Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2017, Article ID 1353691, 17 pages
https://doi.org/10.1155/2017/1353691
Review Article

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China

Correspondence should be addressed to Baofeng Su; nc.ude.fauswn@sfb

Received 24 January 2017; Accepted 13 April 2017; Published 23 May 2017

Academic Editor: Chenzong Li

Copyright © 2017 Jinru Xue and Baofeng Su. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. J. Mulla, “Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps,” Biosystems Engineering, vol. 114, no. 4, pp. 358–371, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. W. J. Foley, A. McIlwee, I. Lawler, L. Aragones, A. P. Woolnough, and N. Berding, “Ecological applications of near infrared reflectance spectroscopy - A tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance,” Oecologia, vol. 116, no. 3, pp. 293–305, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Zhang and J. M. Kovacs, “The application of small unmanned aerial systems for precision agriculture: a review,” Precision Agriculture, vol. 13, no. 6, pp. 693–712, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Ballesteros, J. F. Ortega, D. Hernández, and M. Moreno, “Characterization of vitis vinifera l. Canopy using unmanned aerial vehicle-based remote sensing and photogrammetry techniques,” American Journal of Enology and Viticulture, vol. 66, no. 2, pp. 120–129, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Gago, C. Douthe, R. E. Coopman et al., “UAVs challenge to assess water stress for sustainable agriculture,” Agricultural Water Management, vol. 153, pp. 9–19, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Hoffmann, H. Nieto, R. Jensen, R. Guzinski, P. J. Zarco-Tejada, and T. Friborg, “Estimating evapotranspiration with thermal UAV data and two source energy balance models,” Hydrology and Earth System Sciences Discussions, vol. 12, no. 8, pp. 7469–7502, 2015. View at Publisher · View at Google Scholar
  7. E. Honkavaara, H. Saari, J. Kaivosoja et al., “Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture,” Remote Sensing, vol. 5, no. 10, pp. 5006–5039, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Xia, W. P. Kustas, M. C. Anderson et al., “Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes,” Hydrology and Earth System Sciences, vol. 20, no. 4, pp. 1523–1545, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Ortega-Farías, S. Ortega-Salazar, T. Poblete et al., “Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV),” Remote Sensing, vol. 8, no. 8, article no. 638, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Chang, S. Peng-Sen, and Liu Shi-Rong, “A review of plant spectral reflectance response to water physiological changes,” Chinese Journal of Plant Ecology, vol. 40, no. 1, pp. 80–91, 2016. View at Publisher · View at Google Scholar
  11. H. R. Bin Abdul Rahim, M. Q. Bin Lokman, S. W. Harun et al., “Applied light-side coupling with optimized spiral-patterned zinc oxide nanorod coatings for multiple optical channel alcohol vapor sensing,” Journal of Nanophotonics, vol. 10, no. 3, Article ID 036009, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. B. A. Cruden, D. Prabhu, and R. Martinez, “Absolute radiation measurement in venus and mars entry conditions,” Journal of Spacecraft and Rockets, vol. 49, no. 6, pp. 1069–1079, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Hatfield, J. Baker, and T. J. Arkebauer, “Leaf radiative properties and the leaf energy budget,” in Micrometeorology in Agricultural Systems, Agronomy Monograph, American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, Wis, USA, 2005. View at Publisher · View at Google Scholar
  14. G. D. Batten, “Plant analysis using near infrared reflectance spectroscopy: The potential and the limitations,” Australian Journal of Experimental Agriculture, vol. 38, no. 7, pp. 697–706, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. D. A. Burns and E. W. Ciurczak, Handbook of Near-Infrared Analysis, CRC Press, 2007.
