Remote Sensing Technology and Application, Volume. 39, Issue 4, 927(2024)

Estimation of Leaf Nitrogen Content in Maize based on UAV Hyperspectral Image

Xingyu ZHANG, Yue ZHANG, Chenzhen XIA, Xiaoyan ZHANG, Yuxi LI, and Xiaoyu LI
Author Affiliations
  • College of Resources and Environment, Jilin Agricultural University, Changchun130118, China
  • show less
    References(46)

    [1] Jin DONG, Xuezhen BA, Jiaoyu SHI et al. The multiple obstacles and breakthrough paths of food production capacity security in Northeast China. Agricultural Modernization Research, 44, 755-764(2023).

    [2] S M SWARBREC, M WANG, Y WANG et al. A roadmap for lowering crop nitrogen requirement. Trends in Plant Science, 24(2019).

    [3] S ALIREZA. Remotely sensed vegetation indices for crop nutrition mapping. Journal of the Science of Food and Agriculture, 100(2020).

    [4] Youlu BAI. Present situation and prospect of research on high efficiency fertilization technology. Chinese Agricultural Sciences, 51, 2116-2125(2018).

    [5] S S LIU, L T LI, H Y FAN et al. Real-time and multi-stage recommendations for nitrogen fertilizer topdressing rates in winter oilseed rape based on canopy hyperspectral data. Industrial Crops & Products, 154(2020).

    [6] Xiao SONG, Duanyang XU, Shaomin HUANG et al. Inversion model of nitrogen content in winter wheat canopy leaves based on ground observed spectral data. Journal of Applied Ecology, 31, 1636-1644(2020).

    [7] Shiqin WANG. Study on nitrogen change characteristics and estimation modeling of cotton leaves based on Hyperspectral(2021).

    [8] Yinjie ZHANG, Lei WANG, Youlu BAI et al. Study on stratified diagnosis of nitrogen content in Maize leaves based on High Spectral Analysis. Spectroscopy and Spectral Analysis, 39, 2829-28(2019).

    [9] Qilei ZHU, Dong LIANG, Xinguang XU et al. Remote Sensing estimation of winter wheat straw cover based on Sentinel-2 image and machine learning algorithm. Journal of Wheat Crops, 43, 524-535(2023).

    [10] M KGANYAGO, P MHANGARA, C ADJORLOLO. Estimating crop biophysical parameters using machine learning algorithms and Sentinel-2 imagery. Remote Sensing, 13, 4314(2021).

    [11] J N ZHEN, X P JIANG, Y XU et al. Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods. International Journal of Applied Earth Observations and Geoinformation, 102(2021).

    [12] H T ZHAO, X Y SONG, G J YANG et al. Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing, 11(2019).

    [13] Ting TIAN, Qing ZHANG, Haidong ZHANG. Research progress on the application of UAV remote sensing in crop monitoring. Crop Magazine, 36, 1-8(2020).

    [14] Zhu WANG, Chunmei XIE. Application of UAV Remote sensing technology in agriculture. Agricultural Engineering Te-chnology, 43, 44-45(2023).

    [15] Binbin YUAN, Yang WANG, Hongqi WU et al. Unmanned aerial vehicle LNC retrieval of winter wheat based on vegetation index fusion. Journal of Wheat Crops, 44, 1063-1073(2024).

    [16] Pengfei WEI, Xingang XU, Zhongyuan LI et al. Remote sensing estimation of leaf nitrogen content of summer maize based on UAV multispectral images. Journal of Agricultural Engineering, 35, 126-133,335(2019).

    [17] Yanlong CHEN, Xiaolan WANG, En LI et al. Research and application of band selection method of CEM. Spectroscopy and Spectral Analysis, 40, 3778-3783(2020).

    [18] Ling ZHANG, Xinping CHEN, Liangliang JIA. Study on dynamic diagnosis parameters of nitrogen Nutrition in Summer Maize based on UAV visible Light remote Sensing. Plant Nutrition and Fertilizer Bulletin, 24, 261-269(2018).

    [19] Xiaoke WANG, Tingting LIU, Guiling XU et al. Nitrogen nutrition diagnosis model of hybrid rice vegetation index based on canopy hyperspectral remote sensing. Chinese Rice, 27, 21-29(2021).

