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:D、A与SMC的相关性最高,同时2200nm及1400nm波段范围内的光谱吸收特征参数与SMC的相关性优于1900nm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D2200、La2200、A2200、D1900和Ra2200;SMC的最佳预测模型是采用A2200、D2200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。
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 R a, the absorption peak left area L a, 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 Dzzoo, L,2200, 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, decisioncoefficient 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.
作者
包青岭
丁建丽
王敬哲
Bao Qingling;Ding Jianli;Wang Jingzhe(Key Laboratory of Wisdom City and Environmental Modeling,College of Resource and Environment Sciences,Xinjiang University,Urumqi,Xinjiang 830046,China;Key Laboratory of Oasis Ecology,Ministry of Education,Xinjiang University,Urumqi,Xinjiang 830046,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第11期464-470,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41771470)
新疆自治区重点实验室专项基金资助项目(2016D03001)
自治区科技支疆项目(201591101)
教育部促进与美大地区科研合作与高层次人才培养项目
关键词
光谱学
土壤水分含量
随机森林
吸收特征参数
spectroscopy
soil moisture content
random forest
absorption characteristic parameter
