干旱区生态环境知识资源中心(Arid): 基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究

Arid

	基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究
其他题名	Study on Estimation of Deserts Soil Total Phosphorus Content by Vis-NIR Spectra with Variable Selection
	杨爱霞 1; 丁建丽1 ; 李艳红 2; 邓凯 1
来源期刊	光谱学与光谱分析
ISSN	1000-0593
出版年	2016
卷号	36 期号:3 页码:691-696
中文摘要	以新疆艾比湖湿地保护区采集的300个荒漠土壤样品为研究对象,利用ASD Field Spec~?3 HR光谱仪获取的土壤可见-近红外光谱数据以及化学分析获取的土壤全磷数据为数据源,将原始光谱数据经过卷积平滑、标准正态变量变换以及一阶微分预处理后,采用蚁群-遗传结合区间偏最小二乘法提取荒漠土壤全磷含量特征波长,构建土壤全磷含量偏最小二乘回归预测模型; 并与全谱偏最小二乘、蚁群-区间偏最小二乘、遗传-偏最小二乘模型进行比较。结果表明: 经蚁群-区间偏最小二乘法筛选后,荒漠土壤全磷特征波段为500~700,1 101~1 300,1 501~1 700,1 901~2 100 nm; 进一步采用遗传-区间偏最小二乘法进行变量选择,得到共线性最小的13个有效波长,分别为: 1 621,546,1 259,573,1 572,1 527,564,1 186,1 988,1 541,2 024,1 118和1 191 nm。建模方法比较显示,采用蚁群-遗传结合区间偏最小二乘法选择的特征变量,建立的模型精度最高,其次是遗传算法、蚁群算法和全光谱。蚁群-遗传结合区间偏最小二乘法建立的土壤全磷含量的模型,效验证均方根误差RMSECV以及预测集均方根误差RMSEP分别为0.122和0.108 mg·g~(-1),效验证相关系数R_c以及预测集的相关系数R_p分别为0.535 7,0.555 9。因此,经过卷积平滑、标准正态变量变换以及一阶微分预处理,并利用蚁群-遗传结合区间偏最小二乘法建立的模型不仅简单,而且具有较高的预测精度和较好的稳健性,可以估算荒漠土壤全磷含量。
英文摘要	In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec~?3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According tothe results: through filtering with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500~700, 1 101~1 300, 1 501~1 700, and 1 901~2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1 621, 546, 1 259, 573, 1 572, 1 527, 564, 1 186, 1 988, 1 541,2 024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg·g~(-1), and the related coefficient for cross validation (R_c) and the related coefficient for prediction (R_p) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of ACO-iPLS and GA-iPLS has simple structure, high prediction accuracy andgood robustness, and can be used for estimating the total phosphorus content in desert soil.
中文关键词	光谱学 ; 近红外光谱 ; 蚁群-遗传区间偏最小二乘法 ; 荒漠土壤全磷
英文关键词	Spectroscopy Vis-nir spectra Aco-ga-ipls Deserts soil total phosphorus content
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:5634701
来源机构	新疆大学 ; 新疆师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234440
作者单位	1.新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国; 2.新疆师范大学地理科学与旅游学院, 新疆自治区重点实验室"新疆干旱区湖泊环境与资源实验室", 乌鲁木齐, 新疆 830054, 中国
推荐引用方式 GB/T 7714	杨爱霞,丁建丽,李艳红,等. 基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究[J]. 新疆大学, 新疆师范大学,2016,36(3):691-696.
APA	杨爱霞,丁建丽,李艳红,&邓凯.(2016).基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究.光谱学与光谱分析,36(3),691-696.
MLA	杨爱霞,et al."基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究".光谱学与光谱分析 36.3(2016):691-696.