Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion

Abstract. Through its association with proteins and plant pigments, leaf nitrogen (N) plays an important regulatory role in photosynthesis, leaf respiration, and net primary production. However, the traditional methods of measurement leaf N are rooted in sample-based spectroscopy in laboratory. There is a big challenge of deriving leaf N from the nondestructive field-measured leaf spectra. In this study, the original PROSPECT model was extended by replacing the absorption coefficient of chlorophyll in the original PROSPECT model with an equivalent N absorption coefficient to develop a nitrogen-based PROSPECT model (N-PROSPECT). N-PROSPECT was evaluated by comparing the model-simulated reflectance values with the measured leaf reflectance values. The validated results show that the correlation coefficient (R) was 0.98 for the wavelengths of 400 to 2500 nm. Finally, N-PROSPECT was used to simulate leaf reflectance using different combinations of input parameters, and partial least squares regression (PLSR) was used to establish the relationship between the N-PROSPECT simulated reflectance and the corresponding leaf nitrogen density (LND). The inverse of the PLSR-based N-PROSPECT model was used to retrieve LND from the measured reflectance with a relatively high accuracy (R2=0.77, RMSE=22.15  μg cm−2). This result demonstrates that the N-PROSPECT model established in this study can accurately simulate nitrogen spectral contributions and retrieve LND.

[1]  H. Gausman,et al.  Interaction of Isotropic Light with a Compact Plant Leaf , 1969 .

[2]  H. Lichtenthaler.  CHLOROPHYLL AND CAROTENOIDS: PIGMENTS OF PHOTOSYNTHETIC BIOMEMBRANES , 1987 .

[3]  John R. Miller,et al.  Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Qing Xiao,et al.  Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

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

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

[9]  J. J. Colls,et al.  Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks , 2004 .

[10]  George Alan Blackburn,et al.  Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves , 1999 .

[11]  Stan D. Wullschleger,et al.  Biochemical Limitations to Carbon Assimilation in C3 Plants—A Retrospective Analysis of the A/Ci Curves from 109 Species , 1993 .

[12]  Reyer Zwiggelaar,et al.  A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops , 1998 .

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

[14]  V. K. Dadhwal,et al.  Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data , 2004 .

[15]  J. Schepers,et al.  Site‐Specific Nitrogen Management of Irrigated Maize , 2002 .

[16]  W. Verhoef,et al.  Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

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

[18]  N. Goel.  Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .

[19]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[20]  C. Bacour,et al.  Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .

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

[22]  K. Kouno,et al.  Nitrate leaching in granitic regosol as affected by N uptake and transpiration by corn , 2007 .

[23]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[24]  Qin Zhang,et al.  Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn , 2012 .

[25]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[26]  Weixing Cao,et al.  Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[27]  J. R. Evans.  Photosynthesis and nitrogen relationships in leaves of C3 plants , 2004, Oecologia.

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

[29]  P. Treitz,et al.  Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada , 2008 .

[30]  N. Broge,et al.  Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture , 2002 .

[31]  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 .

[32]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[33]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[34]  Peng Gong,et al.  Inverting a canopy reflectance model using a neural network , 1999 .

[35]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[36]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[37]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[38]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[39]  N. Broge,et al.  Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data , 2002 .

[40]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.