作者机构：
[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yu Lei] C;Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
关键词：
土壤高光谱;小波系数;小波能量特征;土壤有机质;水稻土
摘要：
土壤高光谱在采集过程中难以避免噪声干扰, 造成高光谱数据信噪比较低, 影响土壤有机质含量估测精度。 尝试探究小波能量特征方法, 降低高光谱噪声, 提升土壤有机质含量高光谱估测模型性能。 选取湖北省潜江市运粮湖管理区为试验区, 于2016年9月采集80份深度为0~20 cm的水稻土样本; 土壤样本经风干、 碾磨、 过筛等一系列处理后, 在实验室内采集样本光谱, 并通过重铬酸钾-外加热法测定土壤有机质含量; 利用浓度梯度法, 将总体样本集(80个样本)划分为建模集(54个样本)和验证集(26个样本); 以mexh为小波基函数进行连续小波变换(continuous wavelet transformation), 将土壤高光谱转换为10个分解尺度的小波系数(wavelet coefficients); 逐尺度计算小波系数的均方根作为小波能量特征(energy features), 将10个尺度的小波能量特征组成小波能量特征向量(energy features vector); 逐尺度逐波长计算小波系数与有机质含量的相关系数, 将达到极显著水平(p<0.01)的小波系数作为敏感小波系数(sensitive wavelet coefficients); 利用主成分分析法(principal component analysis)分别计算土壤高光谱和小波能量特征向量的各主成分载荷, 通过比较两者第一主成分贡献率的高低和两者前三个主成分得分的空间离散程度, 判断小波能量特征转换前后建模自变量的主成分信息变化趋势; 基于小波能量特征向量和敏感小波系数分别建立多元线性回归和偏最小二乘回归土壤有机质含量估测模型。 结果表明, 土壤有机质含量越高, 全波段反射率越低, 但不同土样的光谱反射率曲线特征相似, 近红外部分的反射率(780~2 400 nm)高于可见光部分(400~780 nm); 敏感小波系数对应的波长为494, 508, 672, 752, 1 838和2 302 nm; 土壤高光谱与小波能量特征向量的第一主成分贡献率分别为96.28%和99.13%, 小波能量特征向量的前三个主成分散点较土壤高光谱的主成分散点在空间上更为聚集, 表明小波能量特征方法有效减少了噪声影响; 比较全部土壤有机质含量估测模型, 以小波能量特征向量为自变量的多元线性回归模型具有最佳估测精度, 其验证集决定系数(R2)、 相对估测误差(RPD)和均方根误差(RMSE)分别为0.77, 1.82和0.82。 因此, 小波能量特征方法既能够提高数据的信噪比, 提升土壤有机质含量的估测精度, 又实现了土壤高光谱数据降维, 降低了模型复杂度, 可用于土壤有机质含量快速测定和土壤肥力动态监测等研究。 There is no silver-bullet solution of eliminating noise during the acquisition process of soil hyperspectral. As the noise interference, the observations of soil spectra are in low signal-to-noise ratio, which affects the estimation accuracy of soil organic matter content. This paper attempts to adopt the wavelet energy features method to reduce the noise in soil hyperspectral and improve the estimation accuracy of soil organic matter content. The Yunlianghu Farm of Qianjiang City, Hubei Province, located in the hinterland of Jianghan Plain, was selected as the experimental area, and 80 samples of paddy soil with a depth of 0~20 cm were collectedin September 2016. After pretreatment (air drying, milling, sieving), soil sample spectral reflectance and determine soil organic matter contentwere collected in the laboratory. The concentration gradient method was employed to divide the whole sample set (80 samples) into a calibration set (54 samples) and a validation set (26 samples). Continuous wavelet transformation was performed using mexh as a wavelet basis function, transforming the soil hyperspectral into sensitive wavelet coefficients of 10 decomposition scales. Then the root mean square of the wavelet coefficients was calculated scale by scaleto define wavelet energy features, and the wavelet energy features vector was determined by the wavelet energy features. The correlation coefficients between the wavelet coefficients and the organic matter content were calculated scale by scale and wavelength by wavelength, and the wavelet coefficient which reaches the extremely significant level (p<0.01) was defined as the sensitive wavelet coefficients. Principal component analysis was conducted to calculate the principal component loads of soil hyperspectral and wavelet energy features vector, respectively. The trend of principal component information of modeled independent variables before and after wavelet energy features transformation would be judged from the difference between the first principal component contribution rate and the spatial dispersion of the first three principal component scores degree. Moreover, regression models were established based on wavelet energy features vector and sensitive wavelet coefficients, respectively, to estimate soil organic matter content. The results showed that with the increase of soil organic matter content, the full-band reflectance decreased, but the spectral reflectance curves of different soil samples were similar, and the reflectance in the near-infrared bands (780~2 400 nm) was higher than that in the visible bands (400~780 nm). The sensitive wavelet coefficients corresponded to wavelengths of 494, 508, 672, 752, 1 838, and 2 302 nm. The first principal component contribution rates of soil hyperspectral and wavelet energy features vector were 96.28% and 99.13%, respectively. The first three principal component scatter points of wavelet energy features vector were more spatially aggregated than those of soil hyperspectral, which demonstrated that the wavelet energy features method effectively reduces the influence of noise. Comparing the estimation models of soil organic matter content, the multivariate linear regression model adopting wavelet energy features vector as the independent variable had the highest estimation accuracy, whose determination coefficients (R2), relative estimate deviation (RPD), and the root mean squared error (RMSE) of validation set were 0.77, 1.82, and 0.82, respectively. Therefore, the wavelet energy features method which is proved to raise the signal-to-noise ratio of the data without adding to the complexity, could improve the estimation accuracy of soil organic matter and realize the dimensional reduction of soil hyperspectral data. This method can be applied to studies like on-the-go soil properties measurement and soil quality monitoring.
