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《Journal of Geomatics》 2019-05
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Remote Detection of Critical Growth Stages in Rapeseed Using Vegetation Spectral and Stacking Combination Method

TAO Yanqi;PENG Yi;JIANG Qi;LI Yucui;FANG Shenghui;GONG Yan;School of Remote Sensing and Information Engineering, Wuhan University;  
The phenology information of rapeseed is important for assisting field management, catching appropriate viewing time and evaluating yield. In this study, a four-band radiometer is used to collect real-time rapeseed reflectance in multiple bands to detect rapeseed different phenology stages: leaf stage, flowering stage, pod-development state and maturity state. According to crop spectral characteristics, a stacking algorithm based on multiple machine learning classification models is proposed to detect rape phenology with remotely collected canopy spectra. The results show that the recognition accuracy is 90.83%, which is much higher than that of single classifier(89.50%). The developed model is applied to data collected from different sensors(ground-and aerial-collected data), yielding the accuracy of 90.07% with ground-collected spectra and the accuracy of 81.48% with aerial-collected data, which are better than that of single classifier.
【Fund】: 国家高技术研究发展计划(2013AA102401)
【CateGory Index】: TP181;S565.4
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