Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic ...Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.展开更多
Rapid determination of soil organic matter(SOM) using regression models based on soil reflectance spectral data serves an important function in precision agriculture. "Deviation of arch"(DOA)-based regressio...Rapid determination of soil organic matter(SOM) using regression models based on soil reflectance spectral data serves an important function in precision agriculture. "Deviation of arch"(DOA)-based regression and partial least squares regression(PLSR)are two modeling approaches to predict SOM.However,few studies have explored the accuracy of the DOA-based regression and PLSR models.Therefore,the DOA-based regression and PLSR were applied to the visible near-infrared(VNIR) spectra to estimate SOM content in the case of various dataset divisions.A two-fold cross-validation scheme was adopted and repeated 10 000 times for rigorous evaluation of the DOA-based models in comparison with the widely used PLSR model.Soil samples were collected for SOM analysis in the coastal area of northern Jiangsu Province,China.The results indicated that both modelling methods provided reasonable estimation of SOM,with PLSR outperforming DOA-based regression in general.However,the performance of PLSR for the validation dataset decreased more noticeably.Among the four DOA-based regression models,a linear model provided the best estimation of SOM and a cutoff of SOM content(19.76 g kg^(-1)),and the performance for calibration and validation datasets was consistent.As the SOM content exceeded 19.76 g kg^(-1),SOM became more effective in masking the spectral features of other soil properties to a certain extent.This work confirmed that reflectance spectroscopy combined with PLSR could serve as a non-destructive and cost-efficient way for rapid determination of SOM when hyperspectral data were available.The DOA-based model,which requires only 3 bands in the visible spectra,also provided SOM estimation with acceptable accuracy.展开更多
土壤水分的影响是当前采用光谱分析法预测土壤养分含量的关键问题，该文旨在探索去除土壤水分影响、提高有机质高光谱定量估测精度的方法。首先采用地物光谱仪进行湿土和过筛干土的高光谱测试，并进行一阶导数变换；然后，采用奇异值分...土壤水分的影响是当前采用光谱分析法预测土壤养分含量的关键问题，该文旨在探索去除土壤水分影响、提高有机质高光谱定量估测精度的方法。首先采用地物光谱仪进行湿土和过筛干土的高光谱测试，并进行一阶导数变换；然后，采用奇异值分解（singular value decomposition，SVD）结合相关分析筛选土壤水分特征光谱，构建去除水分因素的修正系数，形成湿土光谱的校正光谱；最后基于校正前后湿土光谱，应用偏最小二乘（partial least squares，PLS）回归构建土壤有机质含量的估测模型，并对模型进行验证和比较，分析评价校正前后光谱的预测精度。结果显示：按土壤水分含量梯度划分的2组和全部棕壤及褐土土样共4组样本校正后建模决定系数和均方根误差分别为0.85、0.82、0.74、0.76和0.19%、0.20%、0.23%、0.19%，决定系数提高了0.02～0.09，均方根误差降低了0.01～0.03百分点，验证决定系数、均方根误差和相对分析误差分别为0.78、0.77、0.72、0.76，0.21%、0.15%、0.21%、0.15%和2.03、2.02、1.86、1.98，决定系数提高了0.06～0.15，均方根误差除褐土土样提高0.02百分点外，其他样本组降低了0.01～0.08百分点，相对分析误差提高了0.17～0.43，模型决定系数和相对分析误差得到显著提升；尤其对于土壤水分含量变异系数较小的3组土样，模型从待改进级别提高到性能良好级别，对土壤有机质含量具有较好的预测准确性。说明该方法用于去除土壤水分因素影响和提高有机质含量高光谱估测精度的有效性。展开更多
土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)法,Rank-KS(RKS)和Sample set Partitioning based on joint X-Y distance(SPXY)法,...土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)法,Rank-KS(RKS)和Sample set Partitioning based on joint X-Y distance(SPXY)法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明:KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比(ratio of performance to standard deviation,RPD)>2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下(RPD>2.0),最多减少总校正集70%的样本,对应模型RPD为2.212。当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性。展开更多
利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱...利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。展开更多
基金Project supported by the National Natural Science Foundation of China (No. 30270773), and the Teaching and Research Award Pro-gram for Outstanding Young Teachers in Higher Education Institu-tions & the Specialized Research Fund for the Doctoral Program o
文摘Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.
