Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R²) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg^–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg^–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R² and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg^–1) and 0.80 (RMSECV: 0.03 mg kg^–1) for SPS-0.053 and SPS-0.212 samples, respectively.     

Prediction of Soil Properties by Visible and Near-Infrared Reflectance Spectroscopy

Eurasian Soil Science - The major goal of this research was to assess the ability of visible-near-infrared (VIS-NIR) spectroscopy combined with multivariate regressions based on four modeling...     

基于可见–近红外光谱的水稻土全磷反演研究

采用PLSR偏最小二乘法回归结合留一法交叉验证,利用长期定位试验田以及直湖港小流域面上的水稻土土壤样本建立最优模型,研究了不同光谱预处理方式对水稻土全磷可见-近红外高光谱反演精度的影响,探索水稻土全磷光谱反演的可行性;并结合简单相关系数法以及PLSR模型回归系数法分析了水稻土全磷光谱反演的重要波段。结果表明,光谱预处理方法对土壤全磷反演精度的影响不大;基于PLSR建立的水稻土全磷光谱反演模型的校正决定系数达0.85,交叉验证决定系数为0.70,RPD为1.8,有较好的模型精度;440~740 nm为土壤全磷光谱反演的重要波段。利用PLSR对水稻土全磷进行光谱预测是可行的。     

可见-近红外与中红外光谱预测土壤养分的比较研究

对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外（350～2500 nm）与中红外光谱（4000～650 cm-1）对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay（SG）平滑去噪处理,再用标准正态化（SNV）方法进行基线校正,然后分别应用偏最小二乘回归（PLSR）和支持向量机（SVM）两种方法进行建模,探讨了可见-近红外和中红外光谱对土壤全氮（TN）、全磷（TP）、全钾（TK）和碱解氮（AN）、有效磷（AP）、速效钾（AK）共六种土壤养分的预测效果。结果表明：（1）无论基于可见-近红外光谱还是中红外光谱,PLSR模型的预测精度整体均优于SVM模型。（2）中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱,可见-近红外和中红外光谱均可以可靠地预测TN和TK（性能与四分位间隔距离的比率（RPIQ）大于2.10）,中红外光谱可相对较可靠地预测AN（RPIQ=1.87）;但两类光谱对TP、AP和AK的预测效果均较差（RPIQ<1.34）。（3）当变量投影重要性得分（VIP）大于1.5时,PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域,TN的重要波段主要集中于可见-近红外光谱区域的1910和2207 nm附近,中红外光谱区域的1 120、1 000、960、910、770和668 cm-1附近;TK的重要波段主要集中于可见-近红外光谱区域的540、2176、2225和2268 nm附近,中红外光谱区域的1 040、960、910、776、720和668 cm-1附近。因此,中红外光谱技术结合PLSR模型对土壤养分预测效果较好,可快速准确预测土壤TN和TK,可为指导适时施肥提供技术支撑。     

Near-Infrared Reflectance Spectroscopy as a Method for Determining Organic Carbon Concentrations in Soil

Near-infrared reflectance spectroscopy (NIRS) has the potential to be a reliable method for accurately quantifying soil organic carbon (SOC). The objective of this study was to evaluate NIRS as a method for predicting SOC. Partial least squares (PLS) regression was used to predict SOC from soil reflectance values or the first derivative of the reflectance values. Two model validation techniques were evaluated: One was a full cross-validation and in the other 30 percent of the samples were removed from the calibration data set and then tested using the calibrated model. Significant relationships were observed for predicted SOC when compared to laboratory-measured SOC for all models evaluated, regardless of validation technique. The prediction models using the first derivative of the reflectance values outperformed prediction models using the reflectance values alone. In conclusion, NIRS can be used as a quick and accurate method for measuring SOC.     

