引用本文:
【打印本页】   【HTML】   【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 16次   下载 6 本文二维码信息
码上扫一扫!
分享到: 微信 更多
宁夏银北地区盐碱化土壤水分光谱特征及模型拟合精度分析
尚天浩1, 贾萍萍1, 孙媛1, 张俊华2
1.宁夏大学 资源环境学院, 宁夏 银川 750021;2.宁夏大学 环境工程研究院, 宁夏 银川 750021
摘要:
[目的] 通过分析宁夏银北地区盐碱化土壤水分光谱特征及模型拟合精度,为及时了解该区地表水分状况进而实施田间精准灌溉提供科学依据。[方法] 以宁夏银北地区重度盐渍土壤为研究对象,对土壤原始光谱反射率(raw spectral reflectance,R)进行多种数学变换,运用逐步回归(stepwise regression,SR)和灰色关联度(grey correlation degree,GCD)筛选敏感波段,然后采用多元线性回归(multiple linear regression,MLR)、偏最小二乘回归(partial least squares regression,PLSR)和支持向量机(support vector machine,SVM)进行土壤含水量(soil moisture content,SMC)模型拟合精度计算。[结果] ①SMC较低时,随SMC增加土壤反射率逐渐下降,当SMC超过26.34%后,土壤反射率随水分增加而增大,在近红外波段反射率变化幅度整体大于可见光波段;经连续统去除(continuum removed,CR)处理光谱特征曲线在1 460 nm和1 950 nm处出现明显水分吸收带。②不同反射率转换方式计算出的MLR,PLSR和SVM模型拟合精度不同,SVM模型的整体拟合能力优于MLR和PLSR模型,除反射率倒数(Reciprocal reflectance,RR)变换建立的GCD-SVM模型外,其余SVM模型RC2RP2介于0.943 7~0.999 5,模型的整体拟合精度很高。③在SVM模型中,基于对数一阶微分(first derivative of logarithmic reflectance,FLR)变换计算的GCD-SVM模型决定系数最高(RC2RP2分别为0.987 4,0.999 5),为重度盐渍化地区SMC的最佳拟合模型。[结论] SVM模型为供试土壤水分拟合的最佳模型,能够准确获取研究区重度盐渍化土壤水分状况。
关键词:  盐渍化土壤  土壤水分  高光谱  灰色关联度  支持向量机  拟合精度
DOI:10.13961/j.cnki.stbctb.2020.04.025
分类号:S127;TP97
基金项目:国家自然科学基金项目“基于光谱信息的龟裂碱土盐渍化程度预测研究”(41561078)
Spectral Characteristics of Soil Moisture in Salinized Soil and Model Fitting Accuracy in Northern Yinchua City, Ningxia Hui Autonomous Region
Shang Tianhao1, Jia Pingping1, Sun Yuan1, Zhang Junhua2
1.Institute of Enviromental Engineering, Ningxia University, Yinchuan, Ningxia 750021, China;2.College of Resources and Enviromental Science, Ningxia University, Yinchuan, Ningxia 750021, China
Abstract:
[Objective] To understand the surface moisture conditions and to implement precise field irrigation, spectral characteristics of soil moisture in salinized soil and model fitting accuracy were analyzed in the Northern Yinchuan City of Ningxia Hui Autonomous Region. [Methods] With the severe salinized soil in the Northern Yinchuan City as the subject, a variety of mathematical transformations were carried out on the raw spectral reflectance of soil moisture. The stepwise regression (SR) and the grey correlation degree (GCD) were used to screen sensitive wave bands, and then the multiple linear regression (MLR), partial least-squares regression (PLSR) and support vector machine (SVM) were used to calculate the fitting accuracy model of soil moisture content (SMC). [Results] ① The soil spectral reflectance decreased with the increase of SMC when SMC was below 26.34%, and soil spectral reflectance increased with the increase of SMC when SMC was higher than 26.34%. The change of reflectance in the NIR region were larger than that in visible region, and the spectral characteristic curves showed obvious absorption bands at 1 460 nm and 1 950 nm when continuum removed (CR) was used. ② Different transformation methods of the reflectance had different fitting accuracy about MLR, PLSR and SVM models, the overall fitting capacity of SVM model was better than MLR and PLSR models. Except for the GCD-SVM model by the reciprocal reflectance (RR) transformation, the RC2 and RP2 of the SVM models range from 0.943 7 to 0.999 5 and have high fitting accuracy. ③ In the SVM models, the GCD-SVM model based on first derivative of logarithmic reflectance (FLR) transformation had the highest determination coefficient (RC2 was 0.987 4 and RP2 was 0.999 5), which was the best fitting model of SMC for severe salinized soil. [Conclusion] The SVM model was the best model for SMC, it could accurately predict the surface moisture in severe salinized soil in Northern Yinchuan City of Ningxia region.
Key words:  saline soil  soil moisture content  hyperspectral  grey relational degree  support vector machine (SVM)  fitting precision