Real-time non-destructive monitoring of nitrogen accumulation by hyperspectral remote sensing is important for crop nitrogen management. In this study, winter wheat field experiment incorporating several varieties and exogenous nitrogen treatments was performed at multiple sites. Using hyperspectral readings of the experimental crops, the continuum removal method was used to expand the chlorophyll absorption characteristic band. The correlation among the spectral reflectance of the wheat canopy, the continuum removal spectrum, and leaf nitrogen accumulation (LNA) were systematically analyzed. The correlations between LNA and spectral parameters (e.g., original spectral reflectance, two-band combination parameters, and common vegetation indices) and continuum-removed absorption feature parameters were all compared. Three nonlinear modeling methods were considered (partial least squares regression, SVM regression, and random forest regression) and their relative ability to predict LNA was compared. Continuum removal treatment significantly improved the correlation between the continuum-removed spectra of the chlorophyll absorption regions (550–750 nm) and LNA. Results also show that RSI (NBDI743, NBDI703) could be used to estimate LNA using univariate linear regression (R2 and root mean square error were 0.806 and 1.231 g m−2, respectively). The SVM regression was found to be the most accurate regression model when chlorophyll absorption characteristic band reflectivity values normalized by the continuum removal process were taken as an input (R2 and root mean square error values were 0.895 and 0.903 g m−2, respectively). This approach was able to predict LNA of wheat using continuum-removed absorption features through hyperspectral measurements, which provide technical support for nitrogen diagnosis and precise crop production management.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Clark, R. N., King, T. V. V., Ager, C., & Swayze, G. A. (1995). Initial vegetation species and senescence/stress indicator mapping in the SanLuis valley, Colorado using imaging spectrometer data. In H. H. Posey, J. A. Pendelton, & D. Van Zyl (Eds.), Proceedings, Summitville Fo-rum’95. (Vol. 38, pp. 64–69). Colorado Geological Survey Special Publication.
Cheng, Y., Hu, C., Dai, H., & Lei, Y. (2005). Spectral red edge parameters for winter wheat under different nitrogen support levels. Remote Sensing and Modeling of Ecosystems for Sustainability II, 5884, 58841A. https://doi.org/10.1117/12.614759.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning. https://doi.org/10.1007/bf00994018.
Curran, P. J., Dungan, J. L., Macler, B. A., & Plummer, S. E. (1991). The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment, 35(1), 69–76. https://doi.org/10.1016/0034-4257(91)90066-F.
Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing. https://doi.org/10.1080/0143116042000274015.
Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Colstoun, E. B. D., & Iii, M. M. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9.
Fan, L., Zhao, J., Xu, X., Liang, D., Yang, G., Feng, H., et al. (2019). Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables. Sensors (Switzerland). https://doi.org/10.3390/s19132898.
Feng, W., Yao, X., Zhu, Y., Tian, Y. C., & Cao, W. X. (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 28(3), 394–404. https://doi.org/10.1016/j.eja.2007.11.005.
Feng, W., Guo, B. B., Wang, Z. J., He, L., Song, X., Wang, Y. H., & Guo, T. C. (2014). Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data. Field Crops Research, 159, 43–52. https://doi.org/10.1016/j.fcr.2014.01.010.
Gianquinto, G., Orsini, F., Fecondini, M., Mezzetti, M., Sambo, P., & Bona, S. (2011). A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal of Agronomy, 35(3), 135–143. https://doi.org/10.1016/j.eja.2011.05.005.
Guo, B. B., Qi, S. L., Heng, Y. R., Duan, J. Z., Zhang, H. Y., Wu, Y. P., et al. (2017). Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. European Journal of Agronomy, 82, 113–124. https://doi.org/10.1016/j.eja.2016.10.009.
Guyot, G., & Baret, F. (1988). Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. Journal of Chemical Information and Modeling. https://doi.org/10.1017/CBO9781107415324.004.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. https://doi.org/10.1016/S0034-4257(02)00018-4.
Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542–553. https://doi.org/10.1016/S0034-4257(03)00131-7.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(88)90106-X.
Hunt, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., Perry, E. M., & Akhmedov, B. (2012). A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21(1), 103–112. https://doi.org/10.1016/j.jag.2012.07.020.
Inoue, Y., Sakaiya, E., Zhu, Y., & Takahashi, W. (2012). Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2012.08.026.
Jia, F., Liu, G., Liu, D., Zhang, Y., Fan, W., & Xing, X. (2013). Comparison of different methods for estimating nitrogen concentration in flue-cured tobacco leaves based on hyperspectral reflectance. Field Crops Research, 150, 108–114. https://doi.org/10.1016/j.fcr.2013.06.009.
Ju, X. T., Kou, C. L., Zhang, F. S., & Christie, P. (2006). Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China Plain. Environmental Pollution, 143(1), 117–125. https://doi.org/10.1016/j.envpol.2005.11.005.
Kokaly, R. F., Despain, D. G., Clark, R. N., & Livo, K. E. (2003). Mapping vegetation in Yellowstone National Park using spectral fea-ture analysis of AVIRIS data. Remote Sensing of Environment, 84, 437–456. https://doi.org/10.1016/S0034-4257(02)00133-5.
Lehnert, L. W., Meyer, H., Obermeier, W. A., Silva, B., Regeling, B., Thies, B., & Bendix, J. (2019). Hyperspectral data analysis in R: The hsdar package. Journal of Statistical Software. https://doi.org/10.18637/jss.v089.i12.
Li, F. L., Wang, L., Liu, J., Wang, Y., & Chang, Q. R. (2019). Evaluation of leaf N concentration in winter wheat based on discrete wavelet transform analysis. Remote Sensing, 11(11), 1331. https://doi.org/10.3390/rs11111331.
Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., & Yang, M. (2018). Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm. Remote Sensing, 10(12), 1940. https://doi.org/10.3390/rs10121940.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
Liu, W., Li, M., Zhang, M., Wang, D., Guo, Z., Long, S., et al. (2020). Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance. Ecosystem Health and Sustainability, 6(1), 1726211. https://doi.org/10.1080/20964129.2020.1726211.
Lu, D., Yue, S., Lu, F., Cui, Z., Liu, Z., Zou, C., & Chen, X. (2016). Integrated crop-N system management to establish high wheat yield population. Field Crops Research, 191, 66–74. https://doi.org/10.1016/j.fcr.2016.02.015.
Luo, S., He, Y., Li, Q., Jiao, W., Zhu, Y., Yu, J., et al. (2020). Assessment of unified models for estimating potato leaf area index under water stress conditions across ground-based hyperspectral data. Journal of Applied Remote Sensing, 14 (01), 1. https://doi.org/10.1117/1.jrs.14.014517.
Mevik, B. H., & Wehrens, R. (2007). The pls package: Principal component and partial least squares regression in R. Journal of Statistical Software. https://doi.org/10.18637/jss.v018.i02.
Moharana, S., & Dutta, S. (2016). Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 17–29. https://doi.org/10.1016/j.isprsjprs.2016.09.002.
Mutanga, O., Skidmore, A. K., & Prins, H. H. T. (2004). Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment, 89(3), 393–408. https://doi.org/10.1016/j.rse.2003.11.001.
Mutanga, O., & Skidmore, A. K. (2004). Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation, 5(2), 87–96. https://doi.org/10.1016/j.jag.2004.01.001.
R Development Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Retrieved from http://www.r-project.org/. Accessed 13 Dec 2019
Smil, V. (2002). Nitrogen and food production: Proteins for human diets. Ambio, 31(2), 126–131. https://doi.org/10.1579/0044-7447-31.2.126.
Steinwart, I., & Thomann, P. (2017). liquidSVM: A fast and versatile SVM package. arXiv:1702.06899
Tan, C., Du, Y., Zhou, J., Wang, D., Luo, M., Zhang, Y., & Guo, W. (2018). Analysis of different hyperspectral variables for diagnosing leaf nitrogen accumulation in wheat. Frontiers in Plant Science, 9, 674. https://doi.org/10.3389/fpls.2018.00674.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(79)90013-0.
