采用非负矩阵分解方法的秸秆-土壤光谱解混及盐分估测  

Spectral unmixing of straw and soil to estimate the soil salinity using non-negative matrix factorization

作　　者：李振 曹见飞 杨晗 刘建华 王召海 段欣荣 张乐天 Li Zhen;Cao Jianfei;Yang Han;Liu Jianhua;Wang Zhaohai;Duan Xinrong;Zhang Letian(College of Geography and Environment,Shandong Normal University,Jinan 250014,China;Shandong Dongying Institute of Geographic Sciences,Dongying 257000,China;Shandong Provincial Institute of Land Surveying and Mapping,Jinan 250013,China;China Environmental United(Beijing)Certification Center Co.,LTD.,Jinan 250000,China)

机构地区：[1]山东师范大学地理与环境学院,济南250014 [2]中科山东东营地理科学研究院,东营257000 [3]山东省国土测绘院,济南250013 [4]中环联合(北京)认证中心有限公司,济南250000

出　　处：《农业工程学报》2022年第8期161-168,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中科山东东营地理研究院开放基金(202102);山东省重点研发计划项目(2019GSF109034)。

摘　　要：黄河三角洲地区发展节约型农业,大力推行秸秆还田技术以改善土壤生态环境,当利用遥感技术对部分秸秆覆盖地区进行土壤盐渍化监测时,混合像元制约土壤盐分遥感估测的精度。该研究设置10组不同秸秆覆盖度的盐渍化地表场景,多次光谱测量并取平均值作为其反射光谱。探究非负矩阵分解(Non-negative Matrix Factorization,NMF)方法分离农业秸秆和土壤光谱的有效性,并基于分离出的土壤光谱构建偏最小二乘法土壤盐分估测模型。结果表明:1)秸秆覆盖会使土壤光谱反射率增高,相比于纯土壤光谱在1 730 nm、2 090 nm附近出现吸收谷;2)通过NMF进行混合光谱解混之后,可以有效地将土壤光谱从秸秆覆盖光谱中分离出来,且土壤光谱保留了土壤盐分信息;3)对于不同秸秆覆盖水平下的土壤盐分估测模型而言,与原始混合光谱相比,利用土壤光谱NMF分离数据构建的模型精度普遍提高,建模集决定系数平均提高0.07,均方根误差降低1.21。验证集决定系数、相对分析误差分别平均提高0.07、0.25,均方根误差降低1.22。该研究可为黄河三角洲地区部分秸秆覆盖盐渍土地的近地面遥感估测精度的提高提供方法。Straw returning to the field has been used to promote the soil ecological environment in the Yellow River Delta.A remote sensing estimation of soil salt can greatly contribute to monitor the soil salinization in the straw covered areas.However,the mixed data has posed a great challenge on the pixel accuracy,due to both straw and soil spectral signals in the same pixel.In this study,ten groups of salinized surface scenes were firstly collected with different straw coverage.Multiple spectral measurements were then carried out on each sample,where the average value was taken as the reflection spectrum.The Non-negative Matrix Factorization(NMF)was selected to treat the mixed spectrum under the non-negative restriction in the process of spectral unmixing.The endmember matrix and abundance matrix were updated simultaneously by an iterative method,and finally two endmember spectra were extracted.The spectra of soil and straw were separated to evaluate the performance of the straw removal from the mixed spectrum,according to the retaining salinity information.Finally,the soil salt model was constructed using the Partial Least Square Regression(PLSR),further to verify the effectiveness of NMF for the spectral unmixing and extracting soil spectrum.The results show that:1)The soil spectral reflectance decreased gradually with the increase of the salinity in the whole wavelength range,but the trend of curves remained similar.More importantly,the straw coverage increased the spectral reflectance of soil.The spectrum of straw coverage soil was much more similar to the straw spectrum,as the degree of the straw cover increased.In addition,there were the absorption characteristics near 1730 and 2090 nm in the straw covered soil,compared with the pure soil.2)The soil spectrum was effectively separated from the straw mixed spectrum after NMF unmixing.There were more outstanding characteristics of straw in the separated straw spectrum on the whole with the increase of straw coverage,when the degree of the straw cover reached 15%.The

关 键 词：土壤 模型 盐分 非负矩阵分解 近地面遥感 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]