References
- Amanullah, K., B. Marwat, P. Shah, N. Maula, and S. Arifullah. 2009. Nitrogen levels and its time of application influence leaf area, height and biomass of maize planted at low and high density. Pak. J. Bot. 41 : 761-768.
- Ashourloo, D., M. R. Mobasheri, and A. Huete. 2014. Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote. Sens. 6 : 5107-5123. https://doi.org/10.3390/rs6065107
- Behmann, J., J. Steinrucken, and Lutz. Plumer, P. 2014. Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote. Sens. 93 : 98-111. https://doi.org/10.1016/j.isprsjprs.2014.03.016
- Bendig, J., A. Bolten, S. Bennertz, J. Broscheit, S. Eichfuss, and G. Bareth. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB Imaging. Remote. Sens. 6 : 10395-10412. https://doi.org/10.3390/rs61110395
- Birth, G. S. and G. R. McVey. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. J. Agron. 60 : 640-643. https://doi.org/10.2134/agronj1968.00021962006000060016x
- Blackburn, G. A. 1999. Relationship between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote. Sens. Environ. 70 : 224-237. https://doi.org/10.1016/S0034-4257(99)00048-6
- Calderon, R., J. A. Navas-Cortes, C. Lucena, and P. J. Zarco-Tejada. 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote. Sens. Environ. 139 : 231-245. https://doi.org/10.1016/j.rse.2013.07.031
- Cao, X., Y. Luo, Y. Zhou, J. Fan, X. Xu, J. S. West, X. Xiayu, and D. Cheng. 2015. Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance. PLoS One. 10 : 1-14.
- Carter, G. A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80 : 239-243. https://doi.org/10.1002/j.1537-2197.1993.tb13796.x
- Cho, S. W., C. S. Kang, T. G. Kang, K. M. Cho, and C. S. Park. 2018. Influence of different nitrogen application on flour properties, gluten properties by HPLC and end-use quality of Korean wheat. J. Integr. Agric. 17 : 982-993. https://doi.org/10.1016/S2095-3119(18)61920-3
- Darvishzadeh, R., A. Skidmore, C. Atzberger, and S. V. Wieren. 2008. Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture. Int. J. Appl. Earth. OBS. 10 : 358-373. https://doi.org/10.1016/j.jag.2008.02.005
- Datt, B. 1999. A New reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. J. Plant. Physiol. 154 : 30-36. https://doi.org/10.1016/S0176-1617(99)80314-9
- FAO. 2020. Food outlook - Biannual report on global food markets. pp. 11-16.
- Feng, W., X. Yao, Y. Zhu, Y. C. Tian, and W. X. Cao. 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 28 : 394-404. https://doi.org/10.1016/j.eja.2007.11.005
- Filella, I., L. Serrano, J. Serra, and J. Penuelas. 1995. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop. Sci. 35 : 1400-1405. https://doi.org/10.2135/cropsci1995.0011183X003500050023x
- Fritschi, F.B. and J. D. Ray. 2007. Soybean leaf nitrogen, chlorophyll content, and chlorophyll a/b ratio. Photosynthetica. 45 : 92-98. https://doi.org/10.1007/s11099-007-0014-4
- Gamon, J. A., J. Penuelas, and C. B. Field. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote. Sens. Environ. 41 : 35-44. https://doi.org/10.1016/0034-4257(92)90059-S
- Gitelson, A. and M. N. Merzlyak. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation. J. Plant. Physiol. 143 : 286-292. https://doi.org/10.1016/S0176-1617(11)81633-0
- Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote. Sens. Environ. 58 : 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
- Gitelson, A. A. and M. N. Merzlyak. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Adv. Space. Res. 22 : 689-692. https://doi.org/10.1016/S0273-1177(97)01133-2
- Gitelson, A. A., Y. Gritz, and M. N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant. Physiol. 160 : 271-282. https://doi.org/10.1078/0176-1617-00887
- Goetz, A. F. H. 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote. Sens. Environ. 113 : S5-S16. https://doi.org/10.1016/j.rse.2007.12.014
- Han, L., G. Yang, H. Dai, B. Xu, H. Yang, H. Feng, Z. Li, and X. Yang. 2019. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods. 15 : 10. https://doi.org/10.1186/s13007-019-0394-z
- Ivushkin, K., H. Bartholomeus, A. K. Bregt, A. Pulatov, H. D. Franceschini, H. Kramer, E. N. van Loo, V. J. Roman, and R. Finkers. 2018. UAV based soil salinity assessment of cropland. Geoderma. 338 : 502-512. https://doi.org/10.1016/j.geoderma.2018.09.046
- Kong, L., Y. Xie, L. Hu, J. Si, and Z. Wang. 2017. Excessive nitrogen application dampens antioxidant capacity and grain filling in wheat as revealed by metabolic and physiological analyses. Sci. Rep. 7 : 43363. https://doi.org/10.1038/srep43363
- KOSTAT. 2020. 2019 Food Grain Consumption Survey. pp. 17-30.
