| In order to explore the visual monitoring method for winter wheat loss suffered from waterlogging disaster, the study analyzed the changes of 16 image feature indices through field experiments, as well as the correlation with SPAD, yield and thousand kernel weight, and then constructed estimation models of winter wheat waterlogging based on the attenuation of image feature indices. The results showed that R, normalized redness index(NRI),excess red index(EXR) and color index of vegetation extraction(CIVE) were all significantly increased with the increase of waterlogging time,while normalized greenness index(NGI),normalized green red difference index(NGRDI),green minus red(GMR),excess green index(EXG) and green-red ratio vegetation index(GRVI) were significantly decreased. The above nine image feature indices were significantly correlated with SPAD, yield and thousand kernel weight of wheat, with the maximum absolute values of 0.92, 0.85 and 0.91,respectively.Moreover, the quadratic polynomial model could be efficiently applied for the modeling of SPAD, yield and thousand kernel weight reduction estimation, and the accuracy of the model built with CIVE was highest,with validation determination coefficient of 0.98, 0.95 and 0.96,respectively. These results indicated that digital image technology could be applied as an effective method for monitoring winter wheat waterlogging, and the CIVE was the best index,thus providing guidance for accurately monitoring of wheat under waterlogging stress by digital image technology. |
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