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| Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution |
DUAN Dan-dan1, 2, 3, 4, ZHAO Chun-jiang2, 3, 4, LI Zhen-hai2, 3, 4, YANG Gui-jun2, 3, 4, ZHAO Yu1, 2, 3, 4, QIAO Xiao-jun2, 3, 4, ZHANG Yun-he2, 3, 4, ZHANG Lai-xi2, 3, 4, YANG Wu-de1 |
1 Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu 030801, P.R.China
2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
3 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs/Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
4 Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, P.R.China |
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Abstract The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration (TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution (VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that: (1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer. (2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively. (3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index (GI), mND705 (modified normalized difference 705), and normalized difference vegetation index (NDVI), the values for the determining coefficient (R2), and normalized root mean square error (nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.
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Received: 03 January 2019
Online: 12 April 2019
Accepted:
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| Fund: This study was supported by the Natural Science Foundation of Beijing Academy of Agriculture and Forestry Sciences (BAAFS), China (QNJJ201834), the National Natural Science Foundation of China (41471285 and 41671411), and the National Key R&D Program of China (2017YFD0201501). |
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Corresponding Authors:
Correspondence YANG Wu-de, Tel: +86-10-51503810, E-mail: sxauywd@126.com
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| About author: DUAN Dan-dan, E-mail: duandd@nercita.org.cn; |
Cite this article:
DUAN Dan-dan, ZHAO Chun-jiang, LI Zhen-hai, YANG Gui-jun, ZHAO Yu, QIAO Xiao-jun, ZHANG Yun-he, ZHANG Lai-xi, YANG Wu-de.
2019.
Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution. Journal of Integrative Agriculture, 18(7): 1562-1570.
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| Chen J M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22, 229–242.
Chen P, Haboudane D, Tremblay N, Wang J, Vigeault P, Li B. 2010. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 114, 1987–1997.
Delalieux S, Somers B, Hereijgers S, Verstraeten W W, Keulemans W, Coppin P. 2008. A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sensing of Environment, 112, 3762–3772.
Geng J, Qiang M, Chen J, Zhang M, Li C, Yang Y, Yang X, Zhang W, Liu Z. 2016. Effects of polymer coated urea and sulfur fertilization on yield, nitrogen use efficiency and leaf senescence of cotton. Field Crops Research, 187, 87–95.
Gitelson A A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165–173.
Gitelson A A, Vina A, Ciganda V, Rundquist D C, Arkebauer T J. 2005. Remote estimation of canopy chlorophyll in crops. Geophysical Research Letters, 32, 2688–2691.
Guo Y, Zhang L, Qin Y, Zhu Y, Cao W, Tian Y. 2015. Exploring the vertical distribution of structural parameters and light radiation in rice canopies by the coupling model and remote sensing. Remote Sensing, 7, 5203–5221.
Fitzgerald G, Rodriguez D, O’Leary G. 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index - The canopy chlorophyll content index (CCCI). Field Crops Research, 116, 318–324.
Hurcom S J, Harrison A R. 1998. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. International Journal of Remote Sensing, 19, 3109–3125.
Jeong H, Bhattarai R. 2018. Exploring the effects of nitrogen fertilization management alternatives on nitrate loss and crop yields in tile-drained fields in Illinois. Journal of Environmental Management, 213, 341–352.
Kong W, Huang W, Casa R, Zhou X, Ye H, Dong Y. 2017. Off-nadir hyperspectral sensing for estimation of vertical profile of leaf chlorophyll content within wheat canopies. Sensors, 17, 2711–2730.
Lemaire G, Oosterom E V, Sheehy J, Jeuffroy M H, Massignam A, Rossato L. 2007. Is crop N demand more closely related to dry matter accumulation or leaf area expansion during vegetative growth? Field Crops Research, 100, 91–106.
Li H, Zhao C, Huang W, Yang G. 2013. Non-uniform vertical nitrogen distribution within plant canopy and its estimation by remote sensing: A review. Field Crops Research, 142, 75–84.
Li Z, Wang J, He P, Zhang Y, Liu H, Chang H, Xu X. 2015. Modelling of crop chlorophyll content based on a non-destructive leaf-clip meter of Dualex. Transactions of the Chinese Society of Agricultural Engineering, 31, 191–197. (in Chinese)
Luo J, Ma R, Feng H, Li X. 2016. Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution. Remote Sensing, 8, 789–801.
Sims D A, Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.
Steddom K, Heidel G, Jones D, Rush C M. 2003. Remote detection of rhizomania in sugar beets. Phytopathology, 93, 720–726.
Thomas R L, Sheard R W, Moyer J R. 1967. Comparison of conventional and automated procedures for nitrogen, phosphorus, and potassium analysis of plant material using a single digestion. Agronomy Journal, 59, 240–243.
Wang Z, Wang J, Zhao C, Zhao M, Huang W, Wang C. 2012. Vertical distribution of nitrogen in different layers of leaf and stem and their relationship with grain quality of winter wheat. Journal of Plant Nutrition, 28, 73–91.
Wei C, Huang J, Mansaray L, Li Z, Liu W, Han J. 2017. Estimation and mapping of winter oilseed rape LAI from high spatial resolution satellite data based on a hybrid method. Remote Sensing, 9, 488–504.
Xi L, Yong L I. 2016. Varietal difference in the correlation between leaf nitrogen content and photosynthesis in rice (Oryza sativa L.) plants is related to specific leaf weight. Journal of Integrative Agriculture, 15, 2002–2011.
Yao X, Huang Y, Shang G, Zhou C, Cheng T, Tian Y. Cao W, Zhu Y. 2015. Evaluation of six algorithms to monitor wheat leaf, nitrogen concentration. Remote Sensing, 7, 14939–14966.
Zhang S Y, Zhang X H, Qiu X L, Tang L, Zhu Y, Cao W X, Liu L L. 2017. Quantifying the spatial variation in the potential productivity and yield gap of winter wheat in China. Journal of Integrative Agriculture, 16, 845–857.
Zadoks J C, Chang T T, Konzak C F. 1974. A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.
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