基于可见近红外成像光谱技术土壤剖面氮的预测研究
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摘要
土壤剖面及其不同层次的属性研究对于土壤发生发育、土壤分类等土壤科学研究有着极其重要的意义。传统的土壤信息获取过程耗时费力,而遥感技术可以快速、周期性地提供各种尺度的土壤信息,已被广泛应用于土壤资源调查、土地质量评价、土壤分等定级、土壤分类及土壤制图等研究工作当中。传统的土壤理化属性测试方法费时、繁琐,成本高,难以满足快速监测土壤氮(N)含量的需求。近年来利用光谱测定技术快速、简便、无损地对样品进行定量分析,已在各个领域广泛应用,并取得了良好的效果。成像技术与光谱技术的结合获取的数据既具有高空间分辨率又同时具有高光谱分辨率,能够提供非常丰富的土壤遥感信息,为横向上的土壤定量监测及土壤属性制图等提供了坚实的数据基础。然而,从国内外学者的研究工作来看,土壤科学缺乏一种对于土壤完整剖面的高空间及高光谱分辨率成像技术来测量全氮(TN)含量。对于土壤属性的定量研究大都选用深0-15cm或0-20cm的耕层土样,鲜有针对0-100cm剖面点状样的相关报道,特别是针对纵向上完整土壤剖面TN制图更是未见诸于相关文献。
鉴于此,本文以配备25μm狭缝和视场角13.1°的35mm焦距镜头,电荷耦合器件(CCD)1004pixels×1002pixels的成像光谱仪用于数据采集,涵盖400-1000nm共753个波段。首先利用采自潜江后湖地区0-100cm剖面点状土样光谱反射率,对比分析不同预处理方法、不同建模方法对预测结果的影响,探究最佳的预测模型。然后利用采自咸宁地区0-100cm剖面点状土样的可见近红外(Vis-NIR)土壤反射光谱建立预测TN的校正模型,再利用该模型对该地区3个完整土壤剖面的Vis-NIR高光谱影像数据进行TN的反演并制图,考察成像光谱技术在纵向上的土壤TN预测能力。同时,在研究过程中基于软件Matlab2010b编写了一个土壤高光谱影像处理系统来完成相关数据处理。主要研究成果如下：
1.基于Vis-NIR光谱土壤TN含量的预测研究
土壤建模方法是影响光谱定量结果的主要因素之一。对采自潜江后湖农场0-100cm深度范围的48个剖面点状土样经过风干、研磨、过筛后进行光谱采集。经光谱反射率曲线感兴趣区(ROI)提取,为剔除无效光谱区域决定保留470-1000nm波段数据,然后分析比较了多种预处理方法建立的PCR(主成分回归)和PLSR(偏最小二乘回归)模型,最终确定采用先一阶微分(FD)变换再二阶7窗口Savitzki-Golay平滑作为光谱预处理最佳方法。再分别应用PCR、PLSR和反向传播神经网络(BPNN)3中方法建立土壤TN的定量模型。PCR与PLSR两线性模型的R2分别为0.74和0.80,其RPD分别为2.23和2.22,两模型能用于土壤TN含量的精确估计。由PCR提供主成分数(Principal Components), PLSR提供潜变量数(Laten Variables)分别作为BPNN的输入,构建的两个非线性模型BPNN-PCs和BPNN-LVs均明显优于线性模型PCR和PLSR。其中以4个潜变量作为输入的BPNN-LVs模型预测性能最优,R2以及RPD分别达到0.90和3.11。实验结果表明,提取Vis-NIR光谱的PLSR潜变量因子作为BPNN的输入,所建定量模型可用于土壤N纵向时空分布的快速准确预测。
2.基于Vis-NIR成像光谱的土壤剖面TN反演及制图
采自咸宁崇阳的完整土壤剖面高光谱影像在TN反演前需做一系列预处理。通过用数码相机拍摄有固定格网尺寸背景的数码照片结合剖面高光谱影像进行几何校正,解决了由于光谱仪和拍摄平台等技术局限性导致拍摄的影像存在较大形变的问题,并将影像校正为1mm精度。通过空间维及光谱维的裁剪,去掉木框及平台背景而保留土壤影像数据(160pixels×980pixels)及有效的光谱波段(470-1000nm)。经几何校正和裁剪后的影像使用多种监督分类法处理,结果显示最小距离法对于阴影、裂缝等无效数据与土壤的区分效果最佳。提出一种“采样模板”的方法,让模板依据限定条件进行块状或条状采样,做类似于ROI的平均处理,最终解决点状样与剖面光谱的尺度不一致问题。再利用10个点状土样光谱建立PLSR校正模型,对三个完整土壤剖面高光谱影像进行TN预测。结果表明,Vis-NIR成像光谱可以应用于纵向土壤剖面TN反演及制图,且预测效果良好,经实测值检验0-100cm的R2和RPD分别为0.56和1.41模型达到了粗略估计范畴；0-60cm的效果较好R2和RPD分别为0.87和1.76,显示出成像光谱技术可能具有纵向局限性。各剖面单独预测发现,XL-1在60-100cm的预测偏差拉低了三个剖面总体在0-100cm的预测效果,其在0-60cm的检验结果显示R2=0.94,RPD=2.19,模型优异且达到了可精确预测的范畴,而其0-100cm的R2和RPD则分别降至0.15和1.06。XL-2和XL-3在0-100cm的检验结果中显示R2分别为0.91和0.93,RPD分别为1.81和1.69,模型均达到了粗略定量范畴。以上结果表明,本研究已初步建立起一套基于Vis-NIR成像光谱的土壤剖面TN反演及制图处理流程,该技术用于完整剖面土壤TN含量的粗略估计是可行的。
3.土壤高光谱影像数据处理系统设计与实现
土壤高光谱影像数据大,计算效率低。现有各类图像处理软件仅能实现通用功能,难以满足对影像做无效值剔除、模板采样等特殊处理需求。为了更好地对数据进行处理,本研究基于Matlab2010b设计并编译了一个图形用户界面(GUI),实现了土壤高光谱影像的数据读取、影像裁剪、无效值剔除、采样模板、位置还原、精度评价等功能。数据读取给土壤高光谱影像处理带来了便利,影像空间维及光谱维的裁剪为后续研究提供了良好的数据支持,“采样模板”的实现对光谱尺度不一致问题的解决提供了技术手段。位置还原、精度评价等对预测结果的分析提供了便利。此外,数据处理所需的参数通过GUI输入,令操作十分简单。与ENVI4.7及Pls-Toolbox7.0.2配合在方便易用的同时提高了工作效率,也弥补了相关专业软件在处理方法上的不足。
Soil properties in entire soil profiles or different soil horizons are of great importance in the studies of soil genesis, development, classification, and other soil processes. Traditional soil information acquisition is time-consuming and costly, but remote sensing technology can provide soil information at various scales rapidly and periodically, and has been widely applied in researches such as soil resource survey, land quality evaluation, soil classification, and soil mapping. Traditional methods for measuring soil physical and chemical properties are time-consuming, complicated and costly, and they cannot meet the needs of rapidly monitoring soil property changes. In recent years, the spectrometric technology, which is rapid, simple, and non-destructive in quantitatively analyzing samples, were extensively used in various fields and achieved good results. The data acquired through using imaging technology and spectroscopy technology together have both high spatial resolution and high spectral resolution, thus being able to provide very rich soil remote sensing information which builds a solid foundation for quantitative monitoring of soils and soil properties in the horizontal dimensions. However, studies in soil science thus far show that we still lack an imaging technique specifically for measuring soil total nitrogen (TN) contents in entire soil profiles with high spatial and spectral resolutions. At present, quantitative researches on soil properties mostly use samples collected in the top layer of0-15cm or0-20cm depth; studies on soil point samples in the profile depth of0-100cm were rarely reported, and we have not seen in literatue any study that mapped soil properties vertically along entire soil profiles.
