Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression |
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| Institution: | 1. College of Ecology & Environmental Science, Inner Mongolia Agricultural University, 010019 Hohhot, China;2. Chair of Plant Nutrition, Department of Plant Sciences, Technische Universität München, Emil-Ramann-Str. 2, D-85350 Freising-Weihenstephan, Germany;3. College of Resources & Environmental Sciences, China Agricultural University, 100094 Beijing, China;1. College of Ecology & Environmental Science, Inner Mongolia Agricultural University, 010019 Hohhot, China;2. Chair of Plant Nutrition, Department of Plant Sciences, Technische Universität München, Emil-Ramann-Str. 2, D-85350 Freising-Weihenstephan, Germany;3. College of Resources & Environmental Sciences, China Agricultural University, 100094 Beijing, China;1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 10097, China;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3. Key Laboratory for Information Technologies in Agriculture, The Ministry of Agriculture, Beijing 100097, China;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;5. Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China;6. Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;7. College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China;1. National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China;2. MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China;3. MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China;4. Jiangsu Key laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China;5. Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China;6. Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK;7. Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK;8. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11 A Datun Road, Beijing 100101, China;9. Xinghua Extension Centre for Agricultural Technology, Taizhou 225700, China;1. National Engineering Research Centre for Wheat, State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, #62 Nongye Road, Zhengzhou, Henan 450002, PR China;2. Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan 450002, PR China;1. National Engineering Research Centre for Wheat, Henan Agricultural University, #62 Nongye Road, Zhengzhou, Henan 450002, PR China;2. Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan 450002, PR China;1. Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, 818 Beijing South Road, Urumqi, Xinjiang 830011, PR China;2. Department of Geography and Program in Planning University of Toronto, 100 St. George St., Toronto, Ontario M5S 3G3, Canada;3. Institute of Hydrology and Water Resources, Zhejiang University, Hangzhou, Zhejiang 310058, PR China |
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| Abstract: | Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based on different algorithms. However, the relationships between selected spectral indices and the canopy N content of crops are often inconsistent. The goals of this study were to test the performance of spectral indices and partial least square regression (PLSR) and to compare their use for predicting canopy N content of winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dry North China Plain for three winter wheat growing seasons. The canopy N content of winter wheat varied from 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growth stages and years. The best performing spectral indices and their band combinations varied across growth stages, cultivars, sites and years. Compared with the best performing spectral indices, the average value of the R2 for the PLSR models increased by 76.8% and 75.5% in the calibration and validation datasets, respectively. The results indicate that PLSR is a potentially useful approach to derive canopy N content of winter wheat across growth stages, cultivars, sites and years under field conditions when a broad set of canopy reflectance data are included in the calibration models. PLSR will be useful for real-time estimation of N status of winter wheat in the fields and for guiding farmers in the accurate application of their N fertilisation strategies. |
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| Keywords: | Winter wheat Canopy N content PLSR Spectral indices |
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