地表枯枝落叶层影响下的土壤混合光谱特征及解混方法研究  

Plant Litter Effect of the Soil Organic Carbon Estimation and Unmixing Method Based on the Visible-Near Infrared Spectra

作　　者：赵伟 包妮沙[1,2] 刘善军[1,2] 毛亚纯[1,2] 肖冬[2,3] ZHAO Wei;BAO Ni-sha;LIU Shan-jun;MAO Ya-chun;XIAO Dong(College of Resources and Civil Engineering,Northeastern University,Shenyang 110819,China;Smart Mine Research Center,Northeastern University,Shenyang 110819,China;College of Information Science and Engineering,Northeastern University,Shenyang 110819,China)

机构地区：[1]东北大学资源与土木工程学院,辽宁沈阳110819 [2]东北大学智慧矿山研究中心,辽宁沈阳110819 [3]东北大学信息学院,辽宁沈阳110819

出　　处：《光谱学与光谱分析》2020年第7期2188-2193,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金青年科学基金项目(41401233);中央高校基本科研专项资金项目(N160102001,N2001002)资助。

摘　　要：针对草原土壤属性高光谱监测过程中地表枯落物对土壤光谱建模精度的影响。以呼伦贝尔典型草原土壤光谱为研究对象,通过室内模拟光谱实验及野外光谱实测验证,分析混合光谱特征,揭示枯枝落叶层对土壤光谱影响的规律,提出了基于光谱相似值约束下的盲源分离ICA算法,对混合光谱进行解混,削减枯枝落叶层对土壤光谱的影响。结果表明,(1)随枯枝落叶盖度增加,光谱纤维素吸收指数(CAI)增加,呈二次回归递增趋势,可有效检验混合光谱中枯枝落叶的覆盖程度;(2)混合光谱在700 nm跃迁波段有明显的斜率陡增现象,并在1 680及1 754 nm处存在微弱的木质素吸收特征,在2 100 nm附近处出现强吸收特征;(3)优化后的BSS-ICA算法可有效分离枯枝落叶同土壤的混合光谱,进而提升野外光谱估测有机碳含量的精度,分别利用偏最小二乘回归(PLSR)、支持向量机(SVM)及随机森林(RF)对解混前后光谱建立预测有机碳预测模型;其中SVM模型预测精度最高,预测集的R^2从0.71提高到0.75, RMSE从4.82 g·kg^-1降低到4.50 g·kg^-1。通过实验研究对定向去除外部环境参数中的地表枯枝落叶层对土壤高光谱影响进行了实证,并通过野外实测数据验证解混算法的有效性,为完善野外原位光谱数据反演及监测土壤理化属性提供理论依据。In terms of the application of spectroscopy in-situ for soil quality monitoring from grassland,this paper takes the soil spectrum of Hulunbeier’s typical grassland as the research object.Verification by indoor simulated spectroscopy experiment and field spectrum measurement,and reveal the influence of plant litter on soil spectrum by analyzing the characteristics of mixed spectra.The blind source separation(BSS)independent component analysis(ICA)algorithm is used to separate the mixed spectra.Furthermore,spectral similarity value(SSV)is calculated to optimize BSS-ICA for unmixing soil spectra.The accuracy of the SOC prediction model before and after unmixing is compared to valid applicability of BSS-ICA algorithm.The results show that,(1)the cellulose absorption index(CAI)based on the characteristics of mixed spectra could effectively detect the extent of plant litter cover in the mixed spectra.CAI index would increase with the increasing of plant litter cover in quadratic regression;(2)It is found that a steep slope occurs at the transition band of 700 nm and weak lignin absorption characteristics in 1680 and 1754 nm,strong cellulose absorption occurs at 2100 nm from mixed spectra;The SOC would be overestimated by about 11.94%using SVM prediction model once soil surface covered by only 5%plant litter.(3)The unmixing method of BSS-ICA can reduce the spectral characteristic from plant litter effect,and using partial least squares(PLSR),support vector machine(SVM)and random forest(RF)to model the prediction of organic carbon before and after unmixing.SVM has the highest accuracy among the three methods.The accuracy of SOC prediction was improved from R^2 of 0.71 before unmixing to 0.75 after unmixing,RMSE of 4.82 g·kg^-1 before unmixing to 4.50 g·kg^-1 after unmixing.The optimized BSS-ICA algorithm can effectively separate soil from mixed spectra with litter and might improve the accuracy of SOC estimation by field spectra.This experimental study of reducing the external factors on soil spectra provides a theor

关 键 词：土壤 地表枯枝落叶 盲源分离 混合光谱 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]