    [20] Yuna WANG, Fenling LI, Weidong WANG et al. Nitrogen nutrition monitoring of winter wheat based on UAV hyperspectrum. Journal of Agricultural Engineering, 36, 31-39(2020).

    [21] Y Y FU, G J YANG, R L PU et al. An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives. European Journal of Agronomy, 124(2021).

    [22] Y Y FU, G J YANG, Z H LI et al. Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring. Computers and Electronics in Agriculture, 72(2020).

    [23] D A SIMS, H Y LUO, S HASTINGS et al. Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sensing of Environment, 103(2005).

    [24] A GREGORY. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(1994).

    [25] J PENUELAS, F BARET, I FILELLA. Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica, 31, 221-230(1995).

    [26] A HUETE, K DIDAN, T MIURA et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195-213(2002).

    [27] A A GITELSON, T J ARKEBAUER et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(2003).

    [28] G GUYOT, F BARET, D J MAJOR. High spectral resolution: Determination of spectral shifts between the red and the near infrared. ISPRS Journal L of Photogrammetry and Remote Sensing, 11, 740-760(1988).

    [29] G L MAIRE, C FRANCOIS, E DUFRENE. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89, 1-28(2004).

    [30] J W ROUSE, R H HAAS, J A SCHELL et al. Monitoring Vegetation Systems in the Great Plains with Earth Resources Technology Satellite (ERTS), 351(1974).

    [31] B MISTELE, R GUSTER, U SCHMIDHALTER et al. Validation of field-scaled spectral measurements of the nitrogen status in winter wheat(2004).

    [32] A A GITELSON, G P KEYDAN, M N MERZLYAK. Three-band model for noninvasive estimation of chlorophyll,carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33, 431-433(2006).

    [33] G LEMAIRE, E V OOSTEROM, J SHEEHY et al. Is crop N demand more closely related to dry matter accumulation or leaf area expansion during vegetative growth?. Field Crops Research, 100, 91-106(2006).

    [34] A MACCIONI, G AGATI, P MAZZINGHI. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochem Photobiol B, 61, 52-61(2001).

    [35] D BISUN. A new reflectance index for remote sensing of chlorophyll content in higher plants:Tests using Eucalyptus Leaves. Journal of Plant Physiology, 154, 30-36(1999).

    [36] A A GITELSON, Y J KAUFMAN, M N MERZLYAK. Use of a green channel in remote sensing of global vegetation from Earth Observation Satellite - Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS). Remote Sensing of Environment(1996).

    [37] A A GITELSON, Y GRITZ, M N MERZLYAK. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271-282(2003).

    [38] G FITZGERALD, D RODRIGUEZ, G O'LEARY. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-The Canopy Chlorophyll Content Index(CCCI). Field Crops Research(2010).

    [39] S PRIEBE. Application and results of the manchester short assessment of quality of life(Mansa). International Journal of Social Psychiatry, 45(1999).

    [40] J M CHEN. Evaluation of Vegetation Indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(1996).

    [41] R GENEVIEVE, S MICHAEL, B FREDERIC. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95-107(1996).

    [42] J DASH, P J CURRAN. MERIS terrestrial chlorophyll index (MTCI): The meris terrestrial chlorophyll index. International Journal of Remote Sensing, 25, 151-161(2004).

    [43] N H BROGE, E LEBLANCE. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156-172(2001).

    [44] J E VOGELMANN, B N ROCK, D M MOSS. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(1993).

    [45] Shibing YOU, Yan YAN. Stepwise regression analysis and its application. Statistics and decision-making, 31-35(2017).

    [46] K FRELS, M GUTTIERI, B JOYCE et al. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crops Research, 217, 82-92(2018).

    Tools

    Get Citation

    Copy Citation Text

    Xingyu ZHANG, Yue ZHANG, Chenzhen XIA, Xiaoyan ZHANG, Yuxi LI, Xiaoyu LI. Estimation of Leaf Nitrogen Content in Maize based on UAV Hyperspectral Image[J]. Remote Sensing Technology and Application, 2024, 39(4): 927

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jul. 19, 2021

    Accepted: --

    Published Online: Jan. 6, 2025

    The Author Email:

    DOI:10.11873/j.issn.1004-0323.2024.4.0927

    Topics