本文采用土壤含铁量和有机质含量作为性质指标,对反射率作常规模 型变换,变换后的数值与性质指标的相关系数最大值有一定提高.又进行反射率一阶微分乘以平方根一阶微分等几种创新模型变换,以及将变换效果较好的个别波段 选出进行交叉组合模型变换,变换后的数值与性质指标的相关系数最大值又有一定提高.实验证明,反射率一阶微分乘以平方根一阶微分模型以及选出波段交叉组合 变换模型可以有效提高反射率与土壤性质指标的相关性.

研究采用芦苇和香蒲叶片光谱及实测叶绿素含量数据,选取波段谱带范围为可见光波段400—760nm(为了避免近红外波段受叶片水分含量的影响,降低构建模型的稳定性),利用相关分析与逐步回归分析的统计学分析方法,建立叶面尺度下不同包络线去除衍生转换光谱:BD(band depth)、CRDR(continuum-removed derivative reflectance)、BDR(band depth ratio)、NBDI(normalized band depth index)与叶绿素含量估算模型。通过对入选波段的统计表明在550—750nm,特别是700—750nm(红边)波段范围内产生了较多的有效波段,是今后进行生物参量反演的重点波段范围。舍一交叉验证结果表明芦苇、香蒲和混合样本绿素含量估测的最佳模型分别为BD、CRDR和NBDI模型,其交叉验证决定系数依次为0.87、0.83和0.81,交叉验证均方根误差RMSE依次为0.16、0.15和0.33。并在此基础上利用独立样本非参数检验和多因子方差分析,探讨相关因素对于叶绿素含量估算模型精度的影响。结果表明物种差异、数据类型差异对于叶绿素回归模型的影响较大,而光谱类型差异及光谱数据与数据类型交互作用对于回归模型精度的影响较小。

We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.301 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.

土壤有机碳库是人类可以对其施加影响的最大生物圈碳库,其储量的估算和动态变化研究是全球碳循环研究中的重要内容.传统的各种估算和监测方法存在一定的问题,而利用高光谱遥感估算土壤有机碳含量具有便捷快速、节约成本、非破坏性和准确度高的优势,同时,从机载高光谱传感器获取的土壤图像数据对于估算大尺度区域土壤有机碳含量和跨时空动态研究具有相当大的潜力.但是,基于高光谱遥感数据的监测受到很多因素的影响,包括土壤有机碳含量的差异、成土母质、土壤含水量、土壤质地等以及与土壤有机碳光谱响应特性较为接近的氧化铁的影响.另外,野外土壤表面状态(如植被覆盖、表面粗糙度、土壤含水量和沙石等)以及大气、光照和辐射条件等也都对研究结果和估算精度产生较大的影响.今后该领域的研究应致力于对各影响因素的深入研究,同时寻求更多有效的计量方法提高估算模型的稳定性,并最终应用高光谱遥感数据开展区域尺度土壤有机碳含量的估算.

如何利用高光谱遥感技术快速、准确的监测土壤的理化特性是实现精准农业的必要前提,也是今后农业发展的必然趋势.本文综合国内外的研究结果较系统的阐述了土壤水分含量、土壤质地、土壤氧化铁含量、土壤有机质含量与土壤光谱特征的关系,并提出了该研究领域所存在的一些问题和今后的发展方向.

土壤有机质含量快速估算对于土壤肥力评价、土壤信息化管理和精准施肥具有重要意义。该文通过对北京顺义地区64个土壤样品高光谱曲线进行连续小波变换，估算了该地区潮土有机质质量分数，并与4种常用光谱变换方法进行了比较。结果表明，潮土具有与其他类型土壤类似的光谱曲线，经过去包络线处理后，在可见与近红波段都出现了明显吸收峰；采用连续小波变换方法所确定的潮土有机质估算的敏感波段为1 194、486和866 nm，对应小波分解尺度为2，3和4；利用小波能量系数与有机质质量分数所构建的多元线性回归模型的决定系数R2为0.67，模型实测值与预测值的检验精度R2为0.75，RMSE为0.21；而采用4种常用光谱变换方法建立的潮土有机质估测模型的R2最高只有0.09，说明连续小波变换方法更适合于潮土有机质质量分数估测。Kringing插值分析表明，应在顺义地区东南部增加取样点，以提高模型估算精度。该研究可为潮土土壤肥力的快速测定提供参考。

This paper presents a novel method for estimating black-soil organic matter (SOM) in the black-soil zone of northeast China from hyperspectral reflectance models. Traditional black-soil property measurements are relatively slow, but the pressures of agricultural production and environmental protection require a quick method to collect black-soil organic matter content. SOM estimation using soil hyperspectral reflectance models can meet this requirement, based on the spectral characteristics of black-soil in Northeast China. On the basis of the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The concepts of curvature and ratio indices are also applied to compare and test the stability and accuracy of data modeling. The results show that the response of black-soil spectral reflectance from 400–1,10002nm to organic matter content is more marked than that from 1,100–2,50002nm. Specifically, the main response range of black-soil organic matter is between 620–81002nm, with a maximal spectral response at 71002nm. By comparing different models, we found that the normalized first derivate model is optimal for estimating SOM content, with a determination coefficient of 0.93 and root mean squared errors (RMSE) of 0.18%.

