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叶绿素是影响冬小麦产量和品质的重要农学参数,麦田土壤水分的不同会对冬小麦生长产生明显影响,因此实现水旱地冬小麦叶绿素含量的遥感监测具有重要意义。本研究通过分析灌溉地和旱地冬小麦冠层光谱特征,提取敏感波段,并在此基础上通过相关分析,构建叶绿素含量的最佳遥感监测模型。结果表明:灌溉地和旱地的光谱反射及其一阶导数光谱曲线的变化趋势相似,但其值的大小存在较大差异;灌溉地冬小麦冠层光谱特征波段为624、780、958、1053、1082 nm,以FDMSAVI(1082,624)为变量建立的预测模型效果最佳,检验模型的R2为0.8447;旱地的特征波段为691、848、871、1199和1212 nm,以FDMSAVI(1212,691)为变量所建模型预测效果,检验模型的R2为0.8627。因此,利用高光谱技术进行水旱地冬小麦叶绿素含量的监测是可行的,可为麦田科学管理及决策提供技术支持。
Chlorophyll is an important agronomic parameter affecting the yield and quality of winter wheat. The difference of soil moisture in wheat field will have a significant effect on the growth of winter wheat. Therefore, it is of great significance to realize the remote sensing monitoring of winter wheat chlorophyll content in dryland. In this study, the spectral characteristics of canopy in irrigated and upland winter wheat were analyzed, and the sensitive bands were extracted. On the basis of this, the best remote sensing monitoring model of chlorophyll content was constructed through correlation analysis. The results showed that the spectral reflectance and the first derivative curve of irrigated and dry land showed the similar trends, but their values differed greatly. The canonical spectral bands of irrigated winter wheat were 624,780,958,1053,1082 nm. The prediction model established by using FDMSAVI (1082, 624) as the variable has the best effect. The R2 of the test model is 0.8447. The characteristic bands of dry land are 691, 848, 871, 1199 and 1212 nm. The FDMSAVI (1212, 691) The predicted effect of the model constructed by the variables is R2 of 0.8627 for the test model. Therefore, it is feasible to use hyperspectral technology to monitor the chlorophyll content of winter wheat in dry and upright fields, and provide technical support for scientific management and decision-making of wheat fields.