In view of this situation, this study used a hyperspectral camera (400-1000nm in753spectral bands) with a CCD of1004pixels×1002pixels for data acquisition. We first analyzed the effects of different pre-treatment methods and modeling methods on prediction results, and explored the optimal prediction model using the soil spectral reflectance data from0-100cm soil profiles collected from the Qianjiang area. Then we established a soil calibration model using the Vis-NIR spectral reflectance data of soil point samples collected from the Xianning area, and further applied this model to soil TN content inversion and mapping using the Vis-NIR hyperspectral image data from three complete soil profiles in the same area, so as to examine the capability of the imaging spectroscopy in predicting soil TN contents along vertical soil profiles. At the same time, we designed a Matlab-based soil hyperspectral image processing system for processing relevant data. Main achivements from this study are listed as follows:
1. Soil TN prediction based on Vis-NIR spectra
The method of soil modeling is one of the main factors influencing the accuracy of soil property quantification with Vis-NIR spectroscopy. After drying, grinding and sieving the48point soil samples within the depth of0-100cm in12soil profiles collected from Qianjiang, we obtained their soil spectra. In this study we compared the performances of three calibration methods-PCR, PLSR, and BPNN, based on the Vis-NIR reflectance spectra for soil TN prediction. The spectra data of48soil samples in the470-1000nm wavelength range were processed using the method of first-order derivative transformation combined with second-order Savizky-Golay smoothing, and then leave-one-out cross validation was adopted to determine the optimal factor number. The results indicated that the two linear models (PCR and PLSR) were able to meet the general prediction requirement with little difference:their R2indices are0.74and0.8, respectively, and their RPD values are2.23and2.22, repsectively. The two nonlinear models, built by BPNN combined respectively with PCR and PLSR, are superior to the linear models of PCR and PLSR in prediction precision. The BPNN-PC model used the principal components (PCs) resulting from the principal component regression (PCR) as itsinput, while the BPNN-LV model used the first4latent variables (LVs) obtained from PLSR as its input. Among them, BPNN-LV has the best performance (R2=0.9, RPD=3.11). Therefore, BPNN-LV can be a good model for rapidly and accurately predicting the vertical spatial distribution of soil TN using Vis-NIR spectroscopy.
2. Soil profile TN inversion and mapping based on Vis-NIR imaging spectroscopy
Pre-processing should be conducted on the spectral images of the complete soil profiles taken from Xianning before soil TN inversion. Geometric correction of profile hyperspectral images was performed using photos taken by a digital camera with a fixed-grid background. This method solved the deformation problem of spectral images caused by technological limitations in instruments and platforms, and adjusted the image precision to1mm. Soil image data (160pixels×980pixels) and effective spectral bands (470-1000nm) were kept through image scribing in spatial and spectral dimensions. Comparison of multiple supervised classification methods on the geometrically corrected and scribed images found out that the minimum distance method was the best in distinguishing invalid data (e.g., shadow and crack) from soil data. We proposed a "sampling panel" method, which make the template collect block or strip-shaped samples according to constraint conditions, then average the samples (similar to ROI), and finally solve the scale inconsistency problem between point samples and profile spectra. We further built a PLSR calibration model using the spectrum data of10spot soil samples so as to forecast the TN contents in three complete soil profiles using their spectral images. Results showed that Vis-NIR imaging spectroscopy technology could be used in soil TN inversion and mapping along vertical profiles, and was able to meet the general prediction purpose. Test with measured data obtained a R2vaule of0.56and a RPD value of1.41for the depth of0-100cm, which indicated that the prediction method reached the coarse estimation level. The R2and RPD values were better for the0-60cm depth, with the former being0.87and the latter being1.76; this also indicated that the Vis-NIR spectrum tecnology might have some limitation in the vertical direction. But checking the predictions of each profile separately found that the large forecast deviation of XL-1in the60-100cm depth lowered the total prediction effectiveness of the three profiles in the0-100cm depth; the test results for XL-1in0-60cm depth had R2=0.94and RPD=2.19, implying the model was excellent and could accurately predict, but those in the depth of0-100cm had R2=0.15and RPD=1.06, indicating a large decrease in prediction accuracy. The test results for XL-2and XL-3in the0-100cm depth showed that their models reached the level of rough prediction, with R2being0.91and0.93, and RPD being1.81and1.69, respectively. Above results concluded that this study had preliminarily established a set of procedures for soil TN inversion and mapping using the Vis-NIR spectroscopy technology, and the method was feasible for coarsely estimating soil TN contents in whole soil profiles.
3. Design and implementation of the soil hyperspectral image data processing system
Because of the huge soil hyperspectral image data, it is inefficient to deal with them directly. The existing image processing softwares can only achieve universal functions, and not have some functions, such as removing invalid values in image, sampling in the templates and so on. Based on Matlab2010b, a graphical user interface (GUI) was designed and compiled for processing soil hyperspectral image data. The system has the following functions:soil hyperspectral image data reading, image scribing, invalid value elimination, sample template, prediction value position reduction, accuracy evaluation, etc. It can meet the research needs and is easy to use. It improved our working efficiency and obtained good results in this study. At the same time, it also made up the deficiency of related professional software systems (such as ENVI4.7and Pls-Toolbox7.0.2) in processing methods.
鉴于此,本文以配备25μm狭缝和视场角13.1°的35mm焦距镜头,电荷耦合器件(CCD)1004pixels×1002pixels的成像光谱仪用于数据采集,涵盖400-1000nm共753个波段。首先利用采自潜江后湖地区0-100cm剖面点状土样光谱反射率,对比分析不同预处理方法、不同建模方法对预测结果的影响,探究最佳的预测模型。然后利用采自咸宁地区0-100cm剖面点状土样的可见近红外(Vis-NIR)土壤反射光谱建立预测TN的校正模型,再利用该模型对该地区3个完整土壤剖面的Vis-NIR高光谱影像数据进行TN的反演并制图,考察成像光谱技术在纵向上的土壤TN预测能力。同时,在研究过程中基于软件Matlab2010b编写了一个土壤高光谱影像处理系统来完成相关数据处理。主要研究成果如下：
1.基于Vis-NIR光谱土壤TN含量的预测研究
土壤建模方法是影响光谱定量结果的主要因素之一。对采自潜江后湖农场0-100cm深度范围的48个剖面点状土样经过风干、研磨、过筛后进行光谱采集。经光谱反射率曲线感兴趣区(ROI)提取,为剔除无效光谱区域决定保留470-1000nm波段数据,然后分析比较了多种预处理方法建立的PCR(主成分回归)和PLSR(偏最小二乘回归)模型,最终确定采用先一阶微分(FD)变换再二阶7窗口Savitzki-Golay平滑作为光谱预处理最佳方法。再分别应用PCR、PLSR和反向传播神经网络(BPNN)3中方法建立土壤TN的定量模型。PCR与PLSR两线性模型的R2分别为0.74和0.80,其RPD分别为2.23和2.22,两模型能用于土壤TN含量的精确估计。由PCR提供主成分数(Principal Components), PLSR提供潜变量数(Laten Variables)分别作为BPNN的输入,构建的两个非线性模型BPNN-PCs和BPNN-LVs均明显优于线性模型PCR和PLSR。其中以4个潜变量作为输入的BPNN-LVs模型预测性能最优,R2以及RPD分别达到0.90和3.11。实验结果表明,提取Vis-NIR光谱的PLSR潜变量因子作为BPNN的输入,所建定量模型可用于土壤N纵向时空分布的快速准确预测。
2.基于Vis-NIR成像光谱的土壤剖面TN反演及制图
采自咸宁崇阳的完整土壤剖面高光谱影像在TN反演前需做一系列预处理。通过用数码相机拍摄有固定格网尺寸背景的数码照片结合剖面高光谱影像进行几何校正,解决了由于光谱仪和拍摄平台等技术局限性导致拍摄的影像存在较大形变的问题,并将影像校正为1mm精度。通过空间维及光谱维的裁剪,去掉木框及平台背景而保留土壤影像数据(160pixels×980pixels)及有效的光谱波段(470-1000nm)。经几何校正和裁剪后的影像使用多种监督分类法处理,结果显示最小距离法对于阴影、裂缝等无效数据与土壤的区分效果最佳。提出一种“采样模板”的方法,让模板依据限定条件进行块状或条状采样,做类似于ROI的平均处理,最终解决点状样与剖面光谱的尺度不一致问题。再利用10个点状土样光谱建立PLSR校正模型,对三个完整土壤剖面高光谱影像进行TN预测。结果表明,Vis-NIR成像光谱可以应用于纵向土壤剖面TN反演及制图,且预测效果良好,经实测值检验0-100cm的R2和RPD分别为0.56和1.41模型达到了粗略估计范畴；0-60cm的效果较好R2和RPD分别为0.87和1.76,显示出成像光谱技术可能具有纵向局限性。各剖面单独预测发现,XL-1在60-100cm的预测偏差拉低了三个剖面总体在0-100cm的预测效果,其在0-60cm的检验结果显示R2=0.94,RPD=2.19,模型优异且达到了可精确预测的范畴,而其0-100cm的R2和RPD则分别降至0.15和1.06。XL-2和XL-3在0-100cm的检验结果中显示R2分别为0.91和0.93,RPD分别为1.81和1.69,模型均达到了粗略定量范畴。以上结果表明,本研究已初步建立起一套基于Vis-NIR成像光谱的土壤剖面TN反演及制图处理流程,该技术用于完整剖面土壤TN含量的粗略估计是可行的。
3.土壤高光谱影像数据处理系统设计与实现
土壤高光谱影像数据大,计算效率低。现有各类图像处理软件仅能实现通用功能,难以满足对影像做无效值剔除、模板采样等特殊处理需求。为了更好地对数据进行处理,本研究基于Matlab2010b设计并编译了一个图形用户界面(GUI),实现了土壤高光谱影像的数据读取、影像裁剪、无效值剔除、采样模板、位置还原、精度评价等功能。数据读取给土壤高光谱影像处理带来了便利,影像空间维及光谱维的裁剪为后续研究提供了良好的数据支持,“采样模板”的实现对光谱尺度不一致问题的解决提供了技术手段。位置还原、精度评价等对预测结果的分析提供了便利。此外,数据处理所需的参数通过GUI输入,令操作十分简单。与ENVI4.7及Pls-Toolbox7.0.2配合在方便易用的同时提高了工作效率,也弥补了相关专业软件在处理方法上的不足。
