Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress |
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| Institution: | 1. Global Change Research Centre, Academy of Sciences of the Czech Republic, Bělidla 4a, CZ-603 00 Brno, Czech Republic;2. School of Biological Sciences, University of Wollongong, Northfields Ave, NSW, 2522 Wollongong, Australia;3. School of Land and Food, University of Tasmania, Private Bag 76, TAS, 7001 Hobart, Australia;4. Bayer CropScience NV, Innovation Center, Technologiepark 38, 9052 Zwijnaarde, Belgium;5. Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, DE-52425 Jülich, Germany;6. P&M Technologies, 66 Millwood Street, Sault Ste. Marie, Ontario P6A 6S7, Canada;1. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China;2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;3. College of Geomatics, Xi''an University of Science and Technology, Xi''an 710054, China |
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| Abstract: | Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R2 = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VIgreen, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R2 = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R2 = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5. |
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| Keywords: | Grain protein content Remote sensing Canopy spectra Landsat TM Leaf nitrogen content |
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