基金supported by the National Natural Science Foundation of China (No. 41201215)
文摘Rapid determination of soil organic matter(SOM) using regression models based on soil reflectance spectral data serves an important function in precision agriculture. "Deviation of arch"(DOA)-based regression and partial least squares regression(PLSR)are two modeling approaches to predict SOM.However,few studies have explored the accuracy of the DOA-based regression and PLSR models.Therefore,the DOA-based regression and PLSR were applied to the visible near-infrared(VNIR) spectra to estimate SOM content in the case of various dataset divisions.A two-fold cross-validation scheme was adopted and repeated 10 000 times for rigorous evaluation of the DOA-based models in comparison with the widely used PLSR model.Soil samples were collected for SOM analysis in the coastal area of northern Jiangsu Province,China.The results indicated that both modelling methods provided reasonable estimation of SOM,with PLSR outperforming DOA-based regression in general.However,the performance of PLSR for the validation dataset decreased more noticeably.Among the four DOA-based regression models,a linear model provided the best estimation of SOM and a cutoff of SOM content(19.76 g kg^(-1)),and the performance for calibration and validation datasets was consistent.As the SOM content exceeded 19.76 g kg^(-1),SOM became more effective in masking the spectral features of other soil properties to a certain extent.This work confirmed that reflectance spectroscopy combined with PLSR could serve as a non-destructive and cost-efficient way for rapid determination of SOM when hyperspectral data were available.The DOA-based model,which requires only 3 bands in the visible spectra,also provided SOM estimation with acceptable accuracy.
文摘土壤水分的影响是当前采用光谱分析法预测土壤养分含量的关键问题，该文旨在探索去除土壤水分影响、提高有机质高光谱定量估测精度的方法。首先采用地物光谱仪进行湿土和过筛干土的高光谱测试，并进行一阶导数变换；然后，采用奇异值分解（singular value decomposition，SVD）结合相关分析筛选土壤水分特征光谱，构建去除水分因素的修正系数，形成湿土光谱的校正光谱；最后基于校正前后湿土光谱，应用偏最小二乘（partial least squares，PLS）回归构建土壤有机质含量的估测模型，并对模型进行验证和比较，分析评价校正前后光谱的预测精度。结果显示：按土壤水分含量梯度划分的2组和全部棕壤及褐土土样共4组样本校正后建模决定系数和均方根误差分别为0.85、0.82、0.74、0.76和0.19%、0.20%、0.23%、0.19%，决定系数提高了0.02～0.09，均方根误差降低了0.01～0.03百分点，验证决定系数、均方根误差和相对分析误差分别为0.78、0.77、0.72、0.76，0.21%、0.15%、0.21%、0.15%和2.03、2.02、1.86、1.98，决定系数提高了0.06～0.15，均方根误差除褐土土样提高0.02百分点外，其他样本组降低了0.01～0.08百分点，相对分析误差提高了0.17～0.43，模型决定系数和相对分析误差得到显著提升；尤其对于土壤水分含量变异系数较小的3组土样，模型从待改进级别提高到性能良好级别，对土壤有机质含量具有较好的预测准确性。说明该方法用于去除土壤水分因素影响和提高有机质含量高光谱估测精度的有效性。
文摘土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)法,Rank-KS(RKS)和Sample set Partitioning based on joint X-Y distance(SPXY)法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明:KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比(ratio of performance to standard deviation,RPD)>2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下(RPD>2.0),最多减少总校正集70%的样本,对应模型RPD为2.212。当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性。
文摘利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。