Soil properties in entire soil profiles or different soil horizons are of great importance in the studies of soil genesis, development, classification, and other soil processes. Traditional soil information acquisition is time-consuming and costly, but remote sensing technology can provide soil information at various scales rapidly and periodically, and has been widely applied in researches such as soil resource survey, land quality evaluation, soil classification, and soil mapping. Traditional methods for measuring soil physical and chemical properties are time-consuming, complicated and costly, and they cannot meet the needs of rapidly monitoring soil property changes. In recent years, the spectrometric technology, which is rapid, simple, and non-destructive in quantitatively analyzing samples, were extensively used in various fields and achieved good results. The data acquired through using imaging technology and spectroscopy technology together have both high spatial resolution and high spectral resolution, thus being able to provide very rich soil remote sensing information which builds a solid foundation for quantitative monitoring of soils and soil properties in the horizontal dimensions. However, studies in soil science thus far show that we still lack an imaging technique specifically for measuring soil total nitrogen (TN) contents in entire soil profiles with high spatial and spectral resolutions. At present, quantitative researches on soil properties mostly use samples collected in the top layer of0-15cm or0-20cm depth; studies on soil point samples in the profile depth of0-100cm were rarely reported, and we have not seen in literatue any study that mapped soil properties vertically along entire soil profiles.
In view of this situation, this study used a hyperspectral camera (400-1000nm in753spectral bands) with a CCD of1004pixels×1002pixels for data acquisition. We first analyzed the effects of different pre-treatment methods and modeling methods on prediction results, and explored the optimal prediction model using the soil spectral reflectance data from0-100cm soil profiles collected from the Qianjiang area. Then we established a soil calibration model using the Vis-NIR spectral reflectance data of soil point samples collected from the Xianning area, and further applied this model to soil TN content inversion and mapping using the Vis-NIR hyperspectral image data from three complete soil profiles in the same area, so as to examine the capability of the imaging spectroscopy in predicting soil TN contents along vertical soil profiles. At the same time, we designed a Matlab-based soil hyperspectral image processing system for processing relevant data. Main achivements from this study are listed as follows:
1. Soil TN prediction based on Vis-NIR spectra
The method of soil modeling is one of the main factors influencing the accuracy of soil property quantification with Vis-NIR spectroscopy. After drying, grinding and sieving the48point soil samples within the depth of0-100cm in12soil profiles collected from Qianjiang, we obtained their soil spectra. In this study we compared the performances of three calibration methods-PCR, PLSR, and BPNN, based on the Vis-NIR reflectance spectra for soil TN prediction. The spectra data of48soil samples in the470-1000nm wavelength range were processed using the method of first-order derivative transformation combined with second-order Savizky-Golay smoothing, and then leave-one-out cross validation was adopted to determine the optimal factor number. The results indicated that the two linear models (PCR and PLSR) were able to meet the general prediction requirement with little difference:their R2indices are0.74and0.8, respectively, and their RPD values are2.23and2.22, repsectively. The two nonlinear models, built by BPNN combined respectively with PCR and PLSR, are superior to the linear models of PCR and PLSR in prediction precision. The BPNN-PC model used the principal components (PCs) resulting from the principal component regression (PCR) as itsinput, while the BPNN-LV model used the first4latent variables (LVs) obtained from PLSR as its input. Among them, BPNN-LV has the best performance (R2=0.9, RPD=3.11). Therefore, BPNN-LV can be a good model for rapidly and accurately predicting the vertical spatial distribution of soil TN using Vis-NIR spectroscopy.
2. Soil profile TN inversion and mapping based on Vis-NIR imaging spectroscopy
Pre-processing should be conducted on the spectral images of the complete soil profiles taken from Xianning before soil TN inversion. Geometric correction of profile hyperspectral images was performed using photos taken by a digital camera with a fixed-grid background. This method solved the deformation problem of spectral images caused by technological limitations in instruments and platforms, and adjusted the image precision to1mm. Soil image data (160pixels×980pixels) and effective spectral bands (470-1000nm) were kept through image scribing in spatial and spectral dimensions. Comparison of multiple supervised classification methods on the geometrically corrected and scribed images found out that the minimum distance method was the best in distinguishing invalid data (e.g., shadow and crack) from soil data. We proposed a "sampling panel" method, which make the template collect block or strip-shaped samples according to constraint conditions, then average the samples (similar to ROI), and finally solve the scale inconsistency problem between point samples and profile spectra. We further built a PLSR calibration model using the spectrum data of10spot soil samples so as to forecast the TN contents in three complete soil profiles using their spectral images. Results showed that Vis-NIR imaging spectroscopy technology could be used in soil TN inversion and mapping along vertical profiles, and was able to meet the general prediction purpose. Test with measured data obtained a R2vaule of0.56and a RPD value of1.41for the depth of0-100cm, which indicated that the prediction method reached the coarse estimation level. The R2and RPD values were better for the0-60cm depth, with the former being0.87and the latter being1.76; this also indicated that the Vis-NIR spectrum tecnology might have some limitation in the vertical direction. But checking the predictions of each profile separately found that the large forecast deviation of XL-1in the60-100cm depth lowered the total prediction effectiveness of the three profiles in the0-100cm depth; the test results for XL-1in0-60cm depth had R2=0.94and RPD=2.19, implying the model was excellent and could accurately predict, but those in the depth of0-100cm had R2=0.15and RPD=1.06, indicating a large decrease in prediction accuracy. The test results for XL-2and XL-3in the0-100cm depth showed that their models reached the level of rough prediction, with R2being0.91and0.93, and RPD being1.81and1.69, respectively. Above results concluded that this study had preliminarily established a set of procedures for soil TN inversion and mapping using the Vis-NIR spectroscopy technology, and the method was feasible for coarsely estimating soil TN contents in whole soil profiles.
3. Design and implementation of the soil hyperspectral image data processing system
Because of the huge soil hyperspectral image data, it is inefficient to deal with them directly. The existing image processing softwares can only achieve universal functions, and not have some functions, such as removing invalid values in image, sampling in the templates and so on. Based on Matlab2010b, a graphical user interface (GUI) was designed and compiled for processing soil hyperspectral image data. The system has the following functions:soil hyperspectral image data reading, image scribing, invalid value elimination, sample template, prediction value position reduction, accuracy evaluation, etc. It can meet the research needs and is easy to use. It improved our working efficiency and obtained good results in this study. At the same time, it also made up the deficiency of related professional software systems (such as ENVI4.7and Pls-Toolbox7.0.2) in processing methods.
引文
1.鲍士旦.土壤农化分析[M].北京：中国农业出版社,1999.
2.鲍一丹,何勇,方慧,等.土壤的光谱特征及氮含量的预测研究[J].光谱学与光谱分析,2007,27(1)：62-65.
3.程街亮,纪文君,周银,等.土壤二向反射特性及水分含量对其影响研究[J].土壤学报,2011,48(2)：255-262.
4.陈述彭,童庆禧,郭华东.遥感信息机理研究[M].北京：科学出版社,1998.
