Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data

Abstract Remote sensing is proving to be a rapid non-destructive method for crop nitrogen (N) status assessment. In this study, quantitative relationships between leaf N concentration (LNC) and ground-based canopy hyperspectral reflectance in winter wheat (Triticum aestivum L.) were investigated. Winter wheat field experiments were conducted over three years at different sites (Zhengzhou, Jiaozuo and Kaifeng) in Henan, China. Different N rates and wheat cultivars were tested, and a novel double-peak area index was developed to improve the prediction accuracy and stability of LNC measurement. The common optimal red-edge spectral indices were used to monitor the LNC models. Analysis of the relationship between existing vegetable indices and LNC indicated that red-edge spectral parameters were the most sensitive in this case. Integrated linear regression of LNC with mND705 and REPle was performed to describe the dynamic nature of the LNC patterns, giving coefficients (R2) of 0.83 and 0.82, and the standard errors (SE) of 0.414 and 0.424, respectively. These novel double-peak area parameters were constructed based on analysis of the red-edge characteristics, and the optimal normalized difference of the double-peak areas based on REPig division (NDDAig), in the form of (R755 + R680 − 2 × RREPig)/(R755 − R680), were calculated and found to be highly correlated with LNC (highest R2 = 0.85; lowest SE = 0.385). When independent data were fit into the derived equations, the average relative error (RE) values were 14.1%, 13.7% and 11.5% between measured and estimated LNC using mND705, REPle and NDDAig, respectively, indicating a superior fit and better performance for NDDAig. These results suggest that the models can accurately estimate LNC in wheat, and the novel double-peak area index is more effective for modeling LNC than previously reported red-edge indices.

[1]  F. Baret,et al.  Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. , 2006, Journal of experimental botany.

[2]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[3]  Weixing Cao,et al.  Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat , 2012 .

[4]  Liangyun Liu,et al.  Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR) , 2004 .

[5]  N. Oppelt,et al.  Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data , 2004 .

[6]  J. Clevers,et al.  Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data , 2004 .

[7]  Luis Alonso,et al.  Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC) , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[8]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[9]  Xin-ping Chen,et al.  Reducing environmental risk by improving N management in intensive Chinese agricultural systems , 2009, Proceedings of the National Academy of Sciences.

[10]  Yoshio Inoue,et al.  Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements , 2012 .

[11]  F. T. Turner,et al.  Assessing the nitrogen requirements of rice crops with a chlorophyll meter , 1994 .

[12]  MA Wen-qi,et al.  The Relationship between Fertilizer Input Level and Nutrient Use Efficiency , 2000 .

[13]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[14]  Xin-shi Zhang,et al.  Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method , 2011 .

[15]  P. Gong,et al.  Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia , 2002 .

[16]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[17]  M. D. Steven,et al.  Spectral responses of pot-grown plants to displacement of soil oxygen , 2004 .

[18]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[19]  S. Dobrowski,et al.  Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects , 2003 .

[20]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[21]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[22]  Pierre Martre,et al.  Nitrogen partitioning and remobilization in relation to leaf senescence, grain yield and grain nitrogen concentration in wheat cultivars ☆ , 2014 .

[23]  Linzhang Yang,et al.  Recommendations for nitrogen fertiliser topdressing rates in rice using canopy reflectance spectra , 2008 .

[24]  Fei Li,et al.  Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Germany , 2013 .

[25]  G. Carter,et al.  Spectral reflectance characteristics and digital imagery of a pine needle blight in the southeastern United States , 1996 .

[26]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[27]  William R. Raun,et al.  Improving Nitrogen Use Efficiency for Cereal Production , 1999 .

[28]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[29]  Xia Yao,et al.  Monitoring leaf nitrogen status with hyperspectral reflectance in wheat , 2008 .

[30]  Frits K. van Evert,et al.  Using crop reflectance to determine sidedress N rate in potato saves N and maintains yield , 2012 .

[31]  D. Lamb,et al.  Estimating leaf nitrogen concentration in ryegrass ( Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations , 2002 .

[32]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[33]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[34]  R. Jongschaap,et al.  Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status , 2004 .

[35]  D. Haboudane,et al.  New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat , 2010 .

[36]  TANGYan-Lin,et al.  Relations Between Red Edge Characteristics and Agronomic Parameters of Crops , 2004 .

[37]  J. Porter,et al.  A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand , 1991 .

[38]  Pierre Roumet,et al.  Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer , 2013 .

[39]  John R. Miller,et al.  Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model , 1990 .

[40]  J. Melack,et al.  Remote sensing of foliar chemistry of inundated rice with imaging spectrometry , 1996 .

[41]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[42]  F. Boochs,et al.  Shape of the red edge as vitality indicator for plants , 1990 .

[43]  Weixing Cao,et al.  Monitoring leaf nitrogen in wheat using canopy reflectance spectra , 2006 .

[44]  Bodo Mistele,et al.  Tractor‐Based Quadrilateral Spectral Reflectance Measurements to Detect Biomass and Total Aerial Nitrogen in Winter Wheat , 2010 .

[45]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[46]  G. F. Sassenrath-Cole,et al.  Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration , 2000 .

[47]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[48]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[49]  P. Haschberger,et al.  Laser-Induced Chlorophyll Fluorescence Measurements for Detecting the Nitrogen Status of Wheat (Triticum aestivum L.) Canopies , 2005, Precision Agriculture.

[50]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[51]  T. S. Prasad,et al.  Comparative analysis of red-edge hyperspectral indices , 2003 .

[52]  B. Turner,et al.  Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis , 2004 .

[53]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

[54]  J. Schepers,et al.  Nitrogen Deficiency Detection Using Reflected Shortwave Radiation from Irrigated Corn Canopies , 1996 .