Comparison of different methods for estimating nitrogen concentration in flue-cured tobacco leaves based on hyperspectral reflectance

Abstract Leaf nitrogen content (LNC) is an important indicator of tobacco quality and is used in the prediction of tobacco yield. Reflectance experiments for flue-cured tobacco were conducted over 2 consecutive years. Leaf hyperspectral reflectance and nitrogen content data were collected at 15-day intervals from 30 days after transplant until harvest. In this work, we identified the central band that sensitive to tobacco LNC and the optimum combination to establish new spectral indices (SR and NDVI), which were used in linear models of the specific ratio vegetation index (SR), normalized difference vegetation index (NDVI), stepwise multiple linear regression (SMLR), and back-propagation (BP) neural network models as independent variable or input factors. The central bands for the LNC were concentrated in the visible range (450–750 nm) in combination with the shortwave infrared range (1450–2500 nm) range. The optimum band combinations for SR and NDVI were (590 and 1980 nm) and (1970 and 650 nm), respectively. The BP neural network model was the most stable and accurate model (R2 = 0.91, RMSE = 0.09, and K ¯ = 0.00 ). The SR, NDVI, and SMLR models had R2 values of 0.77, 0.76, and 0.86; RMSE values of 0.26, 0.51, and 0.60, and K ¯ values of 0.05, 0.11, and 0.14, respectively. The results indicate the possibility of monitoring LNC by combining remote sensing with predictive models.

[1]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[2]  W. E. Larson,et al.  Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. , 2000 .

[3]  L. Romero,et al.  Nitrogen Metabolism in Five Plant Species Characteristic of Gypsiferous Soils , 2000 .

[4]  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 .

[5]  Scott C. Chapman,et al.  Using a Chlorophyll Meter to Estimate Specific Leaf Nitrogen of Tropical Maize during Vegetative Growth , 1997 .

[6]  Pan Wen.  Monitoring Soil Nitrogen and Plant Nitrogen Based on Hyperspectral of Cotton Canopy , 2010 .

[7]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[8]  X. Ju,et al.  Yield and nicotine content of flue-cured tobacco as affected by soil nitrogen mineralization , 2008 .

[9]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[10]  G. Fitzgerald,et al.  Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments , 2006, Precision Agriculture.

[11]  F. Stuart Chapin,et al.  Seasonal Changes in Nitrogen and Phosphorus Fractions and Autumn Retranslocation in Evergreen and Deciduous Taiga Trees , 1983 .

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

[13]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Wang Fangyong,et al.  Monitoring of soil nitrogen and plant nitrogen based on hyperspectral of cotton canopy. , 2010 .

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

[16]  Yuxin Miao,et al.  Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn , 2008, Precision Agriculture.

[17]  Zailin Huo,et al.  Simulation for response of crop yield to soil moisture and salinity with artificial neural network , 2011 .

[18]  M. Jeuffroy,et al.  Diagnosis tool for plant and crop N status in vegetative stage Theory and practices for crop N management , 2008 .

[19]  M. Cochrane.  Using vegetation reflectance variability for species level classification of hyperspectral data , 2000 .

[20]  Gilles Rabatel,et al.  Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat , 2011 .

[21]  Xiuliang Jin,et al.  Comparison of two methods for estimation of leaf total chlorophyll content using remote sensing in wheat , 2012 .

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

[23]  Gilles Lemaire,et al.  Dynamics of Accumulation and Partitioning of N in Leaves, Stems and Roots of Lucerne (Medicago sativa L.) in a Dense Canopy , 1992 .

[24]  M. Agnusdei,et al.  Leaf nitrogen concentration and chlorophyll meter readings as predictors of tall fescue nitrogen nutrition status , 2012 .

[25]  G. Agati,et al.  New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. , 2001, Journal of photochemistry and photobiology. B, Biology.

[26]  赵文吉 Zhao Wenji,et al.  Estimating total nitrogen content in water body based on reflectance from wetland vegetation , 2012 .

[27]  T. Yoneyama,et al.  Spectral reflectance ratio of rice canopy for estimating crop nitrogen status , 1990, Plant and Soil.

[28]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[29]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[30]  Bruno Mary,et al.  Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations , 2007 .

[31]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[32]  G. Fitzgerald,et al.  Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI) , 2010 .

[33]  Weixing Cao,et al.  Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance , 2004, Agronomy Journal.

[34]  J. R. Evans,et al.  Nitrogen and Photosynthesis in the Flag Leaf of Wheat (Triticum aestivum L.). , 1983, Plant physiology.

[35]  H. Gausman,et al.  Reflectance of leaf components , 1977 .

[36]  M. Umeda,et al.  Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing , 2011 .

[37]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[38]  W. Cox,et al.  Growth, yield and quality of forage maize under different nitrogen management practices , 1993 .

[39]  Albrecht E. Melchinger,et al.  Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes , 2012 .

[40]  Raymond F. Kokaly,et al.  Investigating a Physical Basis for Spectroscopic Estimates of Leaf Nitrogen Concentration , 2001 .

[41]  Gary E. Varvel,et al.  Light Reflectance Compared with Other Nitrogen Stress Measurements in Corn Leaves , 1994 .

[42]  M. Duru.  EVALUATION OF CHLOROPHYLL METER TO ASSESS NITROGEN STATUS OF COCKSFOOT SWARD , 2002 .

[43]  Gilles Lemaire,et al.  Relation entre dynamique de croissance et dynamique de prélèvement d'azote pour un peuplement de graminées fourragères. I. — Etude de l'effet du milieu , 1984 .

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

[45]  A. Navabi,et al.  Can Leaf Chlorophyll Measures at Differing Growth Stages be used as an Indicator of Winter Wheat and Spring Barley Nitrogen Requirements in Eastern Canada , 2005 .

[46]  B. Mistele,et al.  Estimating the nitrogen nutrition index using spectral canopy reflectance measurements , 2008 .

[47]  Gilles Lemaire,et al.  Growth Rate and % N of Field Grown Crops: Theory and Experiments , 1991 .