Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels |
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| Institution: | 1. Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA;2. CALMIT, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA;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. 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. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia;3. Department of Land Surface, German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany;4. Bavarian Forest National Park, Freyunger Straße 2, 94481 Grafenau, Germany;5. Department of Remote Sensing in Cooperation with German Aerospace Center, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany |
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| Abstract: | Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L.) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R2 of 0.74 and 0.94, respectively. Results showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780–800 nm) and either green (540–560 nm) or red-edge (730–750 nm) spectral bands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively. |
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| Keywords: | Chlorophyll Nitrogen Reflectance Remote sensing Vegetation index |
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