  16. R. Karwa, “Laws of thermal radiation,” in Heat and Mass Transfer, pp. 665–696, Springer, 2017. View at Google Scholar
  17. A. Prashar and H. G. Jones, “Assessing drought responses using thermal infrared imaging,” Methods in Molecular Biology, vol. 1398, pp. 209–219, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Martynenko, K. Shotton, T. Astatkie et al., “Thermal imaging of soybean response to drought stress: the effect of ascophyllum nodosum seaweed extract,” SpringerPlus, vol. 5, no. 1, no. 1393, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Fuentes, R. de Bei, J. Pech, and S. Tyerman, “Computational water stress indices obtained from thermal image analysis of grapevine canopies,” Irrigation Science, vol. 30, no. 6, pp. 523–536, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. A.-K. Mahlein, E.-C. Oerke, U. Steiner, and H.-W. Dehne, “Recent advances in sensing plant diseases for precision crop protection,” European Journal of Plant Pathology, vol. 133, no. 1, pp. 197–209, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. E.-C. Oerke, A.-K. Mahlein, and U. Steiner, “Proximal sensing of plant diseases,” in Detection And Diagnostics of Plant Pathogens, pp. 55–68, Springer, 2014. View at Google Scholar
  22. A. Karnieli, N. Agam, R. T. Pinker et al., “Use of NDVI and land surface temperature for drought assessment: merits and limitations,” Journal of Climate, vol. 23, no. 3, pp. 618–633, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. R. P. Sripada, R. W. Heiniger, J. G. White, and R. Weisz, “Aerial color infrared photography for determining late-season nitrogen requirements in corn,” Agronomy Journal, vol. 97, no. 5, pp. 1443–1451, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Zhang, D. Wu, L. Zhang, Q. Jiao, and Q. Li, “Application of hyperspectral remote sensing for environment monitoring in mining areas,” Environmental Earth Sciences, vol. 65, no. 3, pp. 649–658, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. A. N. Ganeshamurthy, V. Ravindra, R. Venugopalan, M. Mathiazhagan, and R. M. Bhat, “Biomass distribution and development of allometric equations for non-destructive estimation of carbon sequestration in grafted mango trees,” Journal of Agricultural Science, vol. 8, no. 8, 201 pages, 2016. View at Publisher · View at Google Scholar
  26. S. Fuentes, A. R. Palmer, D. Taylor, M. Zeppel, R. Whitley, and D. Eamus, “An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI,” Functional Plant Biology, vol. 35, no. 10, pp. 1070–1079, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Fuentes, C. Poblete-Echeverría, S. Ortega-Farias, S. Tyerman, and R. de Bei, “Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods,” Australian Journal of Grape and Wine Research, vol. 20, no. 3, pp. 465–473, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Poblete-Echeverría, S. Fuentes, S. Ortega-Farias, J. Gonzalez-Talice, and J. A. Yuri, “Digital cover photography for estimating Leaf area index (LAI) in apple trees using a variable light extinction coefficient,” Sensors (Switzerland), vol. 15, no. 2, pp. 2860–2872, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Carrasco-Benavides, S. Ortega-Farías, L. O. Lagos et al., “Crop coefficients and actual evapotranspiration of a drip-irrigated Merlot vineyard using multispectral satellite images,” Irrigation Science, vol. 30, no. 6, pp. 485–497, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Mora, F. Avila, M. Carrasco-Benavides, G. Maldonado, J. Olguín-Cáceres, and S. Fuentes, “Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies,” Computers and Electronics in Agriculture, vol. 123, pp. 195–202, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. R. De Bei, S. Fuentes, M. Gilliham et al., “Viticanopy: A free computer app to estimate canopy vigor and porosity for grapevine,” Sensors (Switzerland), vol. 16, no. 4, article no. 585, 2016. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Fuentes, R. De Bei, C. Pozo, and S. Tyerman, “Development of a smartphone application to characterise temporal and spatial canopy architecture and leaf area index for grapevines,” Wine & Viticulture Journal, vol. 20, no. 6, pp. 56–60, 2012. View at Publisher · View at Google Scholar
