小波分析用于土壤速效钾含量高光谱估测研究  

Application of Wavelet Analysis for Estimation of Soil Available Potassium Content with Hyperspectral Reflectance

作　　者：陈红艳[1] 赵庚星[1] 李希灿[2] 陆文利[3] 隋龙[4] 

机构地区：[1]山东农业大学资源与环境学院,山东泰安271018 [2]山东农业大学信息科学与工程学院,山东泰安271018 [3]山东农业大学园艺科学与工程学院,山东泰安271018 [4]山东省沾化县国土资源局,山东沾化256800

出　　处：《中国农业科学》2012年第7期1425-1431,共7页Scientia Agricultura Sinica

基　　金：国家“863”计划项目(2008AA10Z203);高校博士点基金项目(20103702110010)

摘　　要：【目的】对土壤高光谱数据去噪提纯,提高土壤速效钾含量高光谱估测模型的精度和实用性。【方法】选取土壤有机质、碱解氮、有效磷含量近似而速效钾含量差异较大的样本76个,对土壤样本反射率对数的一阶导数光谱分别基于4种函数进行多层小波离散分解;提取小波低频系数,构建土壤速效钾含量高光谱估测模型。【结果】小波分解1—3层获得的低频系数可用以代表原始光谱。基于各小波函数相同尺度的低频系数,土壤速效钾含量估测建模精度差异不大。其中基于Bior 1.3函数分解的第2层低频系数建模精度略高,作为最佳估测模型,在数据压缩到25%、反映输入光谱信息95.6%的基础上,建模R2达到0.976,RMSE为10.66 mg.kg-1,经验证模型具有较好的预测准确度。【结论】通过小波分析获得小波系数,既提取了土壤高光谱信息,又对数据进行了压缩,结合偏最小二乘回归预测土壤速效钾含量是可行的。【Objective】 This study aimed at improving the precision and practicability of the soil available potassium estimation model by removing the noise of soil hyperspectral reflectance.【Method】 Seventy-six soil samples with similar soil organic matter,nitrogen and phosphorus element contents and different potassium element contents were selected.The first derivative spectrum of the soil sample logarithmic reflectance was decomposed to multiple levels by using four kinds of wavelet function,respectively.The low frequency wavelet coefficients were obtained,and the hyperspectral estimation models of soil available potassium content were built.【Result】The results showed that the low frequency wavelet coefficients of 1-3 levels could represent the original spectrum.Based on the low frequency coefficients of different wavelet functions at the same level,the precise of soil available potassium estimation model showed a little difference.The model built with the low frequency coefficient of the second decomposition level using the Bior 1.3 function had comparatively high accuracy and was chosen as the best model.With the data reducing to 25% and reflecting 95.6% information of the input spectrum,the model building R2 reached 0.976 and RMSE was 10.66 mg？kg-1,which was validated to have fairly good forecast accuracy.【Conclusion】Therefore,wavelet analysis for obtaining wavelet coefficients can not only extract the soil hyperspectral information,but also compress data,which is feasible to forecast soil potassium content in combination with partial least squares regression method.

关 键 词：高光谱 土壤速效钾 小波分析 小波系数 

分 类 号：S158[农业科学—土壤学]