The paper presents a GIS model for mapping soils in the semi arid region of Israel. The model is based on a priori knowledge of the soil generating factors in the study area, namely (1) the parent‐material (lithology) which determines the origin of the soil; and (2) the relief (including the drainage patents) which is responsible for erosion, deposition, and leaching processes along the catena. A special attempt was made to represent soil belts in small subwatersheds with different parent‐material. The width of these belts can be varied as a function of the local relief. We believe that GIS techniques, in contrast to conventional survey and mapping methods, have the potential to overcome the manpower and fiscal restrictions which limit monitoring of large areas and areas which are for different reasons difficult to access. However, systematically generalizing a spatial model for a large area may introduce some errors, either due to local variations which were not taken into account, or regional variations.

研究了土壤有机质含量与土壤高光谱之间的关系，在对原始光谱进行了预处理分析后，运用多元线性逐步回归法（MLSR）和人工神经网络法（ANN）建立了土壤有机质含量的反演模型，并对模型进行了验证。结果表明：人工神经网络所建立的反演模型普遍优于回归模型，网络集成模型优于单个BP网络模型，网络集成是提高反演模型准确性与稳定性的有效途径。网络集成模型为最优模型，总均方根误差为1．31，可以用于土壤有机质含量的快速测算。

土壤有机质含量是反映土壤肥力的重要指标, 对其进行动态监测是实施精准农业的重要措施。 近年来, 众多学者尝试采用土壤近地传感（proximal soil sensing）, 尤其是近地高光谱技术, 在田间和实验室获取不同形态土壤的高光谱数据, 不断引入新方法建立适用于不同地域和不同土壤类型的有机质含量的反演模型。 该研究在实验室内利用ASD FS3采集了土壤高光谱数据, 采用“重铬酸钾-外加热法”测得了土壤有机质含量； 分析了土壤原始光谱反射率（R）与有机质含量的相关性, 选取R2>0.15的敏感波段的反射率； 利用CWT对土壤原始光谱反射率（R）、 光谱反射率的连续统去除（CR）进行不同尺度的分解, 分析小波系数与土壤有机质含量的相关性, 选取R2>0.3的敏感波段的小波系数； 利用R选取的波段信息和R-CWT, CR-CWT的选取的小波系数, 分别建立偏最小二乘回归（PLSR）、 BP神经网络（BPNN）、 支持向量机回归（SVMR）三种不同的土壤有机质含量反演模型。 结果表明： 相比R与土壤有机质含量的决定系数R2, R-CWT, CR-CWT变换后得到的小波系数与土壤有机质含量的决定系数R2分别提高了0.15和0.2左右； CR-CWT-SVMR的模型效果最为显著, 预测集的R2和RMSE分别为0.83, 4.02, RPD值为2.48, 具有较高的估测精度, 能够全面稳定地估算土壤有机质含量； CR-CWT-PLSR的模型精度与CR-CWT-BPNN, CR-CWT-SVMR相比虽有一定差距, 但是其计算量要明显小于非线性的BPNN和SVMR方法, 具有模型简单、 运算速度快等特点, 对开发与设计田间传感器具有较大的应用价值。

基于江苏省宜兴市100个土样的可见光－近红外高光谱反射率400 2　450nm数据结合包络线去除continuum　removalcr与偏最小二乘回归partial 　least 　squares 　re gressionplsr 构建了土壤重金属砷as和有机质om含量的反演模型 结果表明相比普通plsr模型模型决定系数r2和预测根均方误差rmsep分别为0．5123．0９0和0．6215．９34cr－plsr构建的模型预测能力有明显的改善r2和rmsep分别为0．7632．323和0．９114．5９９ cr有效增强了550９001　4201　９00和2　200nm等波段处的反射光谱特征根据模型回归系数分析cr有效突出的波段正是as和om的cr－plsr模型所共用的重要波段研究表明cr能够协助plsr模型重要波段的选择利用遥感技术结合cr－plsr能够有效提高土壤重金属as和om含量的反演精度从而为土壤质量的遥感监测提供参考