  33. C. Francone, V. Pagani, M. Foi, G. Cappelli, and R. Confalonieri, “Comparison of leaf area index estimates by ceptometer and pocketlai smart app in canopies with different structures,” Field Crops Research, vol. 155, pp. 38–41, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Orlando, E. Movedi, L. Paleari et al., “Estimating leaf area index in tree species using the PocketLAI smart app,” Applied Vegetation Science, vol. 18, no. 4, pp. 716–723, 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Campos-Taberner, F. J. García-Haro, R. Confalonieri et al., “Multitemporal monitoring of plant area index in the valencia rice district with PocketLAI,” Remote Sensing, vol. 8, no. 3, article no. 202, 2016. View at Publisher · View at Google Scholar · View at Scopus
  36. C. F. Jordan, “Derivation of leaf-area index from quality of light on the forest floor,” Ecology, vol. 50, no. 4, pp. 663–666, 1969. View at Publisher · View at Google Scholar
  37. Z. Quan, Z. Xianfeng, and J. Miao, “Eco-environment variable estimation from remote sensed data and eco-environment assessment: models and system,” Acta Botanica Sinica, vol. 47, pp. 1073–1080, 2011. View at Google Scholar
  38. A. J. Richardson and C. Weigand, “Distinguishing vegetation from soil background information,” Photogrammetric Engineering and Remote Sensing, p. 43, 1977. View at Google Scholar
  39. X. D. L. Wenlong, “Vegetation index controlling the influence of soil reflection,” 2009, http://www.paper.edu.cn/releasepaper/content/200906-376.
  40. Y. J. Kaufman and D. Tanré, “Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS,” IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 261–270, 1992. View at Publisher · View at Google Scholar · View at Scopus
  41. D. J. Major, F. Baret, and G. Guyot, “A ratio vegetation index adjusted for soil brightness,” International Journal of Remote Sensing, vol. 11, no. 5, pp. 727–740, 1990. View at Publisher · View at Google Scholar · View at Scopus
  42. J. W. Rouse Jr., R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the great plains with erts,” NASA Special Publication 351, 309, 1974. View at Google Scholar
  43. J. A. Gamon, C. B. Field, M. L. Goulden et al., “Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types,” Ecological Applications, vol. 5, no. 1, pp. 28–41, 1995. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Global Change Biology, vol. 13, no. 7, pp. 1484–1497, 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Tanré, C. Deroo, P. Duhaut et al., “Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code,” International Journal of Remote Sensing, vol. 11, no. 4, pp. 659–668, 1990. View at Publisher · View at Google Scholar · View at Scopus
  46. R.-H. Zhang, N. X. Rao, and K. N. Liao, “Approach for a vegetation index resistant to atmospheric effect,” Acta Botanica Sinica, vol. 38, no. 1, pp. 53–62, 1996. View at Google Scholar · View at Scopus
  47. A. J. Richardson and C. Wiegand, “Distinguishing vegetation from soil background information,” Photogrammetric Engineering & Remote Sensing, vol. 43, no. 12, pp. 1541–1552, 1977. View at Google Scholar
  48. F. Baret, S. Jacquemoud, and J. F. Hanocq, “The soil line concept in remote sensing,” Remote Sensing Reviews, vol. 7, no. 1, pp. 65–82, 1993. View at Publisher · View at Google Scholar · View at Scopus
  49. A. Bannari, D. Morin, and D. He, “High spatial and spectral resolution remote sensing for the management of the urban environment,” in Proceedings of the 1st International Airborne Remote Sensing Conference and Exhibition, pp. 12–15, Strasbourg, France, 1994.
  50. A. R. Huete, “A soil-adjusted vegetation index (SAVI),” Remote Sensing of Environment, vol. 25, no. 3, pp. 295–309, 1988. View at Publisher · View at Google Scholar · View at Scopus
  51. J. Qi, A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian, “A modified soil adjusted vegetation index,” Remote Sensing of Environment, vol. 48, no. 2, pp. 119–126, 1994. View at Publisher · View at Google Scholar · View at Scopus
  52. X. Dandan and L. Wenlong, Vegetation Index Controlling The Influence of Soil Reflection, Department of Pastrol Agricultural Science and Technology, 2009.