关键词：
Estimation mechanism;Predictive model;Proximal soil sensing;Soil heavy metal;Spectral derivative
摘要：
Heavy metal contamination of peri-urban agricultural soil is detrimental to soil environmental quality and human health. A rapid assessment of soil pollution status is fundamental for soil remediation. Heavy metals can be monitored by visible and near-infrared spectroscopy coupled with chemometric models. First and second derivatives are two commonly used spectral preprocessing methods for resolving overlapping peaks. However, these methods may lose the detailed spectral information of heavy metals. Here, we proposed a fractional-order derivative (FOD) algorithm for preprocessing reflectance spectra. A total of 170 soil samples were collected from a typical peri-urban agricultural area in Wuhan City, Hubei Province. The reflectance spectra and lead (Pb) and zinc (Zn) concentrations of the samples were obtained in the laboratory. Two calibration methods, namely, partial least square regression and random forest (RF), were used to establish the relation between the spectral data and the two heavy metals. In addition, we aimed to explore the use of spectral estimation mechanism to predict the Pb and Zn concentrations. Three model evaluation parameters, namely, coefficient of determination (R(2)), root mean squared error, and ratio of performance to inter-quartile range (RPIQ), were used. Overall, the spectral reflectance decreased with the increase in Pb and Zn contents. The FOD algorithm gradually removed spectral baseline drifts and overlapping peaks. However, the spectral strength slowly decreased with the increase in fractional order. High fractional-order spectra underwent more spectral noises than low fractional-order spectra. The optimal prediction accuracies were achieved by the 0.25- and 0.5-order reflectance RF models for Pb (validation R(2)=0.82, RPIQ=2.49) and Zn (validation R(2)=0.83, RPIQ=2.93), respectively. A spectral detection of Pb and Zn mainly relied on their covariation with soil organic matter, followed by Fe. In summary, our results provided theoretical bases for the rapid investigation of Pb and Zn pollution areas in peri-urban agricultural soils.
关键词：
Visible and near infrared spectroscopy;Soil organic matter;Fractional order derivative;Local modeling;Memory-based learning
摘要：
Visible and near-infrared (Vis NIR) spectroscopy is used to estimate soil organic matter (SOM). Spectral preprocessing techniques and multivariate modeling methods play important roles in the quantitative analysis of SOM. First and second derivatives (i.e., the conventional integer order derivatives) are commonly used spectral derivatives, which, however, may ignore some detailed spectral information regarding SOM. Here, we presented a fractional order derivative (FOD) method to preprocess the reflectance spectra. Robust modeling methods are still required for accurate estimation of SOM. Local modeling technique (memory-based learning, MBL) was introduced to compare with two global modeling approaches, namely, partial least square (PIS) and random forest (RF). A total of 535 topsoil samples were gathered from Hubei Province, Central China, with their reflectance spectra and SOM contents measured in the laboratory. FOD was allowed to vary from 0 to 2 with an increment of 0.25 at each step. Coefficient of determination (R-2) and ratio of the performance to deviation (RPD) were employed as performance statistics during validation. Results showed that with the increase of derivative order, the baseline drifts and overlapping peaks were gradually removed but the spectral strength decreased concurrently. Higher derivative order reflectance (i.e., 1.5-order, 1.75-order, and 2-order reflectance) were more susceptible to spectral noise interferences. The correlation coefficient of SOM with FOD processed spectra at some specific wavelengths was larger than that with the original reflectance. MBL performed better than PLS and RF, regardless of FOD transformation. Calibration with 0.25-order reflectance and MBL provided the most accurate estimation of SOM, with an RPD of 2.23. Our results confirm the effectiveness of FOD and local modeling (MBL) in the development of Vis NIR models for SOM estimation.
