不同粒径处理的土壤全氮含量高光谱特征拟合模型
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摘要
采集新疆北疆棉田385个自然土壤样本,将筛选出的土壤样品分别过2、1、0. 5、0. 15 mm筛并测定其原始光谱反射率,利用支持向量机(Support vector machine,SVM)、偏最小二乘回归(Partial least squares regression,PLSR)和多元逐步线性回归(Stepwise multiple linear regression,SMLR)方法对土壤原始光谱及其12种光谱变换数据分别构建土壤全氮含量的估测模型,并对模型精度进行检验。结果表明,土壤原始光谱特征在各个波段与全氮含量相关性都较差,不同形式的数据变换均能够提高光谱反射率与全氮含量的相关性,同一种数据变换形式在不同粒径处理中最大相关系数所对应的波段位置差异不大。从不同粒径处理的拟合精度来看,过筛粒径越小对全氮含量的估测精度越高,3种方法的最优拟合模型都是过0. 15 mm筛的处理,其中SVM方法采用(lgR)'变换后,构建模型R2c为0. 898 7,RMSEc为0. 018 1,RPD为2. 704 9,PLSR和SMLR方法均采用R'变换,构建模型的R2c分别为0. 852 0和0. 819 6,RMSEc分别为0. 041 3和0. 043 6,RPD分别为2. 554 9和2. 437 4,3种方法在该过筛处理下均能够很好地估测土壤全氮含量。用未参与建模的样本对3种最优模型进行验证,SVM、PLSR和SMLR模型的检验R2分别为0. 822 9、0. 771 5和0. 705 4,SVM方法优于PLSR和SMLR,模型具有较好的精度和稳定性,从模型的预测误差来看,土壤全氮含量越低其预测误差也越大,在氮素含量较低的情况下无法直接通过光谱反射特征准确反演。
Hyperspectral remote sensing technology is a powerful tool in the analysis of soil compositions as well as soil physical and chemical properties. Totally 385 natural soil samples were collected from cotton fields in North Xinjiang Province,the selected soil samples according to the total nitrogen content were processed by 2 mm,1 mm,0. 5 mm and 0. 15 mm sieves, and their spectral reflectance characteristics were measured. After the transformation of spectral data with twelve forms,the spectral inversion models of soil nitrogen content were established based on support vector machine( SVM),partial least squares regression( PLSR) and stepwise multiple linear regression( SMLR),and the accuracy and universality of the model were tested. The results showed that there was no significant correlation between the original spectral characteristics and soil nitrogen content,and which can be improved by different data transformations. In the same data transformation,there was no obvious difference in the band position corresponding to the maximum correlation coefficient in different particle size processing. According to the fitting accuracy of different particle size treatments,the smaller the particle size of the sieve was,the higher the precision of the total nitrogen content was,the optimal fitting models of the three methods were all processed by 0. 15 mm sieve treatment,the SVM method used( lgR) ' transformation,the model Rc2 was 0. 898 7,the RMSEcwas 0. 018 1 and the RPD was 2. 704 9,the PLSR and the SMLR methods used R' transformation,the Rc2 were 0. 852 0 and 0. 819 6,the RMSEc was 0. 041 3 and 0. 043 6,and the RPD was 2. 554 9 and 2. 437 4,respectively. The optimal model was checked with the samples which were not involved in building model and the R2 of SVM,PLSR and SMLR were 0. 882 9,0. 771 5 and 0. 705 4,respectively. From the prediction error of the model,the lower the soil total nitrogen content was,the greater the prediction error was,it was impossible to accurately estimate the soil total nitrogen content by spectral reflectance characteristics.
Hyperspectral remote sensing technology is a powerful tool in the analysis of soil compositions as well as soil physical and chemical properties. Totally 385 natural soil samples were collected from cotton fields in North Xinjiang Province,the selected soil samples according to the total nitrogen content were processed by 2 mm,1 mm,0. 5 mm and 0. 15 mm sieves, and their spectral reflectance characteristics were measured. After the transformation of spectral data with twelve forms,the spectral inversion models of soil nitrogen content were established based on support vector machine( SVM),partial least squares regression( PLSR) and stepwise multiple linear regression( SMLR),and the accuracy and universality of the model were tested. The results showed that there was no significant correlation between the original spectral characteristics and soil nitrogen content,and which can be improved by different data transformations. In the same data transformation,there was no obvious difference in the band position corresponding to the maximum correlation coefficient in different particle size processing. According to the fitting accuracy of different particle size treatments,the smaller the particle size of the sieve was,the higher the precision of the total nitrogen content was,the optimal fitting models of the three methods were all processed by 0. 15 mm sieve treatment,the SVM method used( lgR) ' transformation,the model Rc2 was 0. 898 7,the RMSEcwas 0. 018 1 and the RPD was 2. 704 9,the PLSR and the SMLR methods used R' transformation,the Rc2 were 0. 852 0 and 0. 819 6,the RMSEc was 0. 041 3 and 0. 043 6,and the RPD was 2. 554 9 and 2. 437 4,respectively. The optimal model was checked with the samples which were not involved in building model and the R2 of SVM,PLSR and SMLR were 0. 882 9,0. 771 5 and 0. 705 4,respectively. From the prediction error of the model,the lower the soil total nitrogen content was,the greater the prediction error was,it was impossible to accurately estimate the soil total nitrogen content by spectral reflectance characteristics.