5.褚小立,王艳斌,陆婉珍.近红外光谱定量校正模型的建立及应用[J].理化检验：化学分册,2008,44(8)：796-800.
6.褚小立,许育鹏,陆婉珍.偏最小二乘法方法在光谱定性分析中的应用研究[J].现代仪器,2007,5：13-15.
7.褚小立,袁洪福,王艳斌,等.近红外稳健分析校正模型的建立(Ⅰ)——样品温度的影响[J].光谱学与光谱分析,2004,24(6)：666-671.
8.从维清,李先友,彭宣贵.潜江水利志[M].1997.
9.戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究[M].遥感文献.北京：科学出版社.1981.315-323.
10.杜昌文.土壤红外光声光谱[M].北京：科学出版社,2012.
11.杜昌文,周健民.傅里叶变换红外光声光谱法测定土壤中有效磷[J].分析化学,2007,35(1)：119-122.
12.高英志,汪诗平,韩兴国,等.退化草地恢复过程中土壤氮素状况以及与植被地上绿色生物量形成关系的研究[J].植物生态学报,2004,28(3)：285-293.
13.高云,汪善勤,李小昱等.三轴向土壤高光谱成像实验台,CN201527394U[P/OL].2010.
14.葛振华,姚贤贺,杜维贤.咸宁地区水利志[M].2001.
15.郭伟良,王丹,宋佳等.近红外光谱法同时快速定量分析蛹虫草菌丝体中4种有效成分[J].光学学报,2011,31(2)：274-281.
16.贺军亮,蒋建军,周生路等.土壤有机质含量的高光谱特性及其反演[J].中国农业科学,2007,40(3)：638-643.
17.何挺,王静,程烨等.土壤氧化铁光谱特征研究[J].地理与地理信息科学,2006,22(2)：30-34.
18.黄康,汪辉君,徐惠荣等.基于最小二乘支持向量机的番茄汁糖酸度分析研究[J].光谱学与光谱分析,2009,29(4)：931-934.
19.黄巧云.土壤学[M].北京：中国农业出版社,2006.
20.季耿善,徐彬彬.遥感波段解译特性[J].土壤专报,1987,114：477-485.
21.纪文君,李曦,李成学等.基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[J].光谱学与光谱分析,2012,32(9)：2393-2398.
22.纪文君,史舟,周清等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,31(3)：277-282.
23.李亮亮,依艳丽,凌国鑫等.地统计学在土壤空间变异研究中的应用[J].土壤通报,2005,36(2)：265-268.
24.李民赞,韩东海,王秀.光谱分析技术及其应用[M].北京：科学出版社,2006.
25.李硕.基于Vis/NIR光谱不同粒径下土壤碳氮的预测研究[D]：华中农业大学,2010.
26.李硕,汪善勤,张美琴.基于可见-近红外光谱比较主成分回归,偏最小二乘回归和反向传播神经网络对土壤氮的预测研究[J].光学学报,2012,32(8)：289-293.
27.李曦,史舟.基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[C].中国土壤学会第十二次全国会员代表大会暨第九届海峡两岸土壤肥料学术交流研讨会,中国四川成都,2012,153-160.
28.李艳,史舟.地统计学在土壤学中的应用[M].中国农业出版社.2006.
29.刘伟东.高光谱遥感土壤信息提取与挖掘研究[D]：中国科学院研究生院(遥感应用研究所),2002.
30.刘燕德,陈兴苗,欧阳爱国.可见/近红外光谱法无损检测赣南脐橙可溶性固形物[J].光学学报,2008,28(3)：478-481.
31.卢艳丽,白由路,王磊,等.黑土土壤中全氮含量的高光谱预测分析[J].农业工程学报,2010,(1)：256-261.
32.路鹏,魏志强,牛铮.应用特征波段和反射变形差的方法进行土壤属性估算[J].光谱学与光谱分析,2009,29(3)：716-721.
33.彭玉魁,张建新,何绪生等.土壤水分、有机质和总氮含量的近红外光谱分析研究[J].土壤学报,1998,(4)：553-559.
34.瞿明凯.几种地统计学方法在县域土壤空间信息处理上的应用与研究[D]：华中农业大学,2012.
35.日本遥感研究会.遥感精解[M].北京：测绘出版社,2011.
36.宋小宁,赵英时.MODIS图象的云检测及分析[J].中国图象图形学报,2003,(9)：112-126.
37.孙光明,刘飞,张帆等.基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法[J].光学学报,2010,(4)：1192-1196.
38.孙家抦.遥感原理与应用[M].武汉：武汉大学出版社,2003.
39.孙建英,李民赞,郑立华,等.基于近红外光谱的北方潮土土壤参数实时分析[J].光谱学与光谱分析,2006,26(3)：426-429.
40.田高友,袁洪福,褚小立,等.结合小波变换与微分法改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4)：516-520.
41.童庆禧,张兵,郑兰芬.高光谱遥感——原理、技术与应用[M].北京：高等教育出版社.2006.
42.汪善勤,舒宁,张海涛.土壤全氮田间Vis/NIR光谱测定方法研究[J].光谱学与光谱分析,2008,28(4)：808-812.
43.吴景贵,席时权,姜岩.红外光谱在土壤有机质研究中的应用[J].光谱学与光谱分析,1998,18(1)：52-57.
44.谢伯承.基于高光谱遥感不同发生层土壤的光谱信息的提取研究[D],西北农林科技大学,2004.
45.徐彬彬.土壤剖面的反射光谱研究[J].土壤,2000,(6)：281-287.
46.徐彬彬,季耿善.土壤光谱反射特性与理化性状的相关分析[J].土壤专报,1987,41：66-66.
47.徐建民,黄昌勇,安曼等.磺酰脲类除草剂对土壤质量生物学指标的影响[J].中国环境科学,2000,20(6)：491-494.
48.徐永明,蔺启忠,黄秀华等.利用可见光／近红外反射光谱估算土壤总氮含量的实验研究[J].地理与地理信息科学,2005,21(1)：19-22.
49.张甘霖,史学正,龚子同.中国土壤地理学发展的回顾与展望[J].土壤学报,2008,45(5)：792-801.
50.张景雄.空间信息的尺度、不确定性与融合[M].武汉：武汉大学出版社,2008.
51.张美琴.基于成像光谱技术土壤反射特性及剖面有机质分布估计[D],华中农业大学,2012.
52.郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测[J].光谱学与光谱分析,2008,28(5)：1160-1164.
53.郑咏梅,张铁强,张军,等.平滑,导数,基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12)：1546-1548.
54.张雪莲,李晓娜,武菊英,等.不同类型土壤总氮的近红外光谱技术测定研究[J].光谱学与光谱分析,2010,30(4)：906-910.
55. Al-Abbas A, Barr R, Hall J, et al. Spectra of normal and nutrient-deficient maize leaves [J]. Argon,1974,66:16-20.
56. Angstrom. The albedo of various surfaces of ground[J]. Geografiska Annaler,1925,7: 323-342.
57. Baumgardner D, Dye J E, Strapp W. Evaluation of the forward scattering spectrometer probe. II-Corrections for coincidence and dead-time losses[J]. Journal of Atmospheric and Oceanic Technology,1985,2:626-632.
58. Baumgardner M F, Kristof S, Johannsen C J, et al. Effects of organic matter on the multispectral properties of soils[C]. Proceedings of the Indiana Academy of Science. 2013,79:413-422.
59. Bellon-Maurel V, Fernandez-Ahumada E, Palagos B, et al. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy[J]. TrAC Trends in Analytical Chemistry,2010, 29(9):1073-1081.
60. Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal,1995, 59(2):364-372.
61. Bowers S A, Hanks R J. Reflection of radiant energy from soils[J]. Soil Science,1965, 100(2):130-138.
62. Brereton R G. Applied chemometrics for scientists[M]. Wiley,2007.
63. Brown D J, Shepherd K D, Walsh M G, et al. Global soil characterization with VNIR diffuse reflectance spectroscopy[J]. Geoderma,2006,132(3):273-290.
64. Brunet D, Barthes B G, Chotte J-L, et al. Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[J]. Geoderma,2007,139(1): 106-117.
65. Burrough P, Bregt A, Heus M, et al. Complementary use of thermal imagery and spectral analysis of soil properties and wheat yields to reveal cyclic patterns in the Flevopolders[J]. Journal of Soil Science,1985,36(1):141-152.
