激光与光电子学进展, 2018, 55 (11): 113002, 网络出版: 2019-08-14   

利用随机森林方法优选光谱特征预测土壤水分含量 下载: 1047次

Prediction of Soil Moisture Content by Selecting Spectral Characteristics Using Random Forest Method
包青岭 1,2丁建丽 1,2,*王敬哲 1,2
作者单位
1 新疆大学资源与环境科学学院智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046
2 绿洲生态教育部重点实验室, 新疆 乌鲁木齐 830046
摘要
为了更加精确地分析土壤光谱中不同水分吸收带内的光谱吸收特征参数在估测土壤水分含量(SMC)中的重要性,以新疆渭干河-库车河绿洲为研究区,采集38个土壤样本进行土壤光谱反射率及SMC的测定。利用去包络线消除法提取反射光谱水分吸收特征参数,包括最大吸收深度D、吸收谷右面积Ra、吸收谷左面积La、吸收谷总面积A、面积归一化最大吸收深度DA和对称度S, 将反射光谱水分吸收特征与SMC进行相关性分析,通过随机森林方法对光谱水分吸收特征参数进行分类,获取各参数对SMC的重要性。运用多元逐步回归模型建立SMC反演模型。结果表明:DA与SMC的相关性最高,同时2200 nm及1400 nm波段范围内的光谱吸收特征参数与SMC的相关性优于1900 nm波段范围内的光谱吸收特征参数;对SMC影响较为重要的前5个参数分别为D2200La2200A2200D1900Ra2200;SMC的最佳预测模型是采用A2200D2200建立的多元逐步回归模型,其建模集决定系数为0.88,建模集均方根误差为2.08,测试集决定系数为0.89,预测均方根误差为2.21,相对分析误差为2.80。随机森林分类能得到对土壤含水量影响较为重要的光谱水分特征参数,为干旱区精准土壤水分快速估测提供了新方法。
Abstract
In order to more accurately analyze the importance of spectral absorption characteristic parameters, which in different soil moisture absorption bands in soil spectra, in soil moisture content estimation, we collect 38 soil samples in Ugan-Kuqa river oasis in Xinjiang to measure soil spectral reflectance and soil moisture content. The characteristic parameters of spectral water absorption are extracted with the continuum-removal method, the features include the maximum absorption depth D, the absorption peak right area Ra, the absorption peak left area La, the absorption peak total area A, area normalization maximum absorption depth DA, and symmetry S. With the correlation analysis of the features and soil moisture content, we use random forest method to classify the characteristic parameters of spectral water absorption, and obtain the importance of each parameter to soil moisture content. Multiple stepwise regression model is used to establish soil moisture content inversion model. The results are as follows: D and A have the strongest correlation with the soil moisture content, the correlation between spectral absorption parameters in the band of 2200 nm or 1400 nm and SMC is better than that of 1900 nm band; the top five parameters that are important for soil moisture content are obtained, they are D2200, La2200, A2200, D1900 and Ra2200, respectively; the best prediction model of SMC is the multiple stepwise regression model with A2200 and D2200, the decision coefficient of the modelling set is 0.88, root mean square error of modeling set is 2.08, decision coefficient of the test set is 0.89, prediction root mean square error is 2.21, and the relative analysis error is 2.80. Random forest classification can obtain the important spectral water characteristic parameters which have great influence on soil moisture content, and it provides a new method for accurate and rapid estimation of soil moisture content in arid areas.

包青岭, 丁建丽, 王敬哲. 利用随机森林方法优选光谱特征预测土壤水分含量[J]. 激光与光电子学进展, 2018, 55(11): 113002. Qingling Bao, Jianli Ding, Jingzhe Wang. Prediction of Soil Moisture Content by Selecting Spectral Characteristics Using Random Forest Method[J]. Laser & Optoelectronics Progress, 2018, 55(11): 113002.

本文已被 5 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!