Hyperspectral Visible and Near-Infrared Determination of Copper Concentration in Agricultural Polluted Soils

The objective of this research was to develop a hyperspectral imaging system for estimating copper concentration in soils as an alternative to standard chemical analyses and to evaluate the analytical accuracy of the system using the visible–near-infrared and near-infrared regions. Hyperspectral imaging is a complex technology providing elevated information content. This work was carried out on air-dried <2-mm soil fraction contaminated by adding 20 mL of copper sulfate at concentrations ranging from 0 to 1000 mg of copper per kg of soil. The samples were scanned in random order and with orientation using visible–near-infrared and near-infrared spectrophotometers. A range of partial least squares regression models derived from the spectral arrays were tested on their ability to predict copper concentration. Significant correlations between predicted and known chemical concentrations were achieved with a correlation coefficient of 0.93 for the visible–near-infrared and 0.77 for the near infrared.     

基于近红外漫反射光谱的云芝提取物多糖含量快速检测研究

为探索快速无损测定云芝提取物中多糖含量的方法,通过采集粉末状云芝提取物近红外光谱,经预处理和波段选择,结合间隔偏最小二乘法(iPLS)和反向区间偏最小二乘法(Bi-PLS),建立并优化云芝提取物多糖含量检测模型。结果表明,光谱区间为9 365.92~8 918.76 cm~(-1)和5 341.48~4 894.32cm~(-1),二阶导数(SD)预处理后,建立的反向区间偏最小二乘法模型更优,其校正决定系数(R_(cal))、校正均方根差(RMSECV)、验证决定系数(R_(val))和验证均方根差(RMSEP)可分别达到0.9089、0.00781、0.9879和0.00292。该模型可以更有效地优选建模所需波段,降低模型复杂度,降低多糖含量的检测成本,提高检测效率,实现云芝提取物多糖含量的快速、无损检测。     

Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy

A successful determination of spectrally active soil components with visible and near infrared reflectance spectroscopy (VIS-NIRS, 400-2500 nm) depends on the selection of an adequate multivariate calibration technique. In this study, the contents of thermolabile organic carbon (C_375 °C), the inert organic C fraction (C_inert) and the sum of both (total soil organic carbon, OC_tot) were estimated with three different methods: partial least squares regression (PLSR) as common standard tool, a combination of PLSR with a genetic algorithm (GA-PLSR) for spectral feature selection, and support vector machine regression (SVMR) with non-linear fitting capacities. The objective was to explore whether these methods show differences concerning their ability to predict soil organic carbon pools from VIS-NIR data. For this analysis, we used both measured spectra and also spectra successively blurred with uniformly distributed white noise. Soil sampling was performed in a floodplain (grassland plots) near Osnabrück (Germany) and comprised a total of 149 samples (109 calibration samples, 40 validation samples); spectral readings were taken in the laboratory with a fibre-optics ASD FieldSpec II Pro FR spectroradiometer.In the external validation, differences between the calibration methods were rather small, none of the applied techniques emerged to be the fittest with superior prediction accuracies. For C_375 °C and OC_tot, all approaches provided reliable estimates with r² (coefficient of determination) greater than 0.85 and RPD values (defined as ratio of standard deviation of measurements to standard error of prediction) greater than 2.5. For C_inert, accuracies dropped to r² < 0.50 and RPD < 1.5; after the removal of two extreme values (n = 38) results improved at best (GA-PLSR) to r² = 0.80 and RPD = 1.98. The noise experiment revealed different responses of the studied approaches. For PLSR and GA-PLSR, increasing spectral noise resulted in successively reduced r² and RPD values. By contrast, SVMR kept high coefficients of determination even at high levels of noise, but increasing noise caused severely biased estimates, so that regression models were less accurate than those of PLSR and GA-PLSR.     