66. Carpenter S R, Caraco N F, Correll D L, et al. Nonpoint pollution of surface waters with phosphorus and nitrogen[J]. Ecological applications,1998,8(3):559-568.
67. Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science,2002,167(2):110-116.
68. Chang C W, Laird D A, Mausbach M J, et al. Near-infrared reflectance spectroscopy: Principal components regression analyses of soil properties[J]. Soil Science Society of America Journal,2001,65(2):480-490.
69. Changwen D, Jing D, Jianmin Z, et al. Characterization of Greenhouse Soil Properties Using Mid-infrared Photoacoustic Spectroscopy [J]. Spectroscopy Letters,2011, 44(5):359-368.
70. Cipra J E, Baumgardner M F, Stoner E R, et al. Measuring radiance characteristics of soil with a field spectroradiometer[J]. Soil Science Society of America Journal,1971, 35(6):1014-1017.
71. Clark R N. Spectroscopy of rocks and minerals, and principles of spectroscopy [J]. Manual of remote sensing,1999,3:3-58.
72. Clark R N, King T V, Klejwa M, et al. High spectral resolution reflectance spectroscopy of minerals[J]. Journal of Geophysical Research:Solid Earth (1978-2012),1990,95(B8):12653-12680.
73. Cozzolino D, Moron A. Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions[J]. Soil and Tillage Research, 2006,85(1):78-85.
74. Dahm D J, Dahm K D. The physics of near-infrared scattering[J]. Near-Infrared Technology in the Agricultural and Food Industries,2001,2.
75. Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal,1986,50(1):120-123.
76. Daniel K W, Tripathi X K, Honda K. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand)[J]. Australian Journal of Soil Research,2003,41(1):47-59.
77. Daughtry C S T. Discriminating crop residues from soil by shortwave infrared reflectance[J]. Agronomy Journal,2001,93(1):125-131.
78. Dematte J A M, Garcia G J. Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance [J]. Soil Science Society of America Journal,1999,63(2):327-342.
79. Du C, Zhou J. Evaluation of soil fertility using infrared spectroscopy:a review[J]. Environmental Chemistry Letters,2009,7(2):97-113.
80. Fabrizzi K P, Moron A, Garcia F O. Soil carbon and nitrogen organic fractions in degraded vs. non-degraded mollisols in Argentina[J]. Soil Science Society of America Journal,2003,67(6):1831-1841.
81. Fidencio Fidencio P H, Poppi R J, de Andrade J C, et al. Determination of organic matter in soil using near-infrared spectroscopy and partial least squares regression[J]. Communications in Soil Science and Plant Analysis,2002,33(9-10):1607-1615.
82. Fystro G The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods[J]. Plant and Soil,2002,246(2):139-149.
83. Galvao L S, Vitorello I. Variability of laboratory measured soil lines of soils from southeastern Brazil [J]. Remote Sensing of Environment,1998,63(2):166-181.
84. Geladi P, Kowalski B R. Partial least-squares regression:a tutorial[J]. Analytica chimica acta,1986,185:1-17.
85. Geladi P, Macdougall D, Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat[J]. Applied spectroscopy,1985,39(3): 491-500.
86. Goetz A F H, Rowan L C. Geologic remote sensing[J]. Science,1981,211(4484): 781-791.
87. Gerbermann A, Neher D. Reflectance of varying mixtures of a clay soil and sand[J]. Photogrammetric Engineering and Remote Sensing,1979,45:1145-1150.
88. Guerrero C, Rossel R A V, Mouazen A M. Special issue 'Diffuse reflectance spectroscopy in soil science and land resource assessment'[J]. Geoderma,2010, 158(1):1-2.
89. Hahn B, Valentine D. Essential MATLAB for engineers and scientists[M]. Newnes, 2007.
90. Hapke B. Bidirectional reflectance spectroscopy:1. Theory [J]. Journal of Geophysical Research:Solid Earth (1978-2012),1981,86(B4):3039-3054.
91. Hester J B. The relation of soil texture and color to the organic matter content[J]. Soil Science Society of America Journal,1939,3(C):112-114.
92. Huang X, Senthilkumar S, Kravchenko A, et al. Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information[J]. Geoderma,2007,141(1):34-42.
93. Hunt G R. Visible and near-infrared spectra of minerals and rocks:Ⅰ. Silicate minerals[J]. Modern Geology,1970,1:283-300.
94. Jahn B R, Upadhyaya S K. Determination of Soil Nitrate and Organic Matter Content Using Portable, Filter-Based Mid-Infrared Spectroscopy[M]. Dordrecht:Springer, 2010.
95. Janik L, Cozzolino D, Dambergs R, et al. The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks[J]. Analytica Chimica Acta,2007,594(1):107-118.
96. Kim H T. Soil Sampling, Preparation and Analysis[M]. Florida:CRC,2005.
97. Kosmas C, Curi N, Bryant R, et al. Characterization of iron oxide minerals by second-derivative visible spectroscopy [J]. Soil Science Society of America Journal, 1984,48(2):401-405.
98. Kuang B, Mouazen A M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms[J]. European Journal of Soil Science,2011,62(4):629-636.
99. Kuang B, Mouazen A M. Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale[J]. European Journal of Soil Science,2012,63(3):421-429.
100. Kusumo B, Hedley M, Tuohy M, et al. Predicting soil carbon and nitrogen concentrations and pasture root densities from proximally sensed soil spectral reflectance[M]. Proximal Soil Sensing. Springer.2010:177-190.
101. Kusumo B H, Hedley C, Hedley M, et al. The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils[J]. Soil Research,2008,46(7):623-635.
102. Kusumo B H, Hedley M J, Hedley C B, et al. Measuring carbon dynamics in field soils using soil spectral reflectance:prediction of maize root density, soil organic carbon and nitrogen content[J]. Plant and soil,2011,338(1-2):233-645.
103. Li Y, Zhang J. Agricultural diffuse pollution from fertilisers and pesticides in China[J]. Water Science and Technology,1999,39(3):25-32.
104. Lillesand T, Kiefer R, Chipman J. Remote sensing and image analysis[J]. John Wiley and Sons, New York,2000.
105. Liu F, Zhang F, Jin Z, et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy[J]. Analytica Chimica Acta,2008,629(1):56-65.
106. Lu P, Su Y, Niu Z, et al. Geostatistical analysis and risk assessment on soil total nitrogen and total soil phosphorus in the dongting lake plain area, China[J]. Journal of environmental quality,2007,36(4):935-942.
107. Martens H, Naes T. Assessment, validation and choice of calibration method[J]. Multivariate calibration,1989,237-266.
108. Montgomery O L. An investigation of the relationship between spectral reflectance and the chemical, physical and genetic characteristics of soils[M]. Laboratory for Applications of Remote Sensing, Purdue University,1976.
109. Morgan C L, Waiser T H, Brown D J, et al. Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[J]. Geoderma,2009,151(3):249-256.
110. Mouazen A M, Kuang B, De Baerdemaeker J, et al. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J]. Geoderma,2010,158(1-2):23-31.
111. Mouazen A M, Maleki M R, De Baerdemaeker J, et al. On-line measurement of some selected soil properties using a VIS-NIR sensor[J]. Soil and Tillage Research, 2007,93(1):13-27.
112. N(?)s T, Isaksson T, Fearn T, et al. A user friendly guide to multivariate calibration and classification[M]. NIR publications,2002.
113. Neema DL, Shah A. A statistical optical model for light reflection and penetration through sand.[J]. International Journal of Remote Sensing,1987,8(8):1209-1217.
114. O'Neal A M. The effect of moisture on soil color[C]. RB Deemer, PR Dawson, and AR Merz:Papers and Proceedings of the First International Congress of Soil Science. 1927,4:335-342.
115. Parkinson D, Coleman D C. Microbial communities, activity and biomass [J]. Agriculture, Ecosystems & Environment,1991,34(1):3-33.
116. Press W H, Flannery B P, Teukolsky S A, et al. Numerical Recipes in FORTRAN 77: Volume 1, Volume 1 of Fortran Numerical Recipes:The Art of Scientific Computing[M]. Cambridge university press,1992.
117. Ramadan Z, Hopke P K, Johnson M J, et al. Application of PLS and Back-Propagation Neural Networks for the estimation of soil properties[J]. Chemometrics and intelligent laboratory systems,2005,75(1):23-30.
118. Reeves J B, Mccarty G W, Reeves V B. Mid-infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils[J]. Journal of agricultural and food chemistry,2001,49(2):766-772.
