摘要
通过人工田间诱发不同等级条锈病,在不同生育期测定冬小麦感染条锈病严重度和冠层光谱,采用偏最小二乘(PLS)方法建立了冠层光谱和条锈病严重度的回归模型。结果显示:PLS反演冬小麦条锈病严重度的效果很好,与文献[4]中提出的利用高光谱指数进行反演的结果相比,精度更高;通过对PLS回归系数的分析,发现叶绿素吸收谷两边(505～550nm,640～670nm,680～700nm)的一阶微分光谱可用于诊断冬小麦条锈病病情,条锈病病害冬小麦在叶绿素吸收谷两边的一阶微分光谱的绝对值会比健康冬小麦的更大。
Following the inoculation of stripe rust disease of different levels in the winter wheat measured the stripe rust disease severity and winter wheat canopy reflectance at different growth field, the authors stages. PLS （ Partial Least Square） was adopted to build a regression model for disease severity inversion from canopy reflectance. The results indicate that the inversion accuracy of PLS is higher than that of the method proposed in reference [ 4 ], which used PRI （ Photochemical Reflectance Index） to predict disease severity. Regression coefficients of PLS were investigated to obtain useful knowledge. It has been found that the first derivatives on the two sides of the chlorophyll verity absorption valley （505 - 550 nm ,640 - 670 nm ,680 - 700 nm） Diseased winter wheat has higher absolute values of first nm） are most important in determining disease sederivatives in the three spectral regions.
出处
《国土资源遥感》
CSCD
2007年第1期57-60,共4页
Remote Sensing for Land & Resources
基金
国防科技工业民用专项科研技术研究项目(JZ20050001-06)
北京自然科学基金(4052014)
地理空间信息工程国家测绘局重点实验室基础测绘经费联合资助
关键词
数据挖掘
高光谱
偏最小二乘
病情指数
Data mining
Hyperspectral
Partial least square
Disease index
