之间存在一定程度的相关性。土壤有机质和pH对土壤微量营养元素含量均有不同程度的影响。 相似文献
17.
Guli Jiapaer 《干旱区科学》2012,4(1):52-62
Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices,but few studies have focused on arid ecosystems.As a dominant species of riparian ecosystems in arid lands,Populus euphratica Oliv.is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages.In this study,we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P.euphratica leaves.Both the shapes and biochemical properties of P.euphratica leaves were found to change with the heights from ground surface.The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes,and also better than hyperspectral indices.Similar results were obtained for estimations of equivalent water thickness (EWT) and leaf mass per area (LMA).Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error (RMSE) and R 2 values between those obtained with the two calibration strategies using the inversed PROSPECT model.Hence,the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems,but the calibration to the model requires special attention. 相似文献
18.
Jeffrey S. Fehmi 《Arid Land Research and Management》2018,32(2):253-256
Adding a surface rock layer (also called rock armor or rock mulch) to constructed slopes improves erosion resistance but has had mixed effects on revegetation. This study investigated the effects of rock layer depth (no rocks, 10-, 15-, and 20-cm rock layers) and rock size (5–20?cm diameter rocks) on vegetation cover. Seeding was applied four times in the first 2 years. After 3 years, plots with a rock layer averaged 7% vegetative cover compared to 85% on plots without a rock layer. There was a nonsignificant trend toward less vegetation with a deeper rock layer. 相似文献
19.
通过开展小麦条锈病接种试验,在多个关键生育期获取被动式的冠层光谱和主动式的叶片生理观测并开展病情调查。在此基础上,结合优选的光谱特征和生理特征采用偏最小二乘回归方法(PLSR)构建病情严重度反演模型,得到不同生育期精度表现最优的特征组合。结果显示,基于光谱观测的优选光谱特征和基于叶片生理观测的Flav(类黄酮相对含量)、Chl(叶绿素含量)的不同组合在小麦挑旗期、灌浆早期和灌浆期分别具有较佳表现,模型精度达到r~2=0.90,RMSE=0.026。相比单纯采用光谱特征,综合冠层光谱和叶片生理观测能够使模型精度提高21%,表明两种数据的结合有利于提高病情严重度估测精度。上述研究可为小麦病害监测仪器的开发提供新的模式和思路。 相似文献
20.
随着高光谱遥感技术的快速发展,通过其定量估测土壤化学成分具有很好的可行性。使用ASD Pro FieldSpec3便携式光谱仪,测量准噶尔盆地人工林地风干土壤样品的可见光-近红外光谱,利用土壤反射光谱值预测全盐的含量。首先,通过皮尔森相关系数分析方法,计算土壤全盐与土壤反射光谱之间的相关性,其中土壤光谱值的二阶导数与土壤全盐的相关系数最高为0.806,均方根误差最小为1.508。其次,在基于光谱反射率的基础上,通过多元统计回归分析,表明土壤光谱在1 130 nm、1 430 nm和1 930 nm波段的全盐反演模型预测的效果较好,可以利用这3个波段建立回归方程,对土壤全盐进行反演估算。 相似文献