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Spectral Estimation of Nitrogen Status in Wheat Crops using Remote Sensing


Affiliations
1 Research & Development Center, Bharathiar University, Coimbatore, Tamil Nadu, India
2 Department of Computer Science and Engineering, SDMCET, Dharwad, Karnataka, India
3 Department of Computer Science and Engineering, KLS VDRIT, Haliyal, Karnataka, India
4 School of Computational Science, Solapur University, Solapur, Maharashtra, India
     

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Remote sensing is the method of acquiring and recording of information about an object without the direct contact. Hyper spectral imaging is the technique to collect and process the information in a narrow and continuous spectral band that was obtained over the electromagnetic spectrum. Hyper spectral satellite data of wheat crop was taken for this study from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor with appropriate longitudes and latitudes from earth explorer site. The vegetation indices used for this study are Normalized Difference Vegetation Index (NDVI) and Vegetation Index Green (VIG). The estimation of these indices has been done on the first derivative of the reflectance obtained from the field. The Protein and Nitrogen values were calculated using satellite data and these values were correlated with experimental analysis using Kjeldahl method.

Keywords

AVIRIS, Hyperspectral Satellite Data, Kjeldahl Method, NDVI, VIG.
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  • P. J. Gibson, and C. H. Power, Introductory Remote Sensing: Digital Image Processing and Applications, Routledge, Taylor & Francis Group, 2000.

  • M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Transactions on Geo Science and Remote Sensing, vol. 46, no. 5, pp. 1416-1427, 2008.

  • J. V. Sinfield, D. Fagerman, and O. Colic, “Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils,” Computers and Electronics in Agriculture, vol. 70, no. 1, pp. 1-18, 2010.

  • E. Hoffland, M. Dicke, W. V. Tintelen, H. Dijkman, and M. L. V. Beusichem, “Nitrogen availability and defense of tomato against two-spotted spider mite,” Journal of Chemical, Ecology, vol. 26, no. 12, pp. 2697-2711, 2000.

  • N. Tremblay, E. Fallon, and N. Ziadi, “Sensing of crop nitrogen status: Opportunities, tools, limitations, and supporting information requirements,” HortTechnology, vol. 21, no. 3, pp. 274-281, 2011.

  • J. L. Hatfield, A. A. Gitelson, J. S. Schepers, and C. L. Walthall, “Application of spectral remote sensing for agronomic decisions,” Agronomy Journal, vol. 100, pp. S117-S131, 2008.

  • D. W. Deering, “Rangeland reflectance characteristics measured by aircraft and spacecraft sensors,” Ph.D. Dissertation, Texas, A & M University, College Station, 1978.

  • F. Solari, J. Shanahan, R. B. Ferguson, J. S. Schepers, and A. A. Gitelson, “Active sensor reflectance measurements of corn nitrogen status and yield potential,” Agronomy Journal, vol. 100, no. 3, pp. 571-579, 2008.

  • A. A. Gitelson, Y. J. Kaufman, R. Stark, and D. Rundquist, “Novel algorithms for remote estimation of vegetation fraction,” Remote Sensing of Environment, vol. 80, no. 1, pp. 76-87, 2002.

  • D. A. Roberts, K. L. Roth, and R. L. Perroy, “Hyperspectral vegetation indices chapter 14,” Hyperspectral Remote Sensing of Vegetation, CRC Press, pp. 309-328, 2011.

  • P. M. Hansen, and J. K. Schjoerring, “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, vol. 86, no. 4, pp. 542-553, 2003.

  • B. Mistele, and U. Schmidhalter, “Estimating the nitrogen nutrition index using spectral canopy reflectance measurements,” European Journal of Agronomy, vol. 29, pp. 184-190, 2008.

  • P. Adiver, “Determination of Nitrogen content in plants using spectral images,” M-Tech. Thesis, Visvesvaraya Technological University, India, 2014.

  • M. E. Celebi, H. A. Kingravi, and P. A. Vela, “A comparative study of efficient initialization methods for the k-means clustering algorithm,” Expert Systems with Applications, vol. 40, no. 1, pp. 200-210, 2013.

  • A. K. Singh, H. V. Kumar, G. R. Kadambi, J. K. Kishore, J. Shuttleworth, and J. Manikanda, “Quality metrics evaluation of hyperspectral images,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, no. 8, pp. 1221-1226, ISPRS Technical Commission Hyderabad, India, 2014.

  • A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666, 2010.

  • Q. Du, L. Zhang, B. Zhang, X. Tong, P. Du, and J. Chanussot, “Hyperspectral remote sensing: Theory, methods, and applications,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, pp. 459-471, 2013.

  • I. Dopido, A. Villa, A. Plaza, and P. Gamba, “A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp. 421-435, 2012.

  • M. Parente, and A. Plaza, “Survey of geometric and statistical unmixing algorithms for hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 2085-2090, 2010.

