基于RBF组合模型的山地红壤有机质含量光谱估测
第54卷第#期 林业科学V154，N。.# 2 0 1 8 年 # 月SCIENTIA SILVAE SINICAE Jun.，2 0 1 8
8oi:10.11707/;.1001-7488.20180603
基于RBF组合模型的山地红壤有机质含量光谱估测！
谢文1赵小敏1郭熙1叶英聪1孙小香1匡丽花2
(1.江西农业大学林学院江西省鄱阳湖流域农业资源与生态重点实验室南方粮油作物协同创新中心南昌330045#
2.南京农业大学公共管理学院南京210095)
摘要：【目的】探讨组合模型在山地红壤有机质含量高光谱估算中应用的可行性，以期为土壤有机质含量估测 提供基础数据和科学依据。【方法】基于山地红壤光谱的全波段（400 ?2 450 nm)研究范围，选择偏最小二乘回归
(PLSR)、B P神经网络（BP)和支持向量机回归分析（SVMR&3种单一高光谱估测模型，分别获得预测结果，并重构预 测结果数据，以绝对误差和最小为目标，计算固定权重与不固定权重两种组合模型的权重值，并基于径向基函数
(RBF)神经网络法建立组合模型，探讨不同赋权方法与是否重构数据条件下的最优组合模型。通过均方根误差
(RMSE)、预测偏差比（RPD)和决定系数（A)评价山地红壤有机质含量的预测精度。【结果】单一预测模型中的 SVMR估测精度最高，验证决定系数（A)为0.64,均方根误差为9.76 g*kg'1，测定值标准差与标准预测误差的比值 为137#在组合模型数据不重构的条件下，不定权组合模型要优于定权组合模型；在组合模型数据重构的条件下，定 权组合模型要略优于不定权组合模型，估测精度相差不大；最优模型是数据重构定权组合模型，模型验证决定系数
(A)为0.87,均方根误差为7.91g*kg_1，测定值标准差与标准预测误差的比值为2.06#组合模型验证精度优于单一
模型，说明利用RBF组合模型估算山地红壤有机质含量是可行的。【结论】对山地红壤有机质含量的快速估测而言，单一模型具有操作简单、运算速度快等特点，因而具有较大应用价值，但组合模型能较大限度地利用各种预测样本信 息，从而能有效减少应用单一模型时所受随机因素的影响，从而提高山地红壤有机质含量的估测精度。
关键词：RBF组合模型；山地红壤；有机质；土壤光谱；偏最小二乘回归；B P神经网络；支持向量机回归
中图分类号：S714; S158; S151; TP79 文献标识码：A文章编号：1001 -7488 (2018 )06 -0016 -08 Spectrum Based Estimation of the Content of Soil Organic Meters in
Mountain Red Soil Using RBF Combination Model
Xie W en1Zhao Xiaomin1Guo Xi1YeYingcong1Sun Xiaoxiang1Kuang Liliua2
(1. College o f Forestry，Jiangxi Agricultural University Key Laboratory o f Poyang Lake Watershed Agricu
Ecology o f Jiangxi Province Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China
2. College 〇o Public Administration，Nanjing Agricultural Universitt Nanjing 210095 )
A bstract ：【Objective】 It is of great significance for rapid acquisition of soil fertility parameters using spectral technology to explore the quantitative estimation of the contents of soil organic m atters.【Method 】Tw ohundred samples at 0 -30 cm depth were collected from mountain red soil region of northern Jiangxi Province. The spectral reflectance of mountain red soil samples was measured b y a n ASD FieldSpec3 instrument under laboratory conditions. Meanwhile the content of organic matters of each soil sample was analyzed using potassium dichromate external heating method in laboratory. Correlation analyses between raw mountain red soil spectral reflectance and organic matter content mountain red soil were conducted. Kennard -Stone algorithm was used to divide mount sets with 186 s amples and prediction sets with 62 samples. Based on the full band (400 - 2 450 nm) of mountain red soil spectra in this study，the partial least squares regression ( PLSR)，
B Pneural network (B P) and support vector machine regression (SVM R) were selected to obtain and rebuild the prediction result，and establish fixed weight and variable weights in combination model witli the minimum absolute error sum as the goal. T hen，the based on radial basis function ( RBF) neural network. The optimal combination model with differe without reconstructed data was i nvestigated. Accuracies of the content predictions of organic matters of the mountain red 收稿日期：2016 - 12 -20#修回日期：2018 -05-07。
基金项目：国家自然科学基金项目（41361049 ); 土壤与农业可持续发展国家重点实验室（中国科学院南京土壤研究所）项目 (0812201202)。
!赵小敏为通讯作者。