摘要：
Accurate estimation of soil organic matter (SOM) is essential in understanding the spatial distribution of SOM to identify areas that need fertilization and the required grade of those fertilizers. Visible and near-infrared spectroscopy is a promising alternative to time consuming and costly conventional soil assessment methods. However, this approach is highly dependent on selecting suitable preprocessing strategies and data mining techniques for regression analysis. In this study, 2D correlation coefficients, including ratio, difference, and normalized difference indices, were introduced to select sensitive spectral parameters. The performance of extreme learning machine (ELM) was evaluated via comparison with that of support vector machine (SVM) for SOM estimation. A total of 257 soil samples were collected from Hubei Province, Central China, with SOM contents and reflectance spectra measured in the laboratory. Five spectral pretreatments, except for the raw spectra, were applied. SVM and ELM models were calibrated on spectral parameters selected by one-dimensional and 2D correlation coefficients and subsequently applied to predict SOM. Results showed that 2D correlation coefficient can effectively highlight the detailed SOM information compared with that of one-dimensional correlation coefficient. The ELM models yielded superior predictability relative to SVM models in all eight established models. The most excellent estimation accuracy was obtained by 2D ratio index and ELM (TRI-ELM) method, with an independent validation R-2 and a ratio of performance to interquartile range of 0.83 and 3.49, respectively. The SOM fertility levels of predicted SOM showed that TRI-ELM method presented the largest similarity to laboratory-measured SOM levels, and misclassified samples were all concentrated within one error level. In summary, our study indicates that the TRI-ELM model is a rapid, inexpensive, and relatively accurate method for identifying SOM fertility level. (c) 2018 Elsevier B.V. All rights reserved.
关键词：
Fuzzy k-means clustering;Normalized soil moisture index;Partial least square-support vector machine;Soil moisture;Soil organic matter;Visible and near-infrared spectroscopy
摘要：
Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in themoist conditions tominimize the influence of SMis growing. The objective of this study was to investigate the transferability of two approaches, SM-based clustermethod with known SM(classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM(utilizing NSMI to indicate the SMand establish models separately), to predict SOM directly inmoist soil spectra. One hundred and twenty one soil sampleswere collected fromCentral China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOMconcentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOMprediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SMdata. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SMled to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SMclusters in known SMstates could improve the performance of PLS-SVMmodels to acceptable prediction accuracies (R<sup>2</sup><inf>cv</inf>= 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM(RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOMprediction without knowing the SMexplicitly, and this method is also easy to carry out and only requires spectral information.<br/>
期刊：
Soil Science Society of America Journal,2018年82(5):1231-1242 ISSN：0361-5995
通讯作者：
Chen, Yiyun;Liu, Yanfang
作者机构：
[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Cheng, Hang; Liu, Yaolin; Liu, Yi] Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;[Hong, Yongsheng; Cheng, Hang; Chen, Yiyun] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.;[Zhang, Yong] Anhui Univ Finance & Econ, Sch Publ Finance & Admin, Bengbu 233030, Peoples R China.;[Yu, Lei] Cent China Normal Univ, Sch Urban & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;[Yu, Lei] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Chen, YY; Liu, YF] W;[Chen, Yiyun] C;Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.;Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
摘要：
<jats:sec><jats:label /><jats:p><jats:list list-type="bullet">
