Abstract: 【Objective】In order to elucidated characteristics of spectrum of cotton leaf infected with verticillium wilt and estimated its severity level (SL)to provide theoretic foundation for further monitoring cotton verticillium wilt at large scale using airborne and airspace remote sensing. 【Method】 The spectrum reflectance of cotton single leaf infected with verticillium wilt was measured in cotton disease garden and cotton field in different growth phase, meanwhile, SL of single leaf infected with verticillium wilt was investigated. The methods of first derivative spectral were used estimate accurately disease of cotton with Verticillium wilt when compared with the reflectance spectrum of different single leaf infected of verticillium wilt. 【Result】The results indicated that Spectral characteristic of cotton leaf infected with verticillium wilt had better regularity with the increase of SL in different periods and varieties. Spectral reflectance increased significantly at visible light region (400～700nm) and near-infrared region (700～1300nm) with the increase of the SL ,and specially signification at blue- violet to red regions(525～680nm) when SL got 25%, cotton leaf of verticillium wilt could be used as a watershed and diagnosed index in early time. There were evident different characteristics of first derivative spectra in these disease leave, it changed significantly in red edge ranges(680～780nm) in different disease level, derivative spectra of red edge swing decreased, and red edge position equal moved to the blue. The thesis indicated that 434～724nm and 909～1600nm were selected out as sensitive bands region to SL of single leaf. Some inversion models for estimating cotton leaf diseased level of verticillium wilt all reached the best significantly level.【Conclusion】The results suggested that different characteristics of spectrum of cotton leaf infected with verticillium wilt were obvious. The model in which the first derivative spectra at 723nm could invert accurately the cotton leaf SL., and it may be used to forecasting the position of cotton leaf infected with verticillium wilt in quantitatively.

Key words: Cotton, Verticillium wilt, hyperspectral, recognition model