设为首页  |   加入收藏
引用本文:
【打印本页】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 90次   下载 14 本文二维码信息
码上扫一扫!
分享到: 微信 更多
淮北平原土壤高光谱特征及有机质含量预测
陆龙妹, 张 平, 卢宏亮, 刘斌寅, 赵明松
安徽理工大学测绘学院
摘要:
以安徽省淮北平原的蒙城县为研究区,采集131个表层土壤(0 ~ 20 cm)样品。采用Cary 5000分光光度计测定土壤光谱反射率,分析该地区典型土壤类型的光谱特征,利用偏最小二乘回归方法建立土壤有机质光谱预测模型。首先比较不同光谱变换对土壤有机质含量光谱预测建模的影响;其次根据光谱相似性对土壤样品进行分类,比较不同土壤类型和不同光谱分类的有机质光谱预测精度。结果表明:①不同土壤有机质含量和不同土壤类型光谱曲线在整体波段范围内趋势基本一致;有机质含量与光谱反射率呈显著负相关;有机质含量越低,曲线特征差异明显,可能是受其他因素的影响;②土壤光谱反射率经倒数的对数处理后,有机质光谱建模的决定系数和相对分析误差均有所提高,均方根误差降低,模型预测效果较优;③按照光谱相似性分类后建立的有机质光谱预测模型,比按土壤类型建立的光谱预测模型精度明显提高。
关键词:  土壤高光谱特征  光谱相似性分类  土壤类型  偏最小二乘回归  淮北平原
DOI:10.13758/j.cnki.tr.2019.02.023
分类号:S127
基金项目:国家自然科学基金项目(41501226)、安徽省高校自然科学研究项目(KJ2015A034)、土壤与农业可持续发展国家重点实验室开放基金项目(Y412201431)和安徽理工大学人才引进项目(ZY020)资助。
Hyperspectral Characteristics of Soils in Huaibei Plain and Estimation of SOM Content
LU Longmei, ZHANG Ping, LU Hongliang, LIU Binyin, ZHAO Mingsong
School of Geomatics, Anhui University of Science and Technology
Abstract:
In this paper, Mengcheng County was taken as the study area in the Huaibei Plain of Anhui Province. A total of 131 topsoil samples (0-20 cm) were collected, their spectral reflectance was measured by Cary 5000 spectrophotometer and the spectral characteristics of typical soil types were analyzed. Moreover, the prediction model of SOM content was established by PLSR. Firstly, whether the different mathematical transformation forms have different effects on the spectral model in predicting SOM content is examined. Secondly, the soil samples were classified according to spectral similarity, and the prediction precision of SOM spectra of different soil types and different spectral classification were compared. The results showed that: 1) The trends of spectral curves of different SOM contents and different soil types were basically similar. The correlation between SOM content and the spectral reflectance was negative, and the curve characteristic varied significantly when SOM content became lower, which is possibly influenced by other factors. 2) PLSR model, established on inverse-log processing of the reflectance, was the best in prediction, with the determination coefficients and the relative percent deviation all increased, the root mean squared error decreased. 3) The precision of SOM spectral prediction model based on the spectral similarity classification was significantly higher than that of the soil type model.
Key words:  Soil hyperspectral characteristics  Spectral similarity classification  Soil type  PLSR  Huaibei Plain

您是第2051171位访问者
版权所有 © 《土壤》编辑部
本系统由北京勤云科技发展有限公司设计   京ICP备09084417号