  53. R. J. Kauth and G. Thomas, “The tasselled cap-a graphic description of the spectral-temporal development of agricultural crops as seen by landsat,” in Proceedings of the LARS Symposia, p. 159, 1976.
  54. T. Qingjiu and M. Xiangjun, “Advances in study on vegetation indices,” Department of Pastrol Agricultural Science and Technology, vol. 13, pp. 327–333, 1998. View at Google Scholar
  55. R. D. Jackson, P. Pinter Jr., R. J. Reginato, and S. B. Idso, “Hand-held radiometry: a set of notes developed for use at the workshop of hand-held radiometry,” in Proceedings of the Early Warning and Crop Condition Assessment, Phoenix, Ariz, USA, February 1980.
  56. P. N. Misra and S. G. Wheeler, “Landsat data from agricultural sites: crop signature analysis,” pp. 1473–1482, 1977. View at Google Scholar · View at Scopus
  57. H. Q. Liu and A. Huete, “Feedback based modification of the NDVI to minimize canopy background and atmospheric noise,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 457–465, 1995. View at Publisher · View at Google Scholar · View at Scopus
  58. L. Wang, J. Liu, L. Yang, Z. Chen, X. Wang, and B. Ouyang, “Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring,” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 29, no. 18, pp. 136–145, 2013. View at Publisher · View at Google Scholar · View at Scopus
  59. B. Li, R. Liu, S. Liu, Q. Liu, F. Liu, and G. Zhou, “Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system,” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 28, no. 13, pp. 160–165, 2012. View at Publisher · View at Google Scholar · View at Scopus
  60. P. J. Zarco-Tejada, V. González-Dugo, and J. A. J. Berni, “Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera,” Remote Sensing of Environment, vol. 117, pp. 322–337, 2012. View at Publisher · View at Google Scholar · View at Scopus
  61. J. A. J. Berni, P. J. Zarco-Tejada, G. Sepulcre-Cantó, E. Fereres, and F. Villalobos, “Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery,” Remote Sensing of Environment, vol. 113, no. 11, pp. 2380–2388, 2009. View at Publisher · View at Google Scholar · View at Scopus
  62. P. J. Zarco-Tejada, V. González-Dugo, L. E. Williams et al., “A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index,” Remote Sensing of Environment, vol. 138, pp. 38–50, 2013. View at Publisher · View at Google Scholar · View at Scopus
  63. X. Wang, M. Wang, S. Wang, and Y. Wu, “Extraction of vegetation information from visible unmanned aerial vehicle images,” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 31, no. 5, pp. 152–159, 2015. View at Publisher · View at Google Scholar · View at Scopus
  64. A. A. Gitelson, “Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation,” Journal of Plant Physiology, vol. 161, no. 2, pp. 165–173, 2004. View at Publisher · View at Google Scholar · View at Scopus
  65. M. S. Kim, C. Daughtry, E. Chappelle, J. McMurtrey, and C. Walthall, “The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (a par),” in Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, CNES, Phoenix, Ariz, USA, January 1994.