119. Rossel R A, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma,2010,158(1):46-54.
120. Rossel R A, Bui E N, De Caritat P, et al. Mapping iron oxides and the color of Australian soil using visible-near-infrared reflectance spectra[J]. Journal of Geophysical Research-Earth Surface(2003-2012),2010,115(F4).
121. Rossel R A, Walvoort D J J, Mcbratney A B, et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma,2006,131(1-2):59-75.
122. Shepherd K D, Walsh M G. Development of reflectance spectral libraries for characterization of soil properties[J]. Soil Science Society of America Journal,2002, 66(3):988-998.
123. Sherman D M, Waite T D. Electronic spectra of Fe (super 3+) oxides and oxide hydroxides in the near IR to near UV[J]. American Mineralogist,1985,70(11-12): 1262-1269.
124. Smiles D, Philip J, Knight J, et al. Hydrodynamic dispersion during absorption of water by soil[J]. Soil Science Society of America Journal,1978,42(2):229-234.
125. Smith K, Jackson D, Withers P. Nutrient losses by surface run-off following the application of organic manures to arable land.2. Phosphorus[J]. Environmental Pollution,2001,112(1):53-60.
126. Stevens A, Van Wesemael B. Soil organic carbon dynamics at the regional scale as influenced by land use history:a case study in forest soils from southern Belgium[J]. Soil Use and Management,2008,24(1):69-79.
127. Stoner E R, Baumgardner M. Characteristic variations in reflectance of surface soils[J]. Soil Science Society of America Journal,1981,45(6):1161-1165.
128. Trauth M H, Gebbers R, Marwan N, et al. MATLAB □ Recipes for Earth Sciences[M]. Springer,2010.
129. Viscarra Rossel R A, Taylor H, Mcbratney A. Multivariate calibration of hyperspectral γ-ray energy spectra for proximal soil sensing [J]. European Journal of Soil Science,2007,58(1):343-353.
130. Viscarra Rossel R A, Cattle S R, Ortega A, et al. In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy [J]. Geoderma,2009, 150(3):253-266.
131. Viscarra Rossel R A, Walvoort D J J, Mcbratney A B, et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma,2006,131(1):59-75.
132. Wetterlind J, Stenberg B. Near-infrared spectroscopy for within-field soil characterization:small local calibrations compared with national libraries spiked with local samples[J]. European Journal of Soil Science,2010,61(6):823-843.
133. Wijaya I, Shibusawa S, Sasao A, et al. Soil parameters maps in paddy field using the real time soil spectrophotometer[J]. Journal of the Japanese Society of Agricultural Machinery,2001,63(3):51-58.
134. Wold S, Albano C, Dunn Ⅲ W J, et al. Pattern recognition:finding and using regularities in multivariate data[J]. Food research and data analysis,1983:147-188.
135. Wold S, Antti H, Lindgren F, et al. Orthogonal signal correction of near-infrared spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998,44(1):175-185.
136. Wold S, Sjostrom M, Eriksson L. PLS-regression:a basic tool of chemometrics[J]. Chemometrics and intelligent laboratory systems,2001,58(2):109-130.
137. Wu G-L, Du G-Z, Liu Z-H, et al. Effect of fencing and grazing on a Kobresia-dominated meadow in the Qinghai-Tibetan Plateau[J]. Plant and Soil,2009, 319(1-2):115-126.
138. Yang H, Kuang B, Mouazen A. Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction[J]. European Journal of Soil Science,2012,63(3):410-420.
139. Zheng J, Tang Z, He Y, et al. Sensitive detection of weak absorption signals in photoacoustic spectroscopy by using derivative spectroscopy and wavelet transform[J]. Journal of Applied Physics,2008,103(9):093116-093116-4.
140. Zimmermann M, Leifeld J, Fuhrer J. Quantifying soil organic carbon fractions by infrared-spectroscopy[J]. Soil Biology and Biochemistry,2007,39(1):224-231.
2.鲍一丹,何勇,方慧,等.土壤的光谱特征及氮含量的预测研究[J].光谱学与光谱分析,2007,27(1)：62-65.
3.程街亮,纪文君,周银,等.土壤二向反射特性及水分含量对其影响研究[J].土壤学报,2011,48(2)：255-262.
4.陈述彭,童庆禧,郭华东.遥感信息机理研究[M].北京：科学出版社,1998.
5.褚小立,王艳斌,陆婉珍.近红外光谱定量校正模型的建立及应用[J].理化检验：化学分册,2008,44(8)：796-800.
6.褚小立,许育鹏,陆婉珍.偏最小二乘法方法在光谱定性分析中的应用研究[J].现代仪器,2007,5：13-15.
7.褚小立,袁洪福,王艳斌,等.近红外稳健分析校正模型的建立(Ⅰ)——样品温度的影响[J].光谱学与光谱分析,2004,24(6)：666-671.
8.从维清,李先友,彭宣贵.潜江水利志[M].1997.
9.戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究[M].遥感文献.北京：科学出版社.1981.315-323.
10.杜昌文.土壤红外光声光谱[M].北京：科学出版社,2012.
11.杜昌文,周健民.傅里叶变换红外光声光谱法测定土壤中有效磷[J].分析化学,2007,35(1)：119-122.
12.高英志,汪诗平,韩兴国,等.退化草地恢复过程中土壤氮素状况以及与植被地上绿色生物量形成关系的研究[J].植物生态学报,2004,28(3)：285-293.
13.高云,汪善勤,李小昱等.三轴向土壤高光谱成像实验台,CN201527394U[P/OL].2010.
14.葛振华,姚贤贺,杜维贤.咸宁地区水利志[M].2001.
15.郭伟良,王丹,宋佳等.近红外光谱法同时快速定量分析蛹虫草菌丝体中4种有效成分[J].光学学报,2011,31(2)：274-281.
16.贺军亮,蒋建军,周生路等.土壤有机质含量的高光谱特性及其反演[J].中国农业科学,2007,40(3)：638-643.
17.何挺,王静,程烨等.土壤氧化铁光谱特征研究[J].地理与地理信息科学,2006,22(2)：30-34.
18.黄康,汪辉君,徐惠荣等.基于最小二乘支持向量机的番茄汁糖酸度分析研究[J].光谱学与光谱分析,2009,29(4)：931-934.
19.黄巧云.土壤学[M].北京：中国农业出版社,2006.
20.季耿善,徐彬彬.遥感波段解译特性[J].土壤专报,1987,114：477-485.
21.纪文君,李曦,李成学等.基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[J].光谱学与光谱分析,2012,32(9)：2393-2398.
22.纪文君,史舟,周清等.几种不同类型土壤的VIS-NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,31(3)：277-282.
23.李亮亮,依艳丽,凌国鑫等.地统计学在土壤空间变异研究中的应用[J].土壤通报,2005,36(2)：265-268.
24.李民赞,韩东海,王秀.光谱分析技术及其应用[M].北京：科学出版社,2006.
25.李硕.基于Vis/NIR光谱不同粒径下土壤碳氮的预测研究[D]：华中农业大学,2010.
26.李硕,汪善勤,张美琴.基于可见-近红外光谱比较主成分回归,偏最小二乘回归和反向传播神经网络对土壤氮的预测研究[J].光学学报,2012,32(8)：289-293.
27.李曦,史舟.基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[C].中国土壤学会第十二次全国会员代表大会暨第九届海峡两岸土壤肥料学术交流研讨会,中国四川成都,2012,153-160.
28.李艳,史舟.地统计学在土壤学中的应用[M].中国农业出版社.2006.
29.刘伟东.高光谱遥感土壤信息提取与挖掘研究[D]：中国科学院研究生院(遥感应用研究所),2002.
30.刘燕德,陈兴苗,欧阳爱国.可见/近红外光谱法无损检测赣南脐橙可溶性固形物[J].光学学报,2008,28(3)：478-481.
31.卢艳丽,白由路,王磊,等.黑土土壤中全氮含量的高光谱预测分析[J].农业工程学报,2010,(1)：256-261.
32.路鹏,魏志强,牛铮.应用特征波段和反射变形差的方法进行土壤属性估算[J].光谱学与光谱分析,2009,29(3)：716-721.
33.彭玉魁,张建新,何绪生等.土壤水分、有机质和总氮含量的近红外光谱分析研究[J].土壤学报,1998,(4)：553-559.
