摘要
利用统计分析方法选取了土壤N、P、K元素含量近似而有机质含量差异较大的样本60个,通过高光谱探测分析获得样本反射率对数的一阶导数光谱,采用Bior1.3函数进行多层离散小波分解,剔除低频近似信号和高频噪声信号,得到反映土壤理化参数的特征光谱曲线;采用相关分析筛选土壤有机质含量的显著相关波段,基于显著相关波段和特征光谱曲线分别构建土壤有机质含量高光谱多元回归估测模型;通过比较分析,确定了提取土壤有机质特征光谱的最佳小波分解尺度并构建了最佳预测模型.结果表明:提取土壤有机质特征光谱的最佳小波分解层数是9层,其次是8层和10层;基于小波9层分解特征光谱曲线的有机质含量估测模型最佳,其决定系数(R2)为0.89,比基于显著相关波段构建模型的R2增加了0.31,比基于原始光谱所构建模型的R2增加了0.10.
A total of 60 soil samples with approximate contents of N,P,and K and greatly different content of organic matter were selected by statistical analysis.Through hyper-spectral detection and analysis,the first derivative spectrum of the soil logarithmic reflectance was obtained,and was decomposed by the Bior 1.3 wavelet function.The approximative signal of the lowest frequency and the noise signal of the highest frequency were removed from the input spectrum so as to obtain the characteristic spectrum corresponding to soil physical and chemical parameters.The sensitive bands of soil organic matter were selected by correlation analysis,and the forecasting models were built by multiple regression analysis,based on the sensitive bands and the characteristic spectrum,respectively.Through comparison analysis,the optimal wavelet decomposing resolution for extracting the characteristic spectrum of soil organic matter was ascertained,and the best forecasting model was established.The best wavelet decomposing resolution was 9,followed by 8 and 10.Based on the characteristic spectrum of wavelet decomposing of 9 resolutions,the model R2 reached 0.89,which was increased by 0.31 as compared to the model based on sensitive bands,and increased by 0.10 as compared to the model based on the original spectrum.
出处
《应用生态学报》
CAS
CSCD
北大核心
2011年第11期2935-2942,共8页
Chinese Journal of Applied Ecology
基金
高校博士点科研基金项目(20103702110010)资助
关键词
高光谱
土壤有机质
小波变换
特征光谱
hyper-spectra
soil organic matter
wavelet transformation
characteristic spectrum.
