Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R700/R670) and 57% (R418/R405), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R763/R761), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R370 and R400 as either simple ratio or a normalized difference index performed most consistently in both experimental (R2 = 0.58) and farmers’ fields (R2 = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.

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

[2]  P. Wang,et al.  Synchronizing N Supply from Soil and Fertilizer and N Demand of Winter Wheat by an Improved Nmin Method , 2006, Nutrient Cycling in Agroecosystems.

[3]  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 .

[4]  P. Barraclough,et al.  Effect of water stress on chlorophyll meter readings in winter wheat , 2001 .

[5]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[6]  Jorge A. Delgado,et al.  Increased nitrogen use efficiencies as a key mitigation alternative to reduce nitrate leaching in north china plain , 2007 .

[7]  Josep Peñuelas,et al.  Visible and near-infrared reflectance techniques for diagnosing plant physiological status , 1998 .

[8]  F. Baret,et al.  HIGH SPEcrRAL RESOLUTION : DETERMINATION OF SPEcrRAL SHIFTS BETWEEN THE RED AND INFRARED , 2012 .

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

[10]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[11]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[12]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[13]  François Gastal,et al.  Nitrogen Dilution Curves and Nitrogen Use Efficiency During Winter–Spring Growth of Annual Ryegrass , 2004 .

[14]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[15]  Yuxin Miao,et al.  On-farm evaluation of an in-season nitrogen management strategy based on soil Nmin test , 2008 .

[16]  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.

[17]  Fei Li,et al.  Estimating N status of winter wheat using a handheld spectrometer in the North China Plain , 2008 .

[18]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[19]  Fumin Wang,et al.  Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. , 2007, Environmental science & technology.

[20]  Claus Buschmann,et al.  In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation , 1993 .

[21]  J. Peñuelas,et al.  Remote sensing of biomass and yield of winter wheat under different nitrogen supplies , 2000 .

[22]  J. Schepers,et al.  Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply , 1996 .

[23]  Moon Sung Kim.  THE USE OF NARROW SPECTRAL BANDS FOR IMPROVING REMOTE SENSING ESTIMATIONS OF FRACTIONALLY ABSORBED PHOTOSYNTHETICALLY ACTIVE RADIATION , 1994 .

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

[25]  M. Boschetti,et al.  Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry , 2009 .

[26]  Moon S. Kim,et al.  Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements , 1994 .

[27]  Bisun Datt,et al.  A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves , 1999 .

[28]  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.

[29]  H. J. Heege,et al.  Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany , 2008, Precision Agriculture.

[30]  Pablo J. Zarco-Tejada,et al.  Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops , 2004 .

[31]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

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

[33]  R. G. Smith,et al.  Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite , 1995 .

[34]  B. Ma,et al.  Canopy Light Reflectance and Field Greenness to Assess Nitrogen Fertilization and Yield of Maize , 1996 .

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

[36]  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..

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

[38]  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.

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

[40]  G. Lemaire,et al.  N Uptake and Distribution in Plant Canopies , 1997 .

[41]  B. Datt.  Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves , 1998 .

[42]  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 .

[43]  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 .

[44]  D. Straeten,et al.  Imaging techniques and the early detection of plant stress. , 2000, Trends in plant science.

[45]  A. Ulrich.  Physiological Bases for Assessing the Nutritional Requirements of Plants , 1952 .

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

[47]  C. Rush,et al.  Remote detection of rhizomania in sugar beets. , 2003, Phytopathology.

[48]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

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

[50]  Fusuo Zhang,et al.  Nitrogen balance and groundwater nitrate contamination: comparison among three intensive cropping systems on the North China Plain. , 2006, Environmental pollution.

[51]  Michele Meroni,et al.  Identification of hyperspectral vegetation indices for Mediterranean pasture characterization , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[52]  N. Tremblay,et al.  Critical Nitrogen Curve and Nitrogen Nutrition Index for Corn in Eastern Canada , 2008 .

[53]  S. Elvira,et al.  A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants , 1992 .

[54]  Jason D. Hill,et al.  Environmental costs and benefits of transportation biofuel production from food- and lignocellulose-based energy crops. A review , 2011, Agronomy for Sustainable Development.

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

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

[57]  E. C. Large.  GROWTH STAGES IN CEREALS ILLUSTRATION OF THE FEEKES SCALE , 1954 .

[58]  J. J. Neeteson,et al.  Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment , 1986 .

[59]  K. R. Reddy,et al.  Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. , 2002, Journal of environmental quality.

[60]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

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

[62]  Bodo Mistele,et al.  Validation of field-scaled spectral measurements of the nitrogen status in winter wheat. , 2004 .

[63]  B. Mistele,et al.  Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic , 2008 .

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

[65]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

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

[67]  Nicolas Tremblay,et al.  Chlorophyll Measurements and Nitrogen Nutrition Index for the Evaluation of Corn Nitrogen Status , 2008 .

[68]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[69]  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 .

[70]  C. H. Perrin.  Rapid Modified Procedure for Determination of Kjeldahl Nitrogen , 1953 .

[71]  J. J. Neeteson,et al.  Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment , 1986, Plant and Soil.

[72]  William R. Raun,et al.  In-Season Optical Sensing Improves Nitrogen-Use Efficiency for Winter Wheat , 2009 .

[73]  M. Jeuffroy,et al.  Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status , 2007, Agronomy for Sustainable Development.