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Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

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Abstract

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R2 (LL R2) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.

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References

  • Alma, O. G., & Bulut, E. (2012). Genetic algorithm based variable selection for partial least squares regression using ICOMP criterion. Asian Journal of Mathematics & Statistics, 5, 82–92.

    Article  Google Scholar 

  • Andersen, C. M., & Bro, R. (2010). Variable selection in regression—a tutorial. Journal of Chemometrics, 24, 728–737.

    Article  CAS  Google Scholar 

  • Bajwa, S.G., & Kulkarni, S.S. (2012). Data mining techniques. In Thenkabail et al. (Eds.), Hyperspectral remote sensing of vegetation. Boca Raton: CRC.

  • Ballesteros, R., Ortega, J. F., Hernández, D., & Moreno, M. A. (2014). Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing. Precision Agriculture, 15, 579–592.

    Article  Google Scholar 

  • Cabrera-Bosquet, L., Molero, G., Stellacci, A. M., Bort, J., Nogués, S., & Araus, J. L. (2011). NDVI as a potential tool for predicting biomass, nitrogen content and growth in potted wheat genotypes. Cereal Research Communications, 39, 147–159.

    Article  Google Scholar 

  • Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). New York: Guilford.

    Google Scholar 

  • Cartelat, A., Cerovic, Y., Goulas, Z. G., Meyer, S., Lelarge, C., Prioul, J.-L., Barbottin, A., Jeuffroy, M.-H., Gate, P., Agati, G., & Moya, I. (2005). Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Research, 91, 35–49.

    Article  Google Scholar 

  • Casa, R., Castaldi, F., Pascucci, S., & Pignatti, S. (2015). Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements. Journal of Agricultural Science, 153, 876–890.

    Article  CAS  Google Scholar 

  • Chong, I. G., & Jun, C. H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78, 102–112.

    Article  Google Scholar 

  • Curran, P. J., Dungan, J. L., & Gholz, H. L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology, 7, 33–48.

    Article  CAS  Google Scholar 

  • Deering, D.W. (1978). Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. dissertation, Texas A & M University, College Station, TX, 338 pp.

  • Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37, 409–428.

    CAS  Google Scholar 

  • Erdle, K., Mistele, B., & Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research, 124, 74–84.

    Article  Google Scholar 

  • Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.

    Article  Google Scholar 

  • Fung, T., Ma, H. F. Y., & Siu, W. L. (2003). Band selection using hyperspectral data of subtropical tree species. Geocarto International, 18, 3–11.

    Article  Google Scholar 

  • Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17.

    Article  CAS  Google Scholar 

  • Guisan, A., Edwards, T. C., Jr., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 89–100.

    Article  Google Scholar 

  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, 542–553.

    Article  Google Scholar 

  • Hatfield, J. L., Gitelson, A. A., Schepers, J. S., & Walthall, C. L. (2008). Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, S-117–S-131.

    Article  CAS  Google Scholar 

  • Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., Lobitz, B. M., Leung, J. G., Gallmeyer, B. A., Aoyagi, M., Slye, R. E., & Brass, J. A. (2004). Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44, 49–61.

    Article  Google Scholar 

  • Hogervorst, M. A., & Schwering, P. B. W. (2011). Hyperspectral data analysis and visualization. In K. Funatsu & K. Hasegawa (Eds.), Knowledge-oriented applications in data mining (pp. 183–205). Croatia: Rijeka. In Tech Publ.

    Google Scholar 

  • Inoue, Y., Sakaiya, E., Zhu, Y., & Takahashi, W. (2012). Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment, 126, 210–221.

    Article  Google Scholar 

  • Jain, N., Ray, S. S., Singh, J. P., & Panigraphy, S. (2007). Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agriculture, 8, 225–239.

    Article  Google Scholar 

  • Min, M., & Lee, W. S. (2005). Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASAE, 48, 455–461.

    Article  CAS  Google Scholar 

  • Pelosi, F., Castaldi, F., & Casa, R. (2015). Operational unmanned aerial vehicle assisted post-emergence herbicide patch spraying in maize: a field study. In J. V. Stafford (Ed.), Precision agriculture 2015 (pp. 159–166). The Netherlands: Wageningen Academic Publishers.

    Google Scholar 

  • Pilbeam, D. J. (2015). Nitrogen. In A. V. Barker, J. David, & D. J. Pilbeam (Eds.), Handbook of plant nutrition, second edition (pp. 17–63). Boca Raton: CRC.

    Google Scholar 

  • Ray, S. S., Singh, J. P., & Panigraphy, S. (2010). Use of hyperspectral remote sensing data for crop stress detection: ground-based studies. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, 38, 562–570.

    Google Scholar 

  • Rinaldi, M., Castrignanò, A., De Benedetto, D., Sollitto, D., Ruggieri, S., Garofalo, P., Santoro, F., Figorito, B., Gualano, S., & Tamborrino, R. (2014). Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data. Envirometrics, 26, 77–88.

