关键词: 冬小麦, 叶绿素含量反演, 预测模型, 时空变化, 无人机

Abstract:

The aim is to accurately estimate the chlorophyll content of winter wheat at different growth stages, and to explore its temporal and spatial change. The multispectral image with high resolution of winter wheat at overwintering stage, returning green stage, jointing stage, booting stage and filling stage were photographed by unmanned aerial vehicle (UAV), and SPAD ground data were collected at the same time. Three kinds of spectral parameters were selected to establish the inversion model, the best prediction model for each growth stage was screened out, and the temporal and spatial changes of chlorophyll content in winter wheat in the experimental area were quantitatively monitored. The results showed that the original wave band model and the reciprocal logarithmic wave band model were the best models for predicting chlorophyll content in overwintering stage and other growth stages, respectively, the inversion accuracy R² were all greater than 0.59. In terms of temporal and spatial changes, before the filling stage, the chlorophyll content of winter wheat in the experimental area exhibited the high in the north and south, and the low in middle. During the filling stage, the chlorophyll content exhibited the high in the north and the low in the south. The chlorophyll content in winter wheat increased gradually from overwintering stage to jointing stage, decreased from jointing stage to booting stage, and decreased significantly from booting stage to filling stage. The wave band reciprocal logarithm model established in this study has higher prediction accuracy, it is suitable for the four growth stages from returning green stage to filling stage, and it has a good temporal and spatial monitoring effect on the chlorophyll content of winter wheat in the experimental area.

Key words: winter wheat, chlorophyll content inversion, predictive model, temporal and spatial change, unmanned aerial vehicle