Combining UAV-based plant height from crop surface models,visible, and near infrared vegetation indices for biomass monitoring in barley |
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| Institution: | 1. Institute of Geography, GIS & RS Group, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany;2. Research Centre Hanninghof, Yara International ASA, Hanninghof 35, 48249 Dülmen, Germany;3. ICASD-International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;1. Soil Biology and Plant Nutrition, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany;2. Organic Plant Production and Agroecosystems Research in the Topics and Subtropics, University of Kassel, Witzenhausen, Germany;3. Soil Science, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany;4. Organic Farming and Cropping Systems, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany;1. Institute of Geography, University of Cologne, 50923 Köln, Germany;3. Research Centre Hanninghof, Yara International, 48249 Dülmen, Germany;4. College of Ecology and Environmental Science, Inner Mongolia Agricultural University, Hohhot 010019, China;5. College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China;6. Airbus Defence and Space, 88039 Friedrichshafen, Germany;7. Institute of Agricultural Resources & Environment, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, China;8. ESRI Germany GmbH, 85402 Kranzberg, Germany;1. Smart Farming Technology Research Centre, Faculty of Engineering, Universiti Putra Malaysia, Seri Serdang 43400, Selangor, Malaysia;2. Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Seri Serdang 43400, Selangor, Malaysia;3. Department of Soil Science and Soil Protection, Czech University of Life Sciences Prague, 165 21 Prague, Czech Republic;4. Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Seri Serdang 43400, Selangor, Malaysia;1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;2. CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia 4067, Australia;3. School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia;4. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Hermitage Research Facility, Yangan Rd., Warwick, QLD 4370, Australia;5. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 203 Tor St., Toowoomba, QLD 4350, Australia;6. China Transport Telecommunications & Information Center, National Engineering Laboratory for Transportation Safety and Emergency Informatics, No. 1, Anwai Waiguan Houshen, Beijing 100011, China;1. School of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China;2. Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan 430070, China;3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079,China |
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| Abstract: | In this study we combined selected vegetation indices (VIs) and plant height information to estimate biomass in a summer barley experiment. The VIs were calculated from ground-based hyperspectral data and unmanned aerial vehicle (UAV)-based red green blue (RGB) imaging. In addition, the plant height information was obtained from UAV-based multi-temporal crop surface models (CSMs). The test site is a summer barley experiment comprising 18 cultivars and two nitrogen treatments located in Western Germany. We calculated five VIs from hyperspectral data. The normalised ratio index (NRI)-based index GnyLi (Gnyp et al., 2014) showed the highest correlation (R2 = 0.83) with dry biomass. In addition, we calculated three visible band VIs: the green red vegetation index (GRVI), the modified GRVI (MGRVI) and the red green blue VI (RGBVI), where the MGRVI and the RGBVI are newly developed VI. We found that the visible band VIs have potential for biomass prediction prior to heading stage. A robust estimate for biomass was obtained from the plant height models (R2 = 0.80–0.82). In a cross validation test, we compared plant height, selected VIs and their combination with plant height information. Combining VIs and plant height information by using multiple linear regression or multiple non-linear regression models performed better than the VIs alone. The visible band GRVI and the newly developed RGBVI are promising but need further investigation. However, the relationship between plant height and biomass produced the most robust results. In summary, the results indicate that plant height is competitive with VIs for biomass estimation in summer barley. Moreover, visible band VIs might be a useful addition to biomass estimation. The main limitation is that the visible band VIs work for early growing stages only. |
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| Keywords: | Point cloud Structure from motion Green red vegetation index GnyLi SAVI NDVI |
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