34.瞿明凯.几种地统计学方法在县域土壤空间信息处理上的应用与研究[D]：华中农业大学,2012.
35.日本遥感研究会.遥感精解[M].北京：测绘出版社,2011.
36.宋小宁,赵英时.MODIS图象的云检测及分析[J].中国图象图形学报,2003,(9)：112-126.
37.孙光明,刘飞,张帆等.基于近红外光谱技术检测除草剂胁迫下油菜叶片中脯氨酸含量的方法[J].光学学报,2010,(4)：1192-1196.
38.孙家抦.遥感原理与应用[M].武汉：武汉大学出版社,2003.
39.孙建英,李民赞,郑立华,等.基于近红外光谱的北方潮土土壤参数实时分析[J].光谱学与光谱分析,2006,26(3)：426-429.
40.田高友,袁洪福,褚小立,等.结合小波变换与微分法改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4)：516-520.
41.童庆禧,张兵,郑兰芬.高光谱遥感——原理、技术与应用[M].北京：高等教育出版社.2006.
42.汪善勤,舒宁,张海涛.土壤全氮田间Vis/NIR光谱测定方法研究[J].光谱学与光谱分析,2008,28(4)：808-812.
43.吴景贵,席时权,姜岩.红外光谱在土壤有机质研究中的应用[J].光谱学与光谱分析,1998,18(1)：52-57.
44.谢伯承.基于高光谱遥感不同发生层土壤的光谱信息的提取研究[D],西北农林科技大学,2004.
45.徐彬彬.土壤剖面的反射光谱研究[J].土壤,2000,(6)：281-287.
46.徐彬彬,季耿善.土壤光谱反射特性与理化性状的相关分析[J].土壤专报,1987,41：66-66.
47.徐建民,黄昌勇,安曼等.磺酰脲类除草剂对土壤质量生物学指标的影响[J].中国环境科学,2000,20(6)：491-494.
48.徐永明,蔺启忠,黄秀华等.利用可见光／近红外反射光谱估算土壤总氮含量的实验研究[J].地理与地理信息科学,2005,21(1)：19-22.
49.张甘霖,史学正,龚子同.中国土壤地理学发展的回顾与展望[J].土壤学报,2008,45(5)：792-801.
50.张景雄.空间信息的尺度、不确定性与融合[M].武汉：武汉大学出版社,2008.
51.张美琴.基于成像光谱技术土壤反射特性及剖面有机质分布估计[D],华中农业大学,2012.
52.郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测[J].光谱学与光谱分析,2008,28(5)：1160-1164.
53.郑咏梅,张铁强,张军,等.平滑,导数,基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12)：1546-1548.
54.张雪莲,李晓娜,武菊英,等.不同类型土壤总氮的近红外光谱技术测定研究[J].光谱学与光谱分析,2010,30(4)：906-910.
55. Al-Abbas A, Barr R, Hall J, et al. Spectra of normal and nutrient-deficient maize leaves [J]. Argon,1974,66:16-20.
56. Angstrom. The albedo of various surfaces of ground[J]. Geografiska Annaler,1925,7: 323-342.
57. Baumgardner D, Dye J E, Strapp W. Evaluation of the forward scattering spectrometer probe. II-Corrections for coincidence and dead-time losses[J]. Journal of Atmospheric and Oceanic Technology,1985,2:626-632.
58. Baumgardner M F, Kristof S, Johannsen C J, et al. Effects of organic matter on the multispectral properties of soils[C]. Proceedings of the Indiana Academy of Science. 2013,79:413-422.
59. Bellon-Maurel V, Fernandez-Ahumada E, Palagos B, et al. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy[J]. TrAC Trends in Analytical Chemistry,2010, 29(9):1073-1081.
60. Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal,1995, 59(2):364-372.
61. Bowers S A, Hanks R J. Reflection of radiant energy from soils[J]. Soil Science,1965, 100(2):130-138.
62. Brereton R G. Applied chemometrics for scientists[M]. Wiley,2007.
63. Brown D J, Shepherd K D, Walsh M G, et al. Global soil characterization with VNIR diffuse reflectance spectroscopy[J]. Geoderma,2006,132(3):273-290.
64. Brunet D, Barthes B G, Chotte J-L, et al. Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[J]. Geoderma,2007,139(1): 106-117.
65. Burrough P, Bregt A, Heus M, et al. Complementary use of thermal imagery and spectral analysis of soil properties and wheat yields to reveal cyclic patterns in the Flevopolders[J]. Journal of Soil Science,1985,36(1):141-152.
66. Carpenter S R, Caraco N F, Correll D L, et al. Nonpoint pollution of surface waters with phosphorus and nitrogen[J]. Ecological applications,1998,8(3):559-568.
67. Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science,2002,167(2):110-116.
68. Chang C W, Laird D A, Mausbach M J, et al. Near-infrared reflectance spectroscopy: Principal components regression analyses of soil properties[J]. Soil Science Society of America Journal,2001,65(2):480-490.
69. Changwen D, Jing D, Jianmin Z, et al. Characterization of Greenhouse Soil Properties Using Mid-infrared Photoacoustic Spectroscopy [J]. Spectroscopy Letters,2011, 44(5):359-368.
70. Cipra J E, Baumgardner M F, Stoner E R, et al. Measuring radiance characteristics of soil with a field spectroradiometer[J]. Soil Science Society of America Journal,1971, 35(6):1014-1017.
71. Clark R N. Spectroscopy of rocks and minerals, and principles of spectroscopy [J]. Manual of remote sensing,1999,3:3-58.
72. Clark R N, King T V, Klejwa M, et al. High spectral resolution reflectance spectroscopy of minerals[J]. Journal of Geophysical Research:Solid Earth (1978-2012),1990,95(B8):12653-12680.
73. Cozzolino D, Moron A. Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions[J]. Soil and Tillage Research, 2006,85(1):78-85.
74. Dahm D J, Dahm K D. The physics of near-infrared scattering[J]. Near-Infrared Technology in the Agricultural and Food Industries,2001,2.
75. Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal,1986,50(1):120-123.
76. Daniel K W, Tripathi X K, Honda K. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand)[J]. Australian Journal of Soil Research,2003,41(1):47-59.
77. Daughtry C S T. Discriminating crop residues from soil by shortwave infrared reflectance[J]. Agronomy Journal,2001,93(1):125-131.
78. Dematte J A M, Garcia G J. Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance [J]. Soil Science Society of America Journal,1999,63(2):327-342.
79. Du C, Zhou J. Evaluation of soil fertility using infrared spectroscopy:a review[J]. Environmental Chemistry Letters,2009,7(2):97-113.
80. Fabrizzi K P, Moron A, Garcia F O. Soil carbon and nitrogen organic fractions in degraded vs. non-degraded mollisols in Argentina[J]. Soil Science Society of America Journal,2003,67(6):1831-1841.
81. Fidencio Fidencio P H, Poppi R J, de Andrade J C, et al. Determination of organic matter in soil using near-infrared spectroscopy and partial least squares regression[J]. Communications in Soil Science and Plant Analysis,2002,33(9-10):1607-1615.
82. Fystro G The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods[J]. Plant and Soil,2002,246(2):139-149.
83. Galvao L S, Vitorello I. Variability of laboratory measured soil lines of soils from southeastern Brazil [J]. Remote Sensing of Environment,1998,63(2):166-181.
84. Geladi P, Kowalski B R. Partial least-squares regression:a tutorial[J]. Analytica chimica acta,1986,185:1-17.
85. Geladi P, Macdougall D, Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat[J]. Applied spectroscopy,1985,39(3): 491-500.
86. Goetz A F H, Rowan L C. Geologic remote sensing[J]. Science,1981,211(4484): 781-791.
87. Gerbermann A, Neher D. Reflectance of varying mixtures of a clay soil and sand[J]. Photogrammetric Engineering and Remote Sensing,1979,45:1145-1150.
88. Guerrero C, Rossel R A V, Mouazen A M. Special issue 'Diffuse reflectance spectroscopy in soil science and land resource assessment'[J]. Geoderma,2010, 158(1):1-2.
89. Hahn B, Valentine D. Essential MATLAB for engineers and scientists[M]. Newnes, 2007.
90. Hapke B. Bidirectional reflectance spectroscopy:1. Theory [J]. Journal of Geophysical Research:Solid Earth (1978-2012),1981,86(B4):3039-3054.
91. Hester J B. The relation of soil texture and color to the organic matter content[J]. Soil Science Society of America Journal,1939,3(C):112-114.
