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 176次   下载 261 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于野外VIS-NIR光谱的土壤盐分主要离子预测
马利芳1, 熊黑钢2
1.新疆大学;2.北京联合大学
摘要:
为明确干旱区土壤盐分主要离子的特征光谱,建立精度高和稳定性好的盐渍土预测模型,以新疆阜康市为研究区域,采用网格法采集55个土壤样本,利用实测VIS-NIR光谱,选择多元线性回归(MLR)、支持向量机(SVM)、随机森林(RF)法构建土壤盐分主要离子含量反演模型,而后对反演精度进行检验。结果显示:①在0.01显著水平下,土壤盐分与Na+、Cl-、Ca2+ 含量均呈显著相关,相关系数分别为0.978、0.814、0.645;②综合光谱响应和相关性分析确定土壤盐分主要离子的特征波段为459、537、1 381、1 386 nm,显著特征波段为459、537 nm;③3种模型拟合效果从高到低依次为RF>MLR>SVM,采用RF所建模型盐分主要离子(Na+、Cl-、Ca2+)R2最高,RMSE最小,RPD最大,分别为2.11、2.03、1.80,为最优预测模型。通过选取土壤主要离子显著特征波段,进而采用RF法构建其估测模型,可以有效提取干旱区土壤盐分的主要离子信息。
关键词:  土壤  盐分  高光谱  反演  支持向量机  随机森林
DOI:10.13758/j.cnki.tr.2020.01.027
分类号:S151.9
基金项目:国家自然科学基金项目(41671198,41761041)资助。
Prediction of Major Ions in Soil Salinity Based on Field VIS-NIR Spectroscopy
malifang1, xiongheigang2
1.xinjianguniversity;2.beijinglianheuniversity
Abstract:
In order to clarify the characteristic spectrum of main salt ions in arid areas, a prediction model for high-precision and stable saline soils was established.Taking Fukang City of Xinjiang as the study area, collected 55 soil samples and field measured spectral data based on VIS-NIR, using multiple linear regression(MLR), support vector machine(SVM) and random forest(RF) method three inversion model of soil salinity and main ion content were established, and the model was tested. The results showed that: 1) At 0.01 significant level, soil salinity had a significant correlation with Na+, Cl- and Ca2+, and the correlation coefficients were 0.978, 0.814 and 0.645, respectively; 2) Comprehensive spectrum response and correlation analysis determined the dominant ion bands of soil salt at 459, 537, 1381, and 1 386 nm, and the significant characteristic bands at 459 and 537 nm; 3) The three model fitting effects from high to low were RF>MLR>SVM in order, and using the model established by RF, the salt main ions (Na+,Cl-,Ca2+) had the highest R2, the smallest RMSE, and the largest RPD, which were 2.11, 2.03, and 1.80, respectively, and were the optimal prediction models. By selecting the dominant characteristic bands of major ions in the soil, RF method was used to construct the estimation model in this area, which can effectively extract the main ion information of soil salinity in the arid area.
Key words:  Soil  Salt  Hyperspectral  Inversion  Support vector machine  Random forest

您是第3180215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