旅游干扰对祁连山风景区土壤性质的影响

本研究以祁连山大野口风景区为研究对象,初步探讨了旅游干扰对该区域土壤性质的影响。研究结果表明:祁连山风景区游客主要集中在5—8月,游客大多选择自驾游方式,以野炊、烧烤式旅游为主。旅游活动对核心区表层土壤的pH、体积质量、含水量及有机质影响较大。随着旅游活动干扰程度的降低,0~10 cm土层土壤pH逐渐上升,10~20 cm土层pH变化不大。土壤体积质量表现出:核心区缓冲区背景区,而土壤含水量则为:背景区缓冲区核心区。核心区和缓冲区土壤表层有机质含量受游客活动影响较大,显著低于背景区,分别占背景区的63.6%和60.1%。表层土壤全氮、全磷、全钾含量均表现出:背景区缓冲区核心区,而10~20 cm土层全量含量差异不大。不同区域土壤速效养分0~10 cm土层含量高于10~20 cm土层含量。核心区表层土壤水解氮、速效磷含量偏高,而深层土壤速效磷含量较高。各个有效微量元素在0~10 cm土层含量普遍高于10~20 cm土层,且在核心区含量均较高。     

近红外漫反射光谱技术快速无损识别灵芝和云芝提取物研究

为了满足食用菌提取物实际生产监管需要,本研究采用近红外漫反射光谱技术对来自不同地区的灵芝和云芝提取物样品进行定性识别研究。在800~2 750nm波段范围,采集灵芝和云芝提取物的漫反射光谱,应用主成分聚类分析和偏最小二乘判别法分别建立识别模型,用146个样品进行建模和48个外部样品集进行验证。结果表明:采用主成分聚类判别分析法,灵芝和云芝提取物的判别界线清晰,正确率达到88.54%;采用偏最小二乘判别法,建立的鉴别分类模型能较好地对灵芝和云芝提取物进行鉴别,校正集和预测集样品的识别正确率均为100%。因此,近红外结合主成分聚类分析和偏最小二乘判别法识别灵芝和云芝提取物是可行的,同时研究结果为灵芝和云芝提取物的快速识别提供了理论依据和使用方法。     

应用拉曼和中红外衰减全反射光谱测定溶液和土壤中的硝酸盐

为比较拉曼光谱和红外光谱在溶液和土壤中硝酸盐含量定量分析的适用性,采用两种光谱对溶液和土壤中的NO3–-N含量(0~200 mg/L)进行快速测定。结果表明,溶液中硝酸盐的拉曼特征峰在1 047 cm–1处,该特征峰强度与NO3–-N浓度成正比,对1 035~1 060 cm-1波段拉曼光谱峰面积和NO3–-N含量进行线性回归,决定系数R2为0.995 4;溶液中硝酸盐的中红外衰减全反射光谱特征吸收峰在1 350 cm–1,吸收峰与NO3–-N含量成正比,特征吸收区1 200~1 500 cm–1峰面积与NO3–-N含量的决定系数R2为0.991 1,表明两种光谱都可用于溶液中硝酸盐的测定。对于土壤样品,红外光谱在1 250~1 500 cm–1处有硝酸盐吸收峰,且吸收峰与NO3–-N含量成正比,峰面积与NO3–-N含量之间的决定系数R2为0.968 4;而对于拉曼光谱,硝酸盐的拉曼峰因受较强干扰导致吸收峰不明显,峰面积与NO3–-N含量之间的决定系数R2仅为0.000 9,表明中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱则很困难。因此,拉曼光谱和中红外衰减全反射光谱都可用于溶液中硝酸盐的测定,且前者灵敏度要高于后者;中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱难以用于土壤中硝酸盐定量分析,这为硝酸盐的快速测定提供理论依据和技术支持。     

Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction

Visible, near-infrared and shortwave-infrared (VNIR-SWIR) spectroscopy is an efficient approach for predicting soil properties because it reduces the time and cost of analyses. However, its advantages are hampered by the presence of soil moisture, which masks the major spectral absorptions of the soil and distorts the overall spectral shape. Hence, developing a procedure that skips the drying process for soil properties assessment directly from wet soil samples could save invaluable time. The goal of this study was twofold:proposing two approaches, partial least squares (PLS) and nearest neighbor spectral correction (NNSC), for dry spectral prediction and utilizing those spectra to demonstrate the ability to predict soil clay content. For these purposes, we measured 830 samples taken from eight common soil types in Israel that were sampled at 66 different locations. The dry spectrum accuracy was measured using the spectral angle mapper (SAM) and the average sum of deviations squared (ASDS), which resulted in low prediction errors of less than 8% and 14%, respectively. Later, our hypothesis was tested using the predicted dry soil spectra to predict the clay content, which resulted in R² of 0.69 and 0.58 in the PLS and NNSC methods, respectively. Finally, our results were compared to those obtained by external parameter orthogonalization (EPO) and direct standardization (DS). This study demonstrates the ability to evaluate the dry spectral fingerprint of a wet soil sample, which can be utilized in various pedological aspects such as soil monitoring, soil classification, and soil properties assessment.     

基于实验室高光谱数据的大围山森林土壤氧化铁全量反演

氧化铁是土壤中含铁矿物的主体,是土壤发育和土壤分类最明显和最有用的指标之一。本文以湖南省大围山森林土壤为研究对象,通过实验室化学成分测定和光谱采集,在光谱预处理及组合变换基础上,采用相关性分析筛选土壤氧化铁全量的敏感波段,并分别建立多元逐步回归和偏最小二乘回归反演模型。结果表明:不同土壤光谱曲线趋势基本一致,均形似陡坎,且在420～580 nm波段,土壤氧化铁全量与光谱反射率呈负相关关系;不同的光谱数据变换方式可以提高光谱与氧化铁全量的相关性,Savitzky-Golay(S-G)平滑和去包络线相结合优于其他预处理方法;土壤氧化铁全量的特征波段主要为392、427、529、523、549、559、565、570、994和1040nm,偏最小二乘回归模型比多元逐步回归模型具有更好的稳定性,适合于快速估算红黄壤区森林土壤氧化铁全量。     

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

The development of rapid techniques, such as hyperspectral spectrophotometry, for investigating arsenic (As) soil contamination could be of great value with respect to conventional methods. This study was conducted to detect As concentrations in artificially polluted soils (from 25 to 1045 mg kg^?1) through hyperspectral visible–near infrared spectrophotometry and to compare two multivariate statistical regression analyses: partial least squares and support vector machines. The correlation coefficient r is greater in the partial least squares in both model (0.93%) and test (0.87%) with respect to support vector machines (0.88% for the model and 0.82% for the test). The most important model variables extracted from the variable importance in projection scores resulted the absorption peaks at 580, 660, 715, and 780 nm. Bands in the visible spectra are not directly associated to As, but the metalloid can interact with the main spectrally active components of soil permitting to multivariate statistical models to screen As concentrations.     

阿尔泰山南坡主要土壤类型及分布

通过样地调查与土壤理化分析相结合对阿尔泰山南部山区的土壤特性进行研究,确定该区域主要的土壤形成特征有有机物质积累特征、粘化特征、钙积特征、盐积特征和相应的化学组成及粘土矿物特征;按土壤发生学分类将主要土壤类型划分为9个土类,从高到低,形成较为完整的土壤垂直谱,并比较了东西部差异;该区域具有温带荒漠草原土壤垂直分布的典型特征,在干旱中温带的山地土壤中有代表性。     

土壤盐渍化高光谱特征分析与建模

基于高光谱遥感技术快速、无损的检测优势,以新疆渭干河-库车河三角洲绿洲为例,探讨利用反射光谱来预测土壤含盐量的可行性。利用野外采集的土壤样本,在实验室内测得了土壤含盐量及原始光谱反射率。利用光谱分析技术计算高光谱指数,与土壤样本含盐量进行相关性分析,筛选出土壤含盐量的光谱特征波段,基于逐步多元线性回归和偏最小二乘回归建立土壤盐分动态监测模型。通过精度检验,结果表明:基于偏最小二乘回归方法,以对数二阶微分光谱特征波段所构建的盐渍化遥感监测模型最优,模型的稳定性和预测精度最高。利用反射光谱来预测土壤含盐量可实现区域尺度上的土壤盐渍化实时监测和评价。     