92. Huang X, Senthilkumar S, Kravchenko A, et al. Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information[J]. Geoderma,2007,141(1):34-42.
93. Hunt G R. Visible and near-infrared spectra of minerals and rocks:Ⅰ. Silicate minerals[J]. Modern Geology,1970,1:283-300.
94. Jahn B R, Upadhyaya S K. Determination of Soil Nitrate and Organic Matter Content Using Portable, Filter-Based Mid-Infrared Spectroscopy[M]. Dordrecht:Springer, 2010.
95. Janik L, Cozzolino D, Dambergs R, et al. The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks[J]. Analytica Chimica Acta,2007,594(1):107-118.
96. Kim H T. Soil Sampling, Preparation and Analysis[M]. Florida:CRC,2005.
97. Kosmas C, Curi N, Bryant R, et al. Characterization of iron oxide minerals by second-derivative visible spectroscopy [J]. Soil Science Society of America Journal, 1984,48(2):401-405.
98. Kuang B, Mouazen A M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms[J]. European Journal of Soil Science,2011,62(4):629-636.
99. Kuang B, Mouazen A M. Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale[J]. European Journal of Soil Science,2012,63(3):421-429.
100. Kusumo B, Hedley M, Tuohy M, et al. Predicting soil carbon and nitrogen concentrations and pasture root densities from proximally sensed soil spectral reflectance[M]. Proximal Soil Sensing. Springer.2010:177-190.
101. Kusumo B H, Hedley C, Hedley M, et al. The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils[J]. Soil Research,2008,46(7):623-635.
102. Kusumo B H, Hedley M J, Hedley C B, et al. Measuring carbon dynamics in field soils using soil spectral reflectance:prediction of maize root density, soil organic carbon and nitrogen content[J]. Plant and soil,2011,338(1-2):233-645.
103. Li Y, Zhang J. Agricultural diffuse pollution from fertilisers and pesticides in China[J]. Water Science and Technology,1999,39(3):25-32.
104. Lillesand T, Kiefer R, Chipman J. Remote sensing and image analysis[J]. John Wiley and Sons, New York,2000.
105. Liu F, Zhang F, Jin Z, et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy[J]. Analytica Chimica Acta,2008,629(1):56-65.
106. Lu P, Su Y, Niu Z, et al. Geostatistical analysis and risk assessment on soil total nitrogen and total soil phosphorus in the dongting lake plain area, China[J]. Journal of environmental quality,2007,36(4):935-942.
107. Martens H, Naes T. Assessment, validation and choice of calibration method[J]. Multivariate calibration,1989,237-266.
108. Montgomery O L. An investigation of the relationship between spectral reflectance and the chemical, physical and genetic characteristics of soils[M]. Laboratory for Applications of Remote Sensing, Purdue University,1976.
109. Morgan C L, Waiser T H, Brown D J, et al. Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[J]. Geoderma,2009,151(3):249-256.
110. Mouazen A M, Kuang B, De Baerdemaeker J, et al. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J]. Geoderma,2010,158(1-2):23-31.
111. Mouazen A M, Maleki M R, De Baerdemaeker J, et al. On-line measurement of some selected soil properties using a VIS-NIR sensor[J]. Soil and Tillage Research, 2007,93(1):13-27.
112. N(?)s T, Isaksson T, Fearn T, et al. A user friendly guide to multivariate calibration and classification[M]. NIR publications,2002.
113. Neema DL, Shah A. A statistical optical model for light reflection and penetration through sand.[J]. International Journal of Remote Sensing,1987,8(8):1209-1217.
114. O'Neal A M. The effect of moisture on soil color[C]. RB Deemer, PR Dawson, and AR Merz:Papers and Proceedings of the First International Congress of Soil Science. 1927,4:335-342.
115. Parkinson D, Coleman D C. Microbial communities, activity and biomass [J]. Agriculture, Ecosystems & Environment,1991,34(1):3-33.
116. Press W H, Flannery B P, Teukolsky S A, et al. Numerical Recipes in FORTRAN 77: Volume 1, Volume 1 of Fortran Numerical Recipes:The Art of Scientific Computing[M]. Cambridge university press,1992.
117. Ramadan Z, Hopke P K, Johnson M J, et al. Application of PLS and Back-Propagation Neural Networks for the estimation of soil properties[J]. Chemometrics and intelligent laboratory systems,2005,75(1):23-30.
118. Reeves J B, Mccarty G W, Reeves V B. Mid-infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils[J]. Journal of agricultural and food chemistry,2001,49(2):766-772.
119. Rossel R A, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma,2010,158(1):46-54.
120. Rossel R A, Bui E N, De Caritat P, et al. Mapping iron oxides and the color of Australian soil using visible-near-infrared reflectance spectra[J]. Journal of Geophysical Research-Earth Surface(2003-2012),2010,115(F4).
121. Rossel R A, Walvoort D J J, Mcbratney A B, et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma,2006,131(1-2):59-75.
122. Shepherd K D, Walsh M G. Development of reflectance spectral libraries for characterization of soil properties[J]. Soil Science Society of America Journal,2002, 66(3):988-998.
123. Sherman D M, Waite T D. Electronic spectra of Fe (super 3+) oxides and oxide hydroxides in the near IR to near UV[J]. American Mineralogist,1985,70(11-12): 1262-1269.
124. Smiles D, Philip J, Knight J, et al. Hydrodynamic dispersion during absorption of water by soil[J]. Soil Science Society of America Journal,1978,42(2):229-234.
125. Smith K, Jackson D, Withers P. Nutrient losses by surface run-off following the application of organic manures to arable land.2. Phosphorus[J]. Environmental Pollution,2001,112(1):53-60.
126. Stevens A, Van Wesemael B. Soil organic carbon dynamics at the regional scale as influenced by land use history:a case study in forest soils from southern Belgium[J]. Soil Use and Management,2008,24(1):69-79.
127. Stoner E R, Baumgardner M. Characteristic variations in reflectance of surface soils[J]. Soil Science Society of America Journal,1981,45(6):1161-1165.
128. Trauth M H, Gebbers R, Marwan N, et al. MATLAB □ Recipes for Earth Sciences[M]. Springer,2010.
129. Viscarra Rossel R A, Taylor H, Mcbratney A. Multivariate calibration of hyperspectral γ-ray energy spectra for proximal soil sensing [J]. European Journal of Soil Science,2007,58(1):343-353.
130. Viscarra Rossel R A, Cattle S R, Ortega A, et al. In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy [J]. Geoderma,2009, 150(3):253-266.
131. Viscarra Rossel R A, Walvoort D J J, Mcbratney A B, et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma,2006,131(1):59-75.
132. Wetterlind J, Stenberg B. Near-infrared spectroscopy for within-field soil characterization:small local calibrations compared with national libraries spiked with local samples[J]. European Journal of Soil Science,2010,61(6):823-843.
133. Wijaya I, Shibusawa S, Sasao A, et al. Soil parameters maps in paddy field using the real time soil spectrophotometer[J]. Journal of the Japanese Society of Agricultural Machinery,2001,63(3):51-58.
134. Wold S, Albano C, Dunn Ⅲ W J, et al. Pattern recognition:finding and using regularities in multivariate data[J]. Food research and data analysis,1983:147-188.
135. Wold S, Antti H, Lindgren F, et al. Orthogonal signal correction of near-infrared spectra[J]. Chemometrics and Intelligent Laboratory Systems,1998,44(1):175-185.
136. Wold S, Sjostrom M, Eriksson L. PLS-regression:a basic tool of chemometrics[J]. Chemometrics and intelligent laboratory systems,2001,58(2):109-130.
137. Wu G-L, Du G-Z, Liu Z-H, et al. Effect of fencing and grazing on a Kobresia-dominated meadow in the Qinghai-Tibetan Plateau[J]. Plant and Soil,2009, 319(1-2):115-126.
138. Yang H, Kuang B, Mouazen A. Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction[J]. European Journal of Soil Science,2012,63(3):410-420.
139. Zheng J, Tang Z, He Y, et al. Sensitive detection of weak absorption signals in photoacoustic spectroscopy by using derivative spectroscopy and wavelet transform[J]. Journal of Applied Physics,2008,103(9):093116-093116-4.
140. Zimmermann M, Leifeld J, Fuhrer J. Quantifying soil organic carbon fractions by infrared-spectroscopy[J]. Soil Biology and Biochemistry,2007,39(1):224-231.