  66. C. S. T. Daughtry, C. L. Walthall, M. S. Kim, E. B. De Colstoun, and J. E. McMurtrey III, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sensing of Environment, vol. 74, no. 2, pp. 229–239, 2000. View at Publisher · View at Google Scholar · View at Scopus
  67. Z. Funian, Z. Hong, C. Jiayu, W. Ruijun, and Y. Fuklin, “Preliminary investigation on difference of crop water stress index baseline for maize,” Chinese Agricultural Science Bulletin, vol. 29, pp. 46–53, 2013. View at Google Scholar
  68. S. A. O'Shaughnessy, S. R. Evett, P. D. Colaizzi, and T. A. Howell, “Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton,” Agricultural Water Management, vol. 98, no. 10, pp. 1523–1535, 2011. View at Publisher · View at Google Scholar · View at Scopus
  69. S. B. Idso, R. D. Jackson, P. J. Pinter Jr., R. J. Reginato, and J. L. Hatfield, “Normalizing the stress-degree-day parameter for environmental variability,” Agricultural Meteorology, vol. 24, pp. 45–55, 1981. View at Publisher · View at Google Scholar · View at Scopus
  70. B. B. D. Silva, J. A. Ferreira, and T. V. R. Rao, “Crop water stress index and water-use efficiency for melon (Cucumis melo l.) on different irrigation regimes,” Agricultural Journal, 2007. View at Google Scholar
  71. G. Kar and A. Kumar, “Surface energy fluxes and crop water stress index in groundnut under irrigated ecosystem,” Agricultural and Forest Meteorology, vol. 146, no. 1-2, pp. 94–106, 2007. View at Publisher · View at Google Scholar · View at Scopus
  72. V. Lebourgeois, J.-L. Chopart, A. Bégué, and L. Le Mézo, “Towards using a thermal infrared index combined with water balance modelling to monitor sugarcane irrigation in a tropical environment,” Agricultural Water Management, vol. 97, no. 1, pp. 75–82, 2010. View at Publisher · View at Google Scholar · View at Scopus
  73. A. Anda, “Irrigation timing in maize by using the crop water stress index (CWSI),” Cereal Research Communications, vol. 37, no. 4, pp. 603–610, 2009. View at Publisher · View at Google Scholar
  74. A. Ruimy, L. Kergoat, and A. Bondeau, “Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency,” Global Change Biology, vol. 5, no. 1, pp. 56–64, 1999. View at Publisher · View at Google Scholar · View at Scopus
  75. A. Haxeltine and I. C. Prentice, “A general model for the light-use efficiency of primary production,” Functional Ecology, vol. 10, no. 5, pp. 551–561, 1996. View at Publisher · View at Google Scholar · View at Scopus
  76. A. A. Gitelson, M. N. Merzlyak, and O. B. Chivkunova, “Optical properties and nondestructive estimation of anthocyanin content in plant leaves,” Photochemistry and Photobiology, vol. 74, no. 1, pp. 38–45, 2001. View at Publisher · View at Google Scholar · View at Scopus
  77. R. D. Jackson, “Spectral indices in N-Space,” Remote Sensing of Environment, vol. 13, no. 5, pp. 409–421, 1983. View at Publisher · View at Google Scholar · View at Scopus
  78. F. Baret and G. Guyot, “Potentials and limits of vegetation indices for LAI and APAR assessment,” Remote Sensing of Environment, vol. 35, no. 2-3, pp. 161–173, 1991. View at Publisher · View at Google Scholar · View at Scopus
  79. P. Ashburn, “The vegetative index number and crop identification,” NASA, Proc. of Tech., vol. 1, Johnson Space Center, 1979. View at Google Scholar
  80. S. Plummer, P. North, and S. Briggs, “The angular vegetation index: An atmospherically resistant index for the second along track sacnning radiometer (atsr-2),” in Proceedings of the Sixth International Symposium of Physical Measurements and Signatures in Remote Sensing, Val D’Isere, France, 1994.
  81. P. J. Zarco-Tejada, A. Berjón, R. López-Lozano et al., “Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy,” Remote Sensing of Environment, vol. 99, no. 3, pp. 271–287, 2005. View at Publisher · View at Google Scholar · View at Scopus
  82. C. S. T. Daughtry, “Agroclimatology: Discriminating crop residues from soil by shortwave infrared reflectance,” Agronomy Journal, vol. 93, no. 1, pp. 125–131, 2001. View at Publisher · View at Google Scholar · View at Scopus
  83. F. Li, Y. Miao, G. Feng et al., “Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices,” Field Crops Research, vol. 157, pp