利用可见光-近红外漫反射光谱技术预测铜冶炼厂周边区域土壤属性

Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam-ples followed by laboratory cumbersome and time-consuming measurements.Visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS) provides a rapid and inexpensive tool to predict various soil properties simultaneously.This study evaluated the suitability of VNIR-DRS for predicting soil properties,including organic matter (OM),pH,and heavy metals (Cu,Pb,Zn,Cd,and Fe),using a total of 254 samples collected in soil profiles near a large copper smelter in China.Partial least square regression (PLSR) with cross-validation was used to relate soil property data to the reflectance spectral data by applying different preprocessing strategies.The performance of VNIR-DRS calibration models was evaluated using the coefficient of determination in cross-validation (R 2 cv) and the ratio of standard deviation to the root mean standard error of cross-validation (SD/RMSE cv).The models provided fairly accurate predictions for OM and Fe (R 2 cv > 0.80,SD/RMSE cv > 2.00),less accurate but acceptable for screening purposes for pH,Cu,Pb,and Cd (0.50 < R 2 cv < 0.80,1.40 < SD/RMSE cv < 2.00),and poor accuracy for Zn (R 2 cv < 0.50,SD/RMSE cv < 1.40).Because soil properties in conta-minated areas generally show large variation,a comparative large number of calibrating samples,which are variable enough and uniformly distributed,are necessary to create more accurate and robust VNIR-DRS calibration models.This study indicated that VNIR-DRS technique combined with continuously enriched soil spectral library could be a nondestructive alternative for soil environment monitoring.     

基于VIS-NIR光谱的互花米草入侵湿地土壤有机碳预测研究

为了研究可见光—近红外光谱技术预测互花米草入侵背景下滨海湿地土壤有机碳含量的潜力,以江苏省典型互花米草湿地为研究对象,利用时空替代法采集15个土壤剖面3个深度共45个土壤样品.在实验室测定土壤光谱及有机碳含量,利用偏最小二乘回归方法建立了基于6种光谱变换的土壤有机碳预测模型,并分析了互花米草入侵年限和土层深度对土壤光谱...     

基于可见/近红外反射光谱的梨树叶片钾含量的快速测定研究

利用可见/近红外反射光谱定量分析技术对梨树鲜叶钾素含量进行快速测定研究。对150个梨树叶片样本进行光谱扫描,其中120个做建模集,30个做验证集。通过对样品的可见/近红外光谱进行多种预处理,并建立钾素预测模型,探讨了可见/近红外光谱数据预处理对预测精度的影响。结果表明,通过原始光谱与S-G(3)平滑相结合的预处理方法,用17个主成分建立的偏最小二乘法模型最好,其交叉验证集和预测集模型的决定系数(R2)分别为0.722 7和0.679 1,交叉验证均方根误差(RMSECV)为1.171,预测的平均相对误差为6.81%,能高效、快速地预测梨树叶片钾素含量,为梨树钾素快速测定提供了新的手段。     

祁连山东段青海云杉林土壤理化特性研究

采集祁连山东段青海云杉林林地0—10cm,10—20cm和20—40cm土层的土壤剖面样品,测定分析了其土壤物理性质、土壤水分状况、土壤全量养分和有机质含量。结果表明,不同土层的土壤容重值均低于1.00g/cm3,且随土层深度的增加呈增加趋势;土壤总孔隙度的变化规律和土壤容重的变化规律相似,不同土层的总孔隙度均在63%以上,通气状况良好;土壤水分含量随深度的增加呈减小趋势,不同土层土壤平均质量含水量最低为18.7%;土壤有机质和土壤全氮含量都达到养分1级;土壤全钾贮量级别为养分2级;土壤全磷含量缺乏,养分级别为4级或5级;土壤pH均值为7.5,表明林地土壤为中性土壤。建议继续加强祁连山东段青海云杉森林生态系统的有效保护和管理。