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Hyperspectal Remote Sensing for Agriculture: A Review

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Pooja Vinod Janse, Ratnadeep R. Deshmukh
10.5120/ijca2017915185

Pooja Vinod Janse and Ratnadeep R Deshmukh. Hyperspectal Remote Sensing for Agriculture: A Review. International Journal of Computer Applications 172(7):30-34, August 2017. BibTeX

@article{10.5120/ijca2017915185,
	author = {Pooja Vinod Janse and Ratnadeep R. Deshmukh},
	title = {Hyperspectal Remote Sensing for Agriculture: A Review},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {7},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {30-34},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume172/number7/28265-2017915185},
	doi = {10.5120/ijca2017915185},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Hyperspectral remote sensing is used for wide range of application. Hyperspectral data provides more than 200 narrow wavelength bands which provide significant information about all biological and chemical properties of material. Hyperspectral remote sensing is widely used in various applications like agricultural and soil research, mining application, crop management application, drought condition assessment, plant species classification, water body analysis, mineral analysis etc. This paper mainly reviews the concept of hyperspectral remote sensing; processing of hyperspectral data; different vegetation indices defined by researcher; the applications of hyperspectral data for agricultural.

References

  1. Campbell J. B., Introduction to Remote Sensing, Taylor and Francis, London, 1996.
  2. R. N. Sahoo, S. S. Ray and K. R. Manjunath, Hyperspectral remote sensing of agriculture, CURRENT SCIENCE, VOL. 108, NO. 848 5, pp. 848-859, 2015.
  3. Blackburn G. A., Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches, Remote Sensing Environment, 66, 273–285, 1998.
  4. Thenkabail P. S., Smith R. B. and Pauw E. D., Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing Environment, 71:158–182, 2000.
  5. Jensen J. R., Remote Sensing of the Environment: An Erath Resource Perspective, Prentice-Hall, 2000.
  6. Hunt J., Ramond E. and Rock B. N., Detection in changes in leaf water content using near and mid-infrared reflectance, Remote Sensing Environment, 30:45–54, 1989.
  7. Ustin S. L., Roberts D. A., Green R. O., Zomer R. J. and Garcia M., Remote sensing methods monitor natural resources, Photon. Spectra, 33(N10), 108–113, 1999.
  8. Thenkabail P. S., Smith R. B. and Pauw E. D., Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogramm. Eng. Remote Sensing, 68:607–627, 2002.
  9. Filippi A. M. and Jensen J. R., Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing Environment, 100:512–530, 2006.
  10. Galford G. L., Mustard J. F., Melillo J., Gendrin A. Cerri C. C. and Cerri E. P. C., Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil, Remote Sensing Environment, 112:576–587, 2008.
  11. Richards J. A., Remote Sensing Digital Image Analysis: An Introduction, Springer-Verlag, Berlin, 1993.
  12. Thenkabail P. S., Enclona E. A., Ashton M. S. and Van Der Meer B., Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing Environment, 91(3-4): 354–376, 2004.
  13. Cochrane M. A., Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing, 21(10):2075–2087, 2000.
  14. Schmidt K. S. and Skidmore A. K., Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing Environment, 85(1):92–108, 2003.
  15. Vaiphasa C., Ongsomwangc S., Vaiphasa T. and Skidmore A. K., Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuarine, Coastal Shelf Sci., 65:371–379, 2005.
  16. Manjunath K. R., Ray S. S. and Panigrahy S., Discrimination of spectrally-close crops using ground-based hyperspectral data. Journal of Indian Society Remote Sensing, 39(4):599–602, 2011.
  17. Sahoo R. N., Biswas A., Singh G. P., Singh R., Gupta V. K., Krishna G. and Pargal S., Discrimination of wheat genotypes through remote sensing. In Annual Report of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, page no. 70, 2013.
  18. Kumar A., Manjunath K. R., Mehra M. R., Sud R. K., Singh R. D. and Panigrahy S., Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices. International Journal of Applied Earth Observation and Geoinformation, Volume 23, Pages 352–359, August 2013.
