Performance of reflectance indices and of a handheld device for estimating in‐field the nitrogen status of grapevine leaves

Background and Aims Optical leaf sensing of plant nitrogen (N) status enables fertilisation based on plant demand. The aim of this study was to assess the suitability of leaf reflectance indices and of a handheld device for the estimation of N and chlorophyll (Chl) in Vitis vinifera L. cv. Riesling. Methods and Results Riesling leaves from a long‐term fertilisation trial were analysed for nitrogen and pigments and their spectral reflectance in the Vis/near‐infrared range was recorded for two consecutive seasons. In the second season the leaves were also measured with a Chl fluorescence based portable device (Dualex). Published reflectance indices and fluorescence indices were regressed against the concentration of pigment and N. Temporal changes in the Chl/N ratio complicated the indirect assessment of N via Chl. Red edge inflection points and several three‐ to four‐waveband indices allowed for a good prediction of leaf N. Fluorescence indices allowed for reliable estimation of N and Chl. Conclusions While leaf pigments can be analysed by simple two‐band indices, accurate N estimation requires more complex indices such as three‐band indices or indices calculated from derivative spectra. Significance of the Study Data from this study will assist growers to monitor non‐destructively the spatial and temporal variability of grapevine N status and thereby, improve fertilisation.

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

[2]  Jb Robinson,et al.  Plant Analysis: An Interpretation Manual , 1997 .

[3]  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 .

[4]  Nicolas Tremblay,et al.  The Dualex - a new tool to determine nitrogen sufficiency in broccoli. , 2009 .

[5]  Li-wen Wang,et al.  Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands , 2016 .

[6]  Prateek Gupta,et al.  A rapid and sensitive method for determination of carotenoids in plant tissues by high performance liquid chromatography , 2015, Plant Methods.

[7]  Z. Cerovic,et al.  Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.) , 2005 .

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

[9]  Weimin Ju,et al.  Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation , 2017, Remote. Sens..

[10]  A. Strever,et al.  The development of a method for the extraction of carotenoids and chlorophylls from grapevine leaves and berries for HPLC profiling , 2010 .

[11]  M. Cho,et al.  An investigation into robust spectral indices for leaf chlorophyll estimation , 2011 .

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

[13]  Yves Goulas,et al.  Dualex: a new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence. , 2004, Applied optics.

[14]  Sébastien Debuisson,et al.  Nondestructive diagnostic test for nitrogen nutrition of grapevine (Vitis vinifera L.) based on dualex leaf-clip measurements in the field. , 2015, Journal of agricultural and food chemistry.

[15]  Damien Arvor,et al.  Remote Sensing and Cropping Practices: A Review , 2018, Remote. Sens..

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

[17]  Craig S. T. Daughtry,et al.  A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[18]  O. Löhnertz,et al.  Must Composition and Nitrogen Uptake in a Long-term Trial as Affected by Timing of Nitrogen Fertilization in a Cool-Climate Riesling Vineyard , 2008 .

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

[20]  B. Zebarth,et al.  Evaluation of leaf chlorophyll index for making fertilizer nitrogen recommendations for silage corn in a high fertility environment , 2002 .

[21]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[22]  S. Chaillou,et al.  Partitioning and mobilization of starch and N reserves in grapevine (Vitis vinifera L.). , 2004, Journal of plant physiology.

[23]  B. Wermelinger,et al.  Seasonal growth and nitrogen distribution in grapevine leaves, shoots and grapes , 2015 .

[24]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[25]  Luis Miguel Contreras-Medina,et al.  A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.

[26]  G. Brunetto,et al.  Nitrogen fertilization of Cabernet Sauvignon grapevines: yield, total nitrogen content in the leaves and must composition , 2015 .

[27]  O. Löhnertz,et al.  Investigation of grapevine root distribution by in situ minirhizotron observation , 2015 .

[28]  J. Gaudillère,et al.  Leaf and must nitrogen content two complementary indicators of grapevine nitrogen status , 2003 .

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

[30]  A. Ramoelo,et al.  Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations , 2011 .

[31]  X. Yao,et al.  Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance , 2011 .

[32]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[33]  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.

[34]  Weixing Cao,et al.  Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis , 2018, Plant Methods.

[35]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[36]  Xu Chu,et al.  Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice , 2014, Plant and Soil.

[37]  B. G. Coombe,et al.  Growth Stages of the Grapevine: Adoption of a system for identifying grapevine growth stages , 1995 .

[38]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[39]  Paul A. Henschke,et al.  Implications of nitrogen nutrition for grapes, fermentation and wine , 2005 .

[40]  Gwendal Latouche,et al.  A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids , 2012, Physiologia plantarum.

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

[42]  W. Collins,et al.  Remote sensing of crop type and maturity , 1978 .

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

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

[45]  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..

[46]  A. Skidmore,et al.  Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species , 2005 .

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

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

[49]  J. L. Gabriel,et al.  Airborne and ground level sensors for monitoring nitrogen status in a maize crop , 2017 .

[50]  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 .

[51]  Francisco M. Padilla,et al.  Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon , 2014 .

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

[53]  Yafit Cohen,et al.  SWIR-based spectral indices for assessing nitrogen content in potato fields , 2010 .

[54]  P. Williams,et al.  Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds , 1993 .

[55]  S. K. Balasundram,et al.  A Review: The Role of Remote Sensing in Precision Agriculture , 2010 .

[56]  Gail Taylor,et al.  Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution , 2010 .

[57]  S. Poni,et al.  Effects of Variable Rate Nitrogen Application on cv. Barbera Performance: Vegetative Growth and Leaf Nutritional Status , 2018, American Journal of Enology and Viticulture.