冻结状态下盐渍化土壤中水溶性盐基离子含量高光谱反演
S127%S153.6; 为探究采用高光谱技术反演冻结状态土壤水溶性盐基离子含量的可行性,该研究针对河套灌区盐渍化土壤,测定土壤在冻结与未冻结状态下的光谱和主要水溶性盐基离子含量(HCO3-、Cl-、CO32-、SO42-、K+、Na+、Ca2+、Mg2+),光谱经标准正态变量变换(standard normal variable,SNV)和变量投影重要性法(variable importance in projection,VIP)筛选出敏感波段后,采用偏最小二乘回归法(partial least squares regression,PLSR)、支持向量机回归法(support vecto...
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Published in | 农业工程学报 Vol. 39; no. 1; pp. 100 - 107 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | Chinese |
Published |
西北农林科技大学水利与建筑工程学院,杨凌 712100%西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌 712100
2023
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Subjects | |
Online Access | Get full text |
ISSN | 1002-6819 |
DOI | 10.11975/j.issn.1002-6819.202209132 |
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Abstract | S127%S153.6; 为探究采用高光谱技术反演冻结状态土壤水溶性盐基离子含量的可行性,该研究针对河套灌区盐渍化土壤,测定土壤在冻结与未冻结状态下的光谱和主要水溶性盐基离子含量(HCO3-、Cl-、CO32-、SO42-、K+、Na+、Ca2+、Mg2+),光谱经标准正态变量变换(standard normal variable,SNV)和变量投影重要性法(variable importance in projection,VIP)筛选出敏感波段后,采用偏最小二乘回归法(partial least squares regression,PLSR)、支持向量机回归法(support vector regression,SVR)和极限学习机法(extreme learning machine,ELM)构建基于特征光谱的土壤离子含量高光谱反演模型,并对比冻结与未冻结状态反演模型的精度.结果表明:在冻结状态下,不同离子的反演精度存在很大差异,其中Cl-和K+的预测精度极高(相对分析误差大于2.5),SO42-、Ca2+和Na+预测精度较好(相对分析误差在2.0~2.5之间),其余离子预测效果较差;3种回归方法中,ELM模型精度最高,SVM模型次之,PLSR模型最低.冻结和未冻结状态下离子的最优反演模型相同,但冻结状态下Cl-、SO42-、K+和Na+反演精度比未冻结状态高,而Ca2+和Mg2+反演精度比未冻结状态低且Mg2+的反演精度差别最大.各离子最优反演模型与未冻结状态下的相对分析误差相比变化为-34.45%~24.43%.该研究构建的VIP-ELM模型为季节性冻土区盐渍化土壤盐基离子的高光谱监测提供了一种可靠途径. |
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AbstractList | S127%S153.6; 为探究采用高光谱技术反演冻结状态土壤水溶性盐基离子含量的可行性,该研究针对河套灌区盐渍化土壤,测定土壤在冻结与未冻结状态下的光谱和主要水溶性盐基离子含量(HCO3-、Cl-、CO32-、SO42-、K+、Na+、Ca2+、Mg2+),光谱经标准正态变量变换(standard normal variable,SNV)和变量投影重要性法(variable importance in projection,VIP)筛选出敏感波段后,采用偏最小二乘回归法(partial least squares regression,PLSR)、支持向量机回归法(support vector regression,SVR)和极限学习机法(extreme learning machine,ELM)构建基于特征光谱的土壤离子含量高光谱反演模型,并对比冻结与未冻结状态反演模型的精度.结果表明:在冻结状态下,不同离子的反演精度存在很大差异,其中Cl-和K+的预测精度极高(相对分析误差大于2.5),SO42-、Ca2+和Na+预测精度较好(相对分析误差在2.0~2.5之间),其余离子预测效果较差;3种回归方法中,ELM模型精度最高,SVM模型次之,PLSR模型最低.冻结和未冻结状态下离子的最优反演模型相同,但冻结状态下Cl-、SO42-、K+和Na+反演精度比未冻结状态高,而Ca2+和Mg2+反演精度比未冻结状态低且Mg2+的反演精度差别最大.各离子最优反演模型与未冻结状态下的相对分析误差相比变化为-34.45%~24.43%.该研究构建的VIP-ELM模型为季节性冻土区盐渍化土壤盐基离子的高光谱监测提供了一种可靠途径. |
Author | 张智韬 陈俊英 王勇 杨锡震 侯晨悦 栗现文 袁心皓 李凡 |
AuthorAffiliation | 西北农林科技大学水利与建筑工程学院,杨凌 712100%西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌 712100 |
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Author_FL | YANG Xizhen LI Fan ZHANG Zhitao WANG Yong CHEN Junying YUAN Xinhao LI Xianwen HOU Chenyue |
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Keywords | 极限学习机法 盐渍化 冻结 盐基离子 反演模型 土壤 高光谱 |
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Snippet | S127%S153.6; 为探究采用高光谱技术反演冻结状态土壤水溶性盐基离子含量的可行性,该研究针对河套灌区盐渍化土壤,测定土壤在冻结与未冻结状态下的光谱和主要水溶性盐基离子含量(HCO3-、Cl-、CO32-、SO42-、K+、Na+、Ca2+、Mg2+),光谱经标准正态变量变换(standard normal... |
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Title | 冻结状态下盐渍化土壤中水溶性盐基离子含量高光谱反演 |
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