赵燕东,皮婷婷.北京地区粘壤土全氮含量的光谱预测模型[J].农业机械学报,2016,47(3):144-149.
Zhao Yandong,Pi Tingting.Spectral Prediction Model of Soil Total Nitrogen Content of Clay Loam Soil in Beijing[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):144-149.
摘要点击次数: 970
全文下载次数: 834
北京地区粘壤土全氮含量的光谱预测模型   [下载全文]
Spectral Prediction Model of Soil Total Nitrogen Content of Clay Loam Soil in Beijing   [Download Pdf][in English]
投稿时间:2015-09-29  
DOI:10.6041/j.issn.1000-1298.2016.03.021
中文关键词:  粘壤土  北京地区  全氮  光谱  多元逐步回归  一元线性回归
基金项目:国家自然科学基金项目(31371537)和北京市共建项目专项
作者单位
赵燕东 北京林业大学 
皮婷婷 北京林业大学 
中文摘要:为实现快速准确地测量土壤的全氮含量,以北京地区粘壤土为样本,对其进行化学测量和光谱分析。利用波长为350~2500nm的光谱数据与实际测得的全氮含量进行相关性分析,选取相关性最大的特征波段构建土壤全氮含量的估算模型。将原光谱反射率和吸光度分别进行一阶微分、二阶微分变换,力求建立精准优化的土壤全氮含量预测模型。结果表明:反射率和吸光度与土壤全氮含量的相关性低,无法用于构建土壤全氮含量预测模型。在其他变换形式中,反射率二阶微分和吸光度二阶微分与土壤全氮含量的相关性最显著,相关系数的绝对值最大分别为0868和0846。相关性最大的特征波段为425~527nm、819nm、1390~1391nm和2200~2219nm。采用一元线性回归和多元逐步回归建立预测模型,最终得到土壤全氮含量最优估算模型以吸光度二阶微分为自变量的多元逐步回归模型,说明光谱结合多元逐步回归法预测土壤全氮含量的方法是可行的。最优模型决定系数R2为0.829,统计量F为86.377,均方根误差RMSE为0.104。该模型可用于预测北京地区粘壤土的土壤全氮含量。
Zhao Y  ong  Pi Tingting
Beijing Forestry University and Beijing Forestry University
Key Words:clay loam soil  Beijing area  total nitrogen  spectrum  multivariate stepwise regression  linear regression
Abstract:In order to quickly and accurately measure the soil total nitrogen content(STNC), 72 soil samples were collected from Beijing City for chemical measurements and spectral analysis. By correlation analysis of the actual measured nitrogen content with spectral data which wavelength is 350~2500nm, the most relevant characteristic wave bands were selected to build the STNC estimation models. To establish accurate and optimized predictive model of STNC, the spectral reflectance and absorbance were converted into first order differential and second order differential. The results showed that both spectral reflectance and absorbance had a low correlation with STNC, so they could not be used to build prediction model. Their correlations were improved by transforming them to the first order differential and the second order differential. In various transformation of reflectance, the second order differential and the second order differential of absorbance were the most relational with STNC. The maximum absolute values of correlation coefficient were 0.868 and 0.846. The most relevant characteristic bands were 425~527nm, 819nm, 1390~1391nm and 2200~2219nm. STNC models were built through linear regression and multivariate stepwise regression. The reciprocal logarithm second order differential model based on multivariate stepwise regression was the optimal model among the 10 prediction models established in this article. This conclusion proved that it is feasible to use multivariate stepwise method for predicting STNC. The R2 of the optimal model was 0.829, statistics value was 86.377 and the RMSE was 0.104. This model can be used to predict the STNC of clay loam soil in Beijing City.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器