  19. Miglani A., Ray S. S., Pandey R. and Parihar J. S., Evaluation of EO-1 Hyperion data for agricultural applications. Journal Indian Soc. Remote Sensing, 36: 255–266, 2008.
  20. Huete A. R., Liu Batchily K., Van Leeuwen, A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sensing of Environment, 59:440-451, 1997.
  21. Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Monitoring vegetation systems in the great plains with ERTS, Third ERTS symposium, NASA SP-351, NASA Washington, DC, Vol. 1, pp.309-317, 1973.
  22. Jorden C.F., Leaf area index from quality of light on the forest floor, Ecology, 50(4):663-666, 1969.
  23. Gao B., NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58:257-266, 1996.
  24. Penuelas J., Pinol J., Ogaya R., and Lilella I., Estimation of plant water content by the reflectance water index WI (R900/ R970), International journal of remote sensing, 18:2869-2875, 1997.
  25. Kaufman Y. J., Tanier D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS, IEEE Transaction on Geoscience and Remote Sensing, 30(2): 261-270, 1992.
  26. Huete A.R., A soil adjusted vegetation index (SAVI), Remote Sensing of Environment, 71:158-182, 2000.
  27. Gitelson A.A., Kaufman Y. J., Stark R., and Rundquist D., Novel algorithm for remote estimation of vegetation fraction, Remote Sensing of Environment, 80:76-87, 2002.
  28. Penuelas J., Baret F., and Filella I., Semi empirical indices to assess carotenoids/ chlorophyll a ratio from leaf spectral reflectance, Photosynthetica, 31:221-230, 1995.
  29. Blackburn G. A., Spectral indices for estimating photosynthetic pigment concentration: A test using senescent tree leaves, International journal of remote sensing, 19:657-675, 1998.
  30. Blackburn G. A., Quantifying chlorophyll and carotenoids from leaf to canopy scale: An evaluation of some hyperspectral approaches, Remote Sensing of Environment, 66:273-285,1998.
  31. Merzlyak M. N., Gitelson A. A., Chivkunova O. B., and Ratikin Y., Non-destructive optical detection of pigment changes during leaf senescent and fruit ripening, Physiologia Plantarum, 105:135-141, 1999.
  32. Kim M. S., The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR), Master Thesis, Department of Geography, University of Maryland, College Park, 1994.
  33. Daughtry C. S. T., Walthall C. L., Kim M. S., de Colstoun E. B. and McMurtrey J. E., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance, Remote Sensing of Environment, 74:229-239, 2000.
  34. Gitelson A. A., Keydan G. P., and Merzlyak M. N., Three band model for noninvasive estimation of chlorophyll, carotenoids and anthocyanin contents in higher plant leaves, Geophysical Research Letters, 33:L11402, 2006.
  35. Gitelson A. A., Merzlyak M. N., and Chivkunova O. B., Optical properties and non-destructive estimation of anthocyanin content in plant leaves, Photochemistry and Photobiology, 74(1):38-45, 2001.
  36. Gaman J. A., Surfus J. S., Assessing leaf pigment content and activity with a reflectometer, New Phytologist, 143: 105-117, 1999.
  37. Van Den Berg A. K., and Perkins T. D., Non-destructive estimation of anthocyanin content in autumn auger maple leaves, Horticultural Science, 40(3):685-685, 2005.
  38. Gitelson A. A., Zur Y., Chivkunova O. B., Merzlyak M. N., Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochemistry and Photobiology, 75(3): 272-281, 2002.
  39. Hunt A. R., Rock B. N., Detection of changes in leaf water content using near- and middle-infrared reflectance, Remote Sensing of Environment, 30:43-54, 1989.
  40. Rock B. N., Vogelmann J. E., Williams D. L., Vogelmann A. F., Hoshizaki T., Detection of forest damage, BioScience, 36(7): 439-445, 1986.
  41. Gamon J. A., Serrano L., Surfus J. S., The photochemical reflectance index: An optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient level, Oecologia, 112:492-501, 1997.
  42. Horler D. N. H., Dockray M., Barber J., The red-edge of plant leaf reflectance, International journal of remote sensing, 4:273-288, 1983.

Keywords

Hyperspectral data, Remote sensing, Spectral reflectance, Agriculture, Crop classification, Vegetation Index.