Remote sensing monitoring severity level of cotton verticillium wilt based on partial least squares regressive analysis

The objective of this study was to estimate the severity level of cotton verticillium wilt using high spatial resolution satellite data and partial least-squares regressive analysis. Firstly, remote sensing factors for monitoring cotton verticillium wilt were picked out by the pre-processed IKONOS image. Then the variable factors were selected according to an approach of Variable Importance in Projection, and used to establish the severity estimating model of cotton verticillium wilt using partial least squares (PLS) regression analysis. Finally the model was applied to calculate the severity level of each pixel in the region of cotton verticillium wilt. The results show that these vegetation indices, i.e. enhanced vegetation index (EVI), renormalized difference vegetation index (RDVI), global environment monitoring index (GEMI), difference vegetation index (DVI), modified soil adjusted vegetation index (MSAVI) and normalized difference vegetation index (NDVI), are sensitive factors for monitoring severity level of cotton verticillium wilt. The model based on those variables achieves better accuracy since precision assessment indices such as determination coefficient (R2), root mean square error (RMSE) and relative error (RE) which are 0.78, 0.45 and 9.2% respectively. Severity level of cotton verticillium wilt can be effectively estimated utilizing high spatial resolution image and partial least squares regression analysis and the result presents an important reference approach for further monitoring crop pests and diseases at large scale using airborne and airspace remote sensing data.