<jats:title>Core Ideas</jats:title>
<jats:list-item><jats:p>Vis‐NIR spectroscopy was applied to estimate soil organic carbon in two different study areas.</jats:p></jats:list-item>
<jats:list-item><jats:p>Model developed in one study area couldn't be transferred in the other area.</jats:p></jats:list-item>
<jats:list-item><jats:p>We used spiking strategy to adapt the calibration models to target area characteristics.</jats:p></jats:list-item>
<jats:list-item><jats:p>|<jats:italic>s</jats:italic><jats:sup>2</jats:sup>–σ<jats:sup>2</jats:sup>| could be used to identify the optimal spiking samples to improve model transferability.</jats:p></jats:list-item>
</jats:list></jats:p><jats:p>Visible and near infrared (Vis‐NIR) spectroscopy technique has been shown to be a cost‐effective alternative for rapidly analyzing soil organic carbon (SOC). However, great challenges remain when applying a Vis‐NIR model for SOC estimation developed in one study area to other study areas without further calibration. The scope of this study was to use spiking strategy to improve the transferability of Vis‐NIR models between two study areas. Specifically, we explored the optimal spiking subset by adding different quantities of spiking samples to construct different‐sized models, and the strategy of spiking with extra‐weighting was used for comparison. Soil data was acquired in two independent study areas (WH area and HH area) in Hubei Province, Central China. The reflectance spectra and SOC contents were measured in the laboratory. Partial least squares regression (PLSR) was used for model calibration. The representativeness of the spiking samples was assessed through the absolute difference between the selected sample variance (s<jats:sup>2</jats:sup>) and the original variance (σ<jats:sup>2</jats:sup>) in the principal component space derived from soil spectra. Results showed that the initial models yielded successful SOC predictions for the soil samples from the same area as the calibration samples, but failed in those samples from the other area. Spiking improved the model transferability between these two study areas. Approximately 33%/48% of the HH/WH calibration set was required as spiking samples in model calibrations and applications in the other area. Spiking with extra‐weighting was of limited use in small‐sized spectral libraries. The use of |<jats:italic>s</jats:italic><jats:sup>2</jats:sup>–σ<jats:sup>2</jats:sup>| is potentially effective in identifying the optimal spiking samples to improve model transferability between different small‐sized study areas in the Vis‐NIR assessments of SOC.</jats:p></jats:sec>
作者机构：
[于雷; 章涛; 朱亚星; 周勇; 夏天; 聂艳] Hubei Provincial Key Laboratory for the Analysis and Simulation of Geographical Process, Central China Normal University, Wuhan;430079, China;College of Urban and Environmental Science, Central China Normal University, Wuhan;Research Institute for Sustainable Development of CCNU, Wuhan;[于雷; 章涛; 朱亚星; 周勇; 夏天; 聂艳] 430079, China <&wdkj&> College of Urban and Environmental Science, Central China Normal University, Wuhan
关键词：
光谱分析;作物;叶绿素;高光谱;特征波长变量;迭代和保留信息变量法;大豆
摘要：
在植物叶绿素特征波长变量筛选过程中,与叶绿素关系较弱的波长变量极易被忽略,导致这些弱信息变量包含叶绿素的有效信息丢失,因此,确定叶片光谱中弱信息变量对揭示叶绿素高光谱响应规律具有重要意义。该研究以江汉平原大豆鼓粒期的叶片为研究对象,采集80组大豆叶片高光谱和SPAD(soil and plant analyzer development)值,分析SPAD值与大豆叶片反射率相关关系和光谱波长变量自相关关系,基于迭代和保留信息变量法(iteratively retains informative variables,IRIV)筛选大豆叶片的特征波长变量,建立偏最小二乘回归(partial least squares regression,PLSR)和支持向量机(support vector machine,SVM)模型估测SPAD值。结果表明,大豆叶片SPAD值与光谱反射率在可见光波段具有极显著负相关,在近红外波段存在不显著的正相关性(P>0.01);可见光、近红外2波段的波长变量之间相关性较弱,但2波段内变量之间的相关性较强;基于IRIV算法确定了大豆叶绿素的特征波长变量,利用特征波长变量建立的估测模型的估测能力高于仅利用强信息波长变量建立的估测模型,表明弱信息变量对估测叶片SPAD值具有重要意义; IRIV-SVM模型估测能力最优,验证集R~2和相对分析误差(RPD)分别为0.73、1.82。该文尝试证明了光谱中弱信息变量的重要性,为揭示叶片高光谱响应机理提供了理论依据。
摘要：
【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0-20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S~0(122个样本)、训练集S~1(60个样本)、验证集S~2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S~1和S~2样本集的9个梯度SM(0%-32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R~2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R_(pre)~2、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R_(pre)~2、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。