引文
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[2]史舟,梁宗正,杨媛媛,等.农业遥感研究现状与展望[J/OL].农业机械学报,2015,46(2):247-260.SHI Zhou, LIANG Zongzheng, YANG Yuanyuan, et al. Status and prospect of agricultural remote sensing[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(2):247-260. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20150237&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2015. 02. 037.(in Chinese)
[3]李民赞,郑立华,安晓飞,等.土壤成分与特性参数光谱快速检测方法及传感技术[J/OL].农业机械学报,2013,44(3):73-87.LI Minzan,ZHENG Lihua,AN Xiaofei,et al. Fast measurement and advanced sensors of soil parameters with NIR spectroscopy[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(3):73-87. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20130315&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298.2013. 03. 015.(in Chinese)
[4]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,2010,26(1):256-261.LU Yanli,BAI Youlu,WANG Lei,et al. Determination for total nitrogen content in black soil using hyperspectral data[J].Transactions of the CSAE,2010,26(1):256-261.(in Chinese)
[5]侯艳军,塔西甫拉提·特依拜,买买提·沙吾提,等.荒漠土壤有机质含量高光谱估算模型[J].农业工程学报,2014,30(16):113-120.HOU Yanjun,TASHPOLAT·Tiyip,MAMAT·Sawut,et al. Estimation model of desert soil organic matter content usinghyperspectral data[J]. Transactions of the CSAE,2014,30(16):113-120.(in Chinese)
[6]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.JI Wenjun,SHI Zhou,ZHOU Qing,et al. VIS-NIR reflectance spectroscopy of the organic matter in several types of soils[J].Journal of Infrared and Millimeter Waves,2012,31(3):277-282.(in Chinese)
[7]张娟娟,田永超,姚霞,等.基于近红外光谱的土壤全氮含量估算模型[J].农业工程学报,2012,28(12):183-188.ZHANG Juanjuan,TIAN Yongchao,YAO Xia,et al. Estimating model of soil total nitrogen content based on near-infraredspectroscopy analysis[J]. Transactions of the CSAE,2012,28(12):183-188.(in Chinese)
[8]刘秀英,王力,宋荣杰,等.黄绵土风干过程中土壤含水率的光谱预测[J/OL].农业机械学报,2015,46(4):266-272.LIU Xiuying,WANG Li,SONG Rongjie et al. Prediction of soil moisture content in air-drying loess using spectral data[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(4):266-272. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20150439&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298.2015. 04. 039.(in Chinese)
[9] FAN R Q,YANG X M,ZHANG X P,et al. Prediction of soil organic carbon in different soil fractions of black soils inNortheast China using near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis,2012,2(2):349-353.
[10]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J/OL].农业机械学报,2017,48(3):164-172.YE Qing, JIANG Xueqing, LI Xican, et al. Comparison on inversion model of soil organic matter content based onhyperspectral data[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):164-172. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20170321&journal_id=jcsam. DOI:10. 6041/j.issn. 1000-1298. 2017. 03. 021.(in Chinese)
[11]张瑶,李民赞,郑立华,等.基于近红外光谱分析的土壤分层氮素含量预测[J].农业工程学报,2015,31(9):121-126.ZHANG Yao,LI Minzan,ZHENG Lihua,et al. Prediction of soil total nitrogen content in different layers based on nearinfrared spectral analysis[J]. Transactions of the CSAE,2015,31(9):121-126.(in Chinese)
[12] FORRESTER S T,JANIK L J,SORIANO-DISLA J M,et al. Use of handheld mid-infrared spectroscopy and partial least-squares regression for the prediction of the phosphorus buffering index in Australian soils[J]. Soil Research,2015,53(1):67-80.
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