Selection of sensitive canopy structure parameters and spectral diagnostic model for lodging intensity of winter wheat

At present, the changes of canopy structure and response mechanism of canopy spectral are not clear on winter wheat under lodging stress. Therefore, in this paper lodging winter wheat at the filling stage was taken as study object, the canopy structural parameters derived from the ratio of canopy stem, leaf and ear with different lodging strength were extracted. The correlation between the canopy structural parameters and lodging angle was analyzed, and the sensitive canopy structural parameters were selected to express lodging strength. The traditional spectral transform and the continuous wavelet transform were adopted to process the canopy hyperspectral data of lodging winter wheat. The bands and wavelet coefficients sensitive to canopy structural parameters were selected. The response model between canopy structural parameters of lodging winter wheat and hyperspectral characteristics parameters were constructed by partial least squares regression (PLSR) method, and the accuracy of the model was verified by field samples (28 samples for the modeling set, and 13 samples for the verification set). The results showed that the spectral curves of winter wheat with various lodging strengths had similar variation characteristics, and the wavelength bands of the troughs and peaks were roughly the same. Throughout the band interval, the spectral reflectance was expressed as: severe lodging > moderate lodging > mild lodging >not lodging. The first-order differential spectral reflectance of winter wheat increased with the increase of lodging degree, which indicated that the more severe the lodging, the more significant the change of the original reflectance data of the canopy spectrum. The correlation between stem-leaf ratio and lodging angle was the highest (R=-0.687, P<0.01), which could be used to characterize the lodging strength of winter wheat. The stem-leaf ratio increased with the decrease of lodging angle. The diagnostic model of lodging disaster of winter wheat based on continuous wavelet transform was superior to that based on the traditional transform, and the determination coefficient of the test samples was 0.632 (P<0.01). The accuracy of lodging disaster classification based on the prediction results of canopy stem-leaf ratio could reach 84.6%. Therefore, the contribution proportion of stems, leaves and ears of the winter wheat canopy changed regularly in the sight of spectrometer after lodging. The stem-leaf ratio of winter wheat canopy could effectively characterize the changes of canopy structure under lodging stress, and had a good relationship with the lodging strength. The difference in the spectral reflectance of stem, leaf and ear and the variation in canopy structure after lodging were directly reflected in the canopy spectral difference of lodging wheat. The response rule between stem-leaf ratio with different lodging strength and canopy spectrum of winter wheat canopy can effectively distinguish the level of lodging disaster degree. It is helpful to provide a priori knowledge for remote sensing monitoring of winter wheat lodging disaster at regional scale.