ABSTRACT Near-infrared analysis (NIRA) methodology was applied to the reflectance spectra of arid and semiarid soils in the visible and near infrared (VIS-NIR; 0.4 1.1 m) spectral region. The method is termed visible and near-infrared analysis (VNIRA). Although the spectra of the soils were characterized as monotonous and featureless, the methodology has yielded a prediction equation for estimating several soil constituents from the reflectance curves. The constituents were: CaCO3, Fe2O3, Al2O3, SiO2, LOI (lost-on-ignition), Fed (free iron oxides), and K2O. Several mathematic manipulations were applied to the raw data in order to derive the optimal prediction equation. Spectral compression into 6, 8, 15, 71, and 350 spectral bands and a spectral derivation technique were applied to four separate soil groups, which were selected on the basis of their chemical characteristics. The wavelengths selected by the method for the optimal prediction equation were assigned to constituents that consisted of spectral features within the VIS-NIR region. An intercorrelation between spectrally featureless constituents and constituents with special features was found to be the major mechanism by which to predict constituents that had no spectral features within this part of the spectrum. It was shown that low spectral resolution is not necessarily a limiting factor in obtaining quantitative information about the chemistry of soil samples. All the examined soil constituents except Fed (which needed 700 spectral bands) required between 15 and 350 spectral bands for optimal prediction. It was concluded that the VIS-NIR (0.4 1.1 m) is a suitable spectral region for obtaining quantitative information about soil chemistry. Although the VNIRA performance is not as precise as the chemical performance, the precision obtained is likely to be useful for rapid soil characterization and remote-sensing applications. We strongly recommend the use of both the VNIRA and the NIRA methods to better interpret high resolution remote-sensing data.

通过对横山县84个土样350～2500nm波段的光谱曲线分析，进行土壤质量12项指标的光谱反演。为获得有效光谱反演指标，利用单相关分析方法计算土壤质量指标与光谱反射率的相关系数并绘制相关系数曲线，根据极大相关性选择最佳波段作为光谱反演指标；剔除异常样本后，利用模糊识别理论建立土壤质量指标反演模型，通过优化得到模型的最佳参数。土壤质量12项指标光谱反演模型的平均检验误差均小于15％，说明模型的精度较高。

为克服Harris角点检测算法中漏检正确角点和提取出较多伪角点的问题,以及在对不同图像处理时,非极大值抑制无法设置通用阈值的现象,本文在进行非极大值抑制时采用自适应阈值,从而可得较多的正确角点。为进一步抑制角点检测中的伪角点数目并提高处理大图像的算法效率,加入预筛选得备选角点这一步骤,通过在进行Harris角点检测之前就先去除部分肯定不可能是角点的像素点,以减少最终得到的伪角点数,并有效地减少了运算量,提高了效率。实验结果显示改进的Harris角点检测算法的运行时间仅为原始算法的30%,且可以得到更多的精确角点和更少的伪角点,具有很好的角点检测性能。

提出基于特征角点的控制点自适应匹配算法：依据图像数据灰度特征自动确定相应阈值，从基准影 像上提取控制点。采用动态模板进行不等距搜索，确定潜在目标控制点；构建三角网，利用等角变换判别目标控制点；构建仿射变换方程来筛选目标控制点。该算法 无需设定灰度相关系数最大值阈值即可判别目标控制点。选取ASTER和TM两种成像差异显著的图像数据，对该算法进行试验，结果表明。该算法可以准确提取 有价值的特征角点，具有较高的控制点识别精度和运算效率，具有较强的适应性和应用价值，较之传统匹配算法有明显优势。

对在宜兴市和横山县采集的174个土样400 nm~2 500 nm波段的光谱曲线进行了研究。为了有效去除背景噪声对目标光谱的影响,并将非线性关系线性化,首先对土壤光谱进行了14种变换,然后运用光谱微分技术、逐步回归分析等方法研究了土壤光谱反射特性与土壤有机质之间的关系。结果表明,反射率对数的一阶微分这一变换形式对土壤有机质含量最为敏感。建立了相应的回归预测模型,模型方程判定系数达到0.885,较好地利用土壤光谱反射特性预测了土壤有机质的含量。

为了对比不同方法建模效果的差异，筛选潮土有机质高光谱最佳预测模型，该研究采集国家潮土土壤肥力与肥料效益长期监测站不同施肥处理耕层土样83份，采用25种光谱预处理方法（15种单一预处理方法，10种预处理方法相加算法）结合3种建模方法（多元线性回归、偏最小二乘回归、支持向量机回归），构建不同的潮土有机质高光谱预测模型。对比模型预测结果表明，最佳光谱建模方法是偏最小二乘回归法，该方法结合多种预处理方法均获得了较高的模型预测精度和可靠性，25个检验模型的平均决定系数、均方根误差值RMSEv和相对分析误差RPD值分别为0.913、1.264 g/kg和3.299。使用预处理方法相加算法能更好地提升模型精度，相比使用单一预处理方法，3种建模方法的检验模型平均决定系数分别提高了0.049、0.033和0.071，RMSEv分别降低了0.318、0.204和0.528 g/kg，RPD值分别提高了0.530、0.307和1.144。先用多元散射校正法再进行5个平滑点数的一阶导数预处理在3种建模方法中表现均较好（平均决定系数=0.934，平均RMSEv=1.17 g/kg，平均RPD=3.59)，可作为潮土有机质预测模型的通用预处理方法。偏最小二乘回归模型结合最大值标准化预处理所建模型（决定系数=0.948，RMSEv=0.972 g/kg，RPD=4.276）精度高、可靠性强，且建模过程数据运算更为简便，是筛选出的最佳潮土有机质高光谱预测模型。该研究结果对潮土有机质高光谱预测建模有一定的指导作用，并为筛选最佳高光谱预测模型提供技术参考。