摘要
及时、有效地预测作物氮含量,能够为作物氮素养分的监测和诊断提供合理的信息支持。本研究通过采集冬小麦开花期和灌浆期的光谱信息和相应的植株氮含量,对原始光谱及其变换形式与植株氮含量进行相关性分析,筛选不同光谱反射条件下的敏感波段,采用多元线性回归、逐步回归和偏最小二乘法分别建立植株氮含量遥感估算模型。结果表明:(1)开花期所筛选的最佳敏感波段为FR_(689)、FLR_(753)、FR_(768)、F/R_(749)、FLR_(495);灌浆期所筛选的最佳敏感波段为1/R_(955)、LR_(955)、L/RR_(955)、F/R_(490)、R_(955);(2)开花期采用PLS建立的模型效果最好,建模和验证的R^(2)和RMSE分别为0.72、0.12和0.62、0.16;(3)灌浆期采用PLS建立的模型效果最好,建模和验证的R^(2)、RMSE分别为0.70、0.11和0.32、0.18。研究发现开花期所构建的植株氮含量遥感估算模型有较高的精度和可靠性,可为更好地检测冬小麦氮素利用效率和精准施肥提供理论依据。
The timely and effective prediction of crop nitrogen content can provide reasonable information support for monitoring and diagnosis of crop nitrogen nutrients.By collecting spectral information of flowering and filling stages of winter wheat and corresponding plant nitrogen content,the correlation between the original spectrum,reciprocal spectrum,logarithmic spectrum,differential spectrum,reciprocal logarithmic spectrum,logarithmic differential spectrum,reciprocal differential spectrum and plant nitrogen content was analyzed.Sensitive bands under different spectral reflectance conditions were screened.Multivariate linear regression was used.Stepwise regression and partial least squares(PLS)were used to establish remote sensing estimation models of plant nitrogen content.The results showed that:(1)The optimum sensitive bands at flowering stage were FR_(689),FLR_(753),FR_(768),F/R_(749)and FLR_(495),while the optimum sensitive bands at filling stage were 1/R_(955),LR_(955),L/R_(955),F/R_(490)and R_(955).(2)The most effective model was the PLS for flowering stage,and R^(2)and RMSE for modeling and validation were 0.72,0.12 and 0.62,0.16,respectively.(3)P was used at filling stage.The model established by LS has the best effect.R^(2)and RMSE of modeling and validation are 0.70,0.11,0.32 and 0.18,respectively.The study found that the remote sensing estimation model of plant nitrogen content at flowering stage had high accuracy and reliability,which provided theoretical basis for better detection of nitrogen use efficiency and precise fertilization of winter wheat.
作者
杨福芹
李蕊
冯海宽
李天驰
王果
YANG Fuqin;LI Rui;FENG Haikuan;LI Tianchi;WANG Guo(College of Civil Engineering,Henan Institute of Engineering,Zhengzhou 451191;School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China)
出处
《东北农业科学》
2023年第3期118-124,共7页
Journal of Northeast Agricultural Sciences
基金
国家自然科学基金项目(41601346)
河南工程学院教育教学改革研究项目(2021JYYB038)
河南省科技攻关计划项目(202102310333、182102310021)
关键词
冬小麦
植株氮含量
多元线性回归
逐步回归
偏最小二乘回归
Winter wheat
Plant nitrogen content
Multiple linear regression
Stepwise regression
Partial least squares regression