    Article  Google Scholar 

  • SAS Institute Inc. (2012). SAS/STAT software, release 9.3. Cary: SAS Institute Inc.

  • Schuenemeyer, J., & Drew, L. (2011). Statistics for earth and environmental scientists. Hoboken: Wiley.

    Google Scholar 

  • Shaver, T. M., Khosla, R., & Westfall, D. G. (2010). Evaluation of two ground-based active crop canopy sensors in maize: growth stage, row spacing, and sensor movement speed. Soil Science Society of America Journal, 74, 2101–2108.

    Article  CAS  Google Scholar 

  • Shi, T., Wang, J., Liu, H., & Wu, G. (2015). Estimating leaf nitrogen concentration in heterogeneous crop plants from hyperspectral reflectance. International Journal of Remote Sensing, 36, 4652–4667.

    Article  Google Scholar 

  • Soil Survey Staff. (2010). Keys to soil taxonomy, 11th edition. Washington: USDA – US Department of Agriculture, Natural Resources Conservation Service.

    Google Scholar 

  • Stellacci, A. M., Castrignanò, A., Diacono, M., Troccoli, A., Ciccarese, A., Armenise, E., Gallo, A., De Vita, P., Lonigro, A., Mastro, M. A., & Rubino, P. (2012). Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response. Italian Journal of Agronomy, 7, 247–253.

    Article  Google Scholar 

  • Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111, 119–129.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering & Remote Sensing, 607–621.

  • Thenkabail, P. S., Enclona, E. A., Ashton, M. S., & Van Der Meer, B. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354–376.

    Article  Google Scholar 

  • Thenkabail, P.S., Lyon, J.G., & Huete, A. (2012). Advances in hyperspectral remote sensing of vegetation agricultural crops. In Thenkabail et al., (Eds.), Hyperspectral remote sensing of vegetation. Boca Raton: CRC.

  • Torres, L. O., & Link, A. (2010). Best management principles and techniques to optimise nutrient use efficiency. York: Proceedings International Fertiliser Society 683.

    Google Scholar 

  • Torres-Sánchez, J., Peña, J. M., de Castro, A. I., & López-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113.

    Article  Google Scholar 

  • Troccoli, A., Colecchia, S. A., Cattivelli, L., & Gallo, A. (2007). Agro-climatic characterization of Foggia town—analysis of the historical series of temperature and precipitation, recorded in Foggia from 1955 to 2006. Foggia: Cannone s.a.s. (in Italian).

    Google Scholar 

  • Wang, F. M., Huang, J. F., & Wang, X. Z. (2008a). Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. Journal of Integrative Plant Biology, 50, 291–299.

    Article  Google Scholar 

  • Wang, F. M., Huang, J. F., Zhou, Q. F., & Wang, X. Z. (2008b). Optimal waveband identification for estimation of leaf area index of paddy rice. Journal of Zhejiang University. Science. B, 9, 953–963.

    Article  Google Scholar 

  • Wang, W., Yao, X., Yao, X. F., Tian, Y. C., Liu, X. J., Ni, J., Cao, W. X., & Zhu, Y. (2012). Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Research, 129, 90–98.

    Article  Google Scholar 

  • Wold, S., Sjöströma, M., & Lennart, E. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130.

    Article  CAS  Google Scholar 

  • Wyszecki, G., & Stiles, W. S. (1982). Color science: concepts and methods, quantitative data and formulae. New York: Wiley.

    Google Scholar 

  • Yao, X., Ren, H., Cao, Z., Tian, Y. C., Cao, W. X., Zhu, Y., & Cheng, T. (2014). Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds. International Journal of Applied Earth Observation and Geoinformation, 32, 114–124.

    Article  Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.

    Article  Google Scholar 

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Acknowledgements

The authors thank the reviewers of this paper and Dr. Federica Lucà for providing constructive comments, which have contributed to the improvement of the published version. We are grateful to Dr. Giacoma Girone for his help in polishing the English of this paper.

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Authors and Affiliations

  1. Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA), Research Unit for Cropping System in Dry Environments, Bari, Italy

    A. M. Stellacci & A. Castrignanò

  2. Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA), Cereal Research Centre, Foggia, Italy

    A. Troccoli

  3. Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, 13 East Lansing, 48823, MI, USA

    B. Basso

  4. National Research Council of Italy, Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM), Rende (CS), Italy

    G. Buttafuoco

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  1. A. M. Stellacci
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  4. B. Basso
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  5. G. Buttafuoco
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Correspondence to G. Buttafuoco.

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Stellacci, A.M., Castrignanò, A., Troccoli, A. et al. Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches. Environ Monit Assess 188, 199 (2016). https://doi.org/10.1007/s10661-016-5171-0

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