  • R. C. Gonzalez, and R. Steven, Image Processing Using MATLAB, 2nd ed., Prentice Hall, 2007.

  • A. Plaza, G. Martin, J. Plaza, M. Zortea, and S. Sanchez, “Recent developments in endmember extraction and spectral unmixing,” vol. 25, pp. 227-234, Springer, 2010.

  • M. Zortea, and A. Plaza, “A quantitative and comparative analysis of different implementations of N-FINDR: A fast endmember extraction algorithm,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 4, pp. 787-791, 2009.

  • J. M. P. Nascimento, and J. M. B. Dias, “Vertex component analysis: A fast algorithm to unmix hyperspectral data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005.

  • A. A. Gitelson, Y. J. Kaufman, R. Stark, and D. Rundquist, “Novel algorithms for remote estimation of vegetation fraction,” Remote Sensing of Environment, vol. 80, no. 1, pp. 76-87, 2002.

  • I. Polonen, H. Saari, J. Kaivosoja, E. Honkavaara, and L. Pesonen, “Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV,” Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870J, pp. 5-15, 2013.

  • A. Nandibewoor, P. Adiver, and R. Hegadi, “Identification of vegetation from satellite derived hyper spectral indices,” In 2014 International Conference on Contemporary Computing and Informatics (IC3I), IEEE, pp. 1215-1219, 2014.

  • J. J. Henik, “Utilizing NDVI and remote sensing data to identify spatial variability in plant stress as influenced by management,” Iowa State University, Graduate Thesis Dissertation, Paper 12341, 2012.

  • Z. Chunjiang, L. Liangyun, W. Jihua, H. Wenjiang, S. Xiaoyu, L. Cunjun, and W. Zhijie, “Methods and application of remote sensing to forecast wheat grain quality,” 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS, 2004), IEEE, vol. 6, pp. 4008-4010, 2004.

  • K. Arai, O. Shigetomi, M. Sakashita, and Y. Miura, “Estimation of protein content in rice crop and nitrogen content in rice leaves through regression analysis with NDVI derived from camera mounted radio-control helicopter,” International Journal of Advanced Research in Artificial Intelligence, vol. 3, no. 3, pp. 12-19, 2014.

  • R. F. Muñoz-Huerta, R. G. Guevara-Gonzalez, L. M. Contreras-Medina, I. Torres-Pacheco, J. Prado-Olivarez, and R. V. Ocampo-Velazquez, “A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances,” Sensors, vol. 13, no. 8, pp. 10823-10843, 2013.

  • O. Nevalainen, T. Hakala, J. Suomalainen, and S. Kaasalainen, “Nitrogen concentration estimation with hyperspectral LIDAR,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-5/W2, pp. 205-210, 2013.

  • T. Jamer, “Spectroscopy and hyperspectral imagery for monitoring summer barley,” International Journal of Remote Sensing, vol. 34, pp. 6067-6068, 2013.

  • J. G. P. W. Clevers, and A. A. Gitelson, “Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3,” International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 344-351, 2013.

  • T. Jarmer, H. Lilienthal, and T. Udelhoven, “Spectral determination of nitrogen content of wheat canopies,” 3rd EARSeL Workshop on Imaging Spectroscopy, pp. 513-517, 2003.

  • B. J. Jones Jr., Kjeldahl Method for Nitrogen Determination, Micro-Macro Publishing, Athens, 1991.

  • J. U. H. Eitel, D. S. Long, P. E. Gessler, and E. R. Hunt, “Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat,” Agronomy Journal, vol. 100, no. 6, pp. 1694-1702, 2008.


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  • Spectral Estimation of Nitrogen Status in Wheat Crops using Remote Sensing

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Authors

Archana Nandibewoor
Research & Development Center, Bharathiar University, Coimbatore, Tamil Nadu, India
Pavitra Kini
Department of Computer Science and Engineering, SDMCET, Dharwad, Karnataka, India
Akash Konnur
Department of Computer Science and Engineering, KLS VDRIT, Haliyal, Karnataka, India
Ravindra Hegadi
School of Computational Science, Solapur University, Solapur, Maharashtra, India

Abstract


Remote sensing is the method of acquiring and recording of information about an object without the direct contact. Hyper spectral imaging is the technique to collect and process the information in a narrow and continuous spectral band that was obtained over the electromagnetic spectrum. Hyper spectral satellite data of wheat crop was taken for this study from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor with appropriate longitudes and latitudes from earth explorer site. The vegetation indices used for this study are Normalized Difference Vegetation Index (NDVI) and Vegetation Index Green (VIG). The estimation of these indices has been done on the first derivative of the reflectance obtained from the field. The Protein and Nitrogen values were calculated using satellite data and these values were correlated with experimental analysis using Kjeldahl method.

Keywords


AVIRIS, Hyperspectral Satellite Data, Kjeldahl Method, NDVI, VIG.

References