Valero-Mora, P. M. (2010). ggplot2: Elegant graphics for data analysis. Journal of statistical software. (Vol. 35). Springer. https://doi.org/10.18637/jss.v035.b01.
Wang, F., Huang, J., Wang, Y., Liu, Z., Peng, D., & Cao, F. (2013). Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function. International Journal of Digital Earth, 6(6), 550–562. https://doi.org/10.1080/17538947.2011.628414.
Wang, Z., Skidmore, A. K., Darvishzadeh, R., Heiden, U., Heurich, M., & Wang, T. (2015). Leaf nitrogen content indirectly estimated by leaf traits derived from the PROSPECT Model.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3172–3182. https://doi.org/10.1109/JSTARS.2015.2422734.
Wang, L., Zhou, X., Zhu, X., & Guo, W. (2017). Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data. Computers and Electronics in Agriculture, 140, 327–337. https://doi.org/10.1016/j.compag.2017.05.023.
Wang, Y. J., Li, T. H., Jin, G., Wei, Y. M., Li, L. Q., Kalkhajeh, Y. K., et al. (2019). Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics. Journal of the Science of Food and Agriculture. https://doi.org/10.1002/jsfa.10009.
Wen, P. F., He, J., Ning, F., Wang, R., Zhang, Y. H., & Li, J. (2019). Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2019.105590.
Woodard, H. J., & Bly, A. (1998). Relationship of nitrogen management to winter wheat yield and grain protein in South Dakota. Journal of Plant Nutrition, 21(2), 217–233. https://doi.org/10.1080/01904169809365397.
Yao, X., Feng, W., Zhu, Y., Tian, Y. C., & Cao, W. X. (2007). A non-destructive and real-time method of monitoring leaf nitrogen status in wheat. New Zealand Journal of Agricultural Research, 50(5), 935–942. https://doi.org/10.1080/00288230709510370.
Yao, X., Huang, Y., Shang, G., Zhou, C., Cheng, T., Tian, Y., et al. (2015). Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 7(11), 14939–14966. https://doi.org/10.3390/rs71114939.
Zhang, J. H. (2006). Rice nitrogen nutrition diagnosis using continuum removed reflectance. Chinese Journal of Plant Ecology, 30(1), 83–89. https://doi.org/10.17521/cjpe.2006.0012. (In Chinese with English Abstract).
Zhang, M., Li, M., Liu, W., Cui, L., Li, W., Wang, H., et al. (2019). Analyzing the performance of statistical models for estimating leaf nitrogen concentration of Phragmites australis based on leaf spectral reflectance. Spectroscopy Letters. https://doi.org/10.1080/00387010.2019.1619584.
Zhu, Y., Li, Y., Feng, W., Tian, Y., Yao, X., & Cao, W. (2006). Monitoring leaf nitrogen in wheat using canopy reflectance spectra. Canadian Journal of Plant Science, 86(4), 1037–1046. https://doi.org/10.4141/P05-157.
Zhu, Y., Zhou, D., Yao, X., Tian, Y., & Cao, W. (2007). Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice. Australian Journal of Agricultural Research, 58(11), 1077–1085. https://doi.org/10.1071/AR06413.
This work was supported by grants from the National Key Research and Development Program of China (2016YFD0300609), the Key Scientific and Technological Projects of Henan Province (192102110012) and Henan Modern Agriculture (Wheat) Research System (S2010-01-G04). We acknowledge TopEdit LLC for the linguistic editing and proofreading during the preparation of this manuscript.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Guo, J., Zhang, J., Xiong, S. et al. Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling. Precision Agric (2021). https://doi.org/10.1007/s11119-021-09804-z
- Winter wheat
- Leaf nitrogen accumulation
- Continuum removal
- Regression model