- Lelong, C. C. D., P. C. Pinet, and H. Poilve. 1998. Hyperspectral imaging and stress mapping in agriculture. Remote. Sens. Environ. 66 : 179-191. https://doi.org/10.1016/S0034-4257(98)00049-2
- Li, F., Y. Miao, S. D. Hennig, M. L. Gnyp, X. Chen, L. Jia, and G. Bareth. 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis. Agric. 11 : 335-357. https://doi.org/10.1007/s11119-010-9165-6
- Li, B., X. Xu, J. Han, L. Zhang, C. Bian, L. Jin, and J. Liu. 2019. The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods. 15 : 15. https://doi.org/10.1186/s13007-019-0399-7
- Luo, L., Y. Zhang, and G. Xu. 2020. How does nitrogen shape plant architecture? J. Exp. Bot. 71 : 4415-4427. https://doi.org/10.1093/jxb/eraa187
- Mahlein, A. K., U. Steiner, H. W. Dehne, and E. C. Oerke. 2010. Spectral signature of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric. 11 : 413-431. https://doi.org/10.1007/s11119-010-9180-7
- McKinney, G. 1941. Absorption of light by chlorophyll solutions. J. Biol. Chem. 140 : 315-322. https://doi.org/10.1016/S0021-9258(18)51320-X
- Merzlyak, M. N., A. A. Gitelson, O. B. Chivkunova, and V. Y. Rakitin. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106 : 135-141. https://doi.org/10.1034/j.1399-3054.1999.106119.x
- Mishra, P., M. S. M. Asaari, A. Herrero-Langreo, S. Lohumi, B. Diezma, and P. Scheunders. 2017. Close range hyperspectral imaging of plants: A review. Biosyst. Eng. 164 : 49-67. https://doi.org/10.1016/j.biosystemseng.2017.09.009
- Moran, R. 1982. Formulae for determination of chlorophyllous pigments extracted with N,N-dimethylformamide. Plant Physiol. 69 : 1376-1381. https://doi.org/10.1104/pp.69.6.1376
- Nebiker, S., M. Abacherli, N. Lack, and S. Laderach. 2016. Lightweight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases. ISPRS-Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 963-970.
- Netto, A. T., E. Campostrini, J. G. de. Oliveira, and R. E. Bressan-Smith. 2005. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci. Hortic. 104 : 199-209. https://doi.org/10.1016/j.scienta.2004.08.013
- Nutter, F. W. 1989. Detection and measurement of plant disease gradients in peanut with a multispectral radiometer. Phytopathol. 79 : 958-963. https://doi.org/10.1094/Phyto-79-958
- Penuelas, J., B. Frederic, and I. Filella. 1995. Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica. 31 : 221-230.
- Penuelas, J. and I. Filella. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends. Plant. Sci. 3 : 151-156. https://doi.org/10.1016/S1360-1385(98)01213-8
- Reyniers, M., D. J. J. Walvoort, and J. De Baardemaaker, 2006. A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat. Int. J. Remote. Sens. 27 : 4159-4179. https://doi.org/10.1080/01431160600791650
- Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In Proc. Third ERTS Symposium. NASA SP-351. 1 : 301-317.
- Rumpf, T., A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, and L. Plumer. 2010. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Compute. Electron. Agric. 74 : 91-99. https://doi.org/10.1016/j.compag.2010.06.009
- Siegal, B. S. and A. F. H. Goetz. 1977. Effect of vegetation on rock and soil type discrimination, Photogramm. Eng. Rem. S. 43 : 191-196.
- Sims, D. A. and J. A. Gamon. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote. Sens. Environ. 81 : 337-354. https://doi.org/10.1016/S0034-4257(02)00010-X
- Vogelmann, J. E., B. N. Rock, and D. M. MOSS, 1993. Red edge spectral measurements from sugar maple leaves. Int. J. Remote. Sens. 14 : 1563-1575. https://doi.org/10.1080/01431169308953986
- Wijitdechakul, J., S. Sasaki, Y. Kiyoki, and C. Koopipat. 2016. UAV-based multispectral image analysis system with semantic computing for agricultural health conditions monitoring and real-time management. International Electronics Symposium (IES). pp. 459-464.
- Wu, C., Z. Niu, Q. Tang, and W. Huang. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agr. Forest. Meterol. 148 : 1230-1241. https://doi.org/10.1016/j.agrformet.2008.03.005
- Xingyun, L., T. Zhang, X. Lu, D. S. Ellsworth, H. BassiriRad, C. You, D. Wang, P. He, Q. Deng, H. Liu, J. Mo, and Q. Ye. 2019. Global response patterns of plant photosynthesis to nitrogen addition: A meta-analysis. Glob. Chang. Biol. 26 : 3585-3600. https://doi.org/10.1111/gcb.15071
- Xu, J., H. Cai, X. Wang, C. Ma, Y. Lu, Y. Ding, X. Wang, H. Chen, Y. Wang, and Q. Saddique. 2019. Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching. Agric. Water. Manag. 228 : 105904.
- Zarco-Tejada, P. J., V. Gonzalez-Dugo, and J. A. Berni. 2012. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote. Sens. Environ. 117 : 322-337. https://doi.org/10.1016/j.rse.2011.10.007
- Zhang, L., Y. Niu, H. Zhang, W. Han, G. Li, J. Tang, and X. Peng. 2019. Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Front. Plant. Sci. 10 : 1-18. https://doi.org/10.3389/fpls.2019.00001
- Zheng, T., P. F. Qi, Y. L. Cao, Y. N. Han, H. L. Ma, Z. R. Guo, Y. Wang, Y. Y. Qiao, S. Y. Hua, H. Y. Yu, J. P. Wang, J. Zhu, C. Y. Zhou, Y. Z. Zhang, Q. Chen, L. Kong, J. R. Wang, Q. T. Jiang, Z. H. Yan, X. J. Lan, G. Q. Fan, Y. M. Wei, and Y. L. Zheng. 2018. Mechanisms of wheat (Triticum aestivum) grain storage proteins in response to nitrogen application and its impacts on processing quality. Sci. Rep. 8 : 11928. https://doi.org/10.1038/s41598-018-30451-4