基于高光谱遥感的小麦叶片氮积累量  

Monitoring leaf nitrogen accumulation with hyper-spectral remote sensing in wheat

作　　者：冯伟[1] 朱艳[1] 田永超[1] 曹卫星[1] 姚霞[1] 李映雪[1] 

机构地区：[1]南京农业大学江苏省信息农业高技术研究重点实验室,农业部作物生长调控重点开放实验室,南京210095

出　　处：《生态学报》2008年第1期23-32,共10页Acta Ecologica Sinica

基　　金：国家自然科学基金资助项目(30671215,30400278);江苏省自然科学基金资助项目(BK2005212,BK2003079)~~

摘　　要：作物氮素状况是评价长势、提高产量和改善品质的重要指标,因此叶片氮积累量的实时无损估测对作物生产的氮素管理具有重要意义。以多个小麦品种在不同施氮水平下的连续3 a大田试验为基础,研究了小麦叶片氮积累量与冠层高光谱参数的定量关系。结果表明,冠层叶片氮积累量随着施氮水平的提高而增加,光谱反射率在不同叶片氮积累量水平下发生相应的变化。叶片氮积累量的敏感波段主要存在于近红外平台和可见光区,其中,"红边"区域表现最为显著。通过微分等技术构造多种植被指数,对高光谱参数和叶片氮积累量间进行相关回归分析,SD r/SDb、FD742和AVHRR-GVI 3个参数与叶片氮积累量关系最密切,方程拟合决定系数R2分别为0.9163、0.9097和0.9142,估计标准误差SE分别为1.165、1.079和1.077。经不同年际独立数据的检验表明,利用光谱参数FD742建立的模型对叶片氮积累量的估测精度为0.8449,预测的RMSE为0.984;红边位置REPIG对叶片氮积累量的估测精度和预测的RMSE分别为0.8394和1.014,表明预测值与观察值之间符合精度高,比较而言,FD742为自变量建立的模型可以更好地评估不同条件下叶片氮素积累状况。Crop nitrogen status is a key indicator for evaluating crop growth, increasing yield and improving grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. This study aims at identification of the quantitative relationship between leaf nitrogen accumulation and canopy reflectance spectra in winter wheat （ Triticum aestivum L. ） using three field experiments with different wheat varieties and nitrogen levels. Results showed that leaf nitrogen accumulation in wheat increased with increasing nitrogen rates. Canopy reflectance changed with increasing leaf nitrogen accumulation. Sensitivity bands occurred mainly during visible light and near infrared, and strong correlation existed between red light and leaf nitrogen accumulation. The relationships of eight vegetation indicators and leaf nitrogen accumulation were analyzed using statistical models. Hyper-spectral variables were significantly correlated with leaf nitrogen accumulation, and the relationships of leaf nitrogen accumulation to SDr/SDb, FD742 and AVHRR-GVI were all highly significant with determination of coefficients （ R^2 ） as 0. 9163, 0. 9097 and 0. 9142, respectively, and standard errors （SE） as 1. 165, 1. 079 and 1. 077, respectively. Tests with another independent dataset showed that FD742 and REPIG could well predict leaf nitrogen accumulation in wheat with an R^2 of 0.8449 and 0. 8394, and root mean square error （RMSE） of 0. 984 and 1. 014, respectively. This suggests that FD742 and REPIG can be used to estimate leaf nitrogen accumulation, of which FD742 performed best in modeling and testing.

关 键 词：小麦 高光谱遥感 叶片氮积累量 监测模型 

分 类 号：S512.1[农业科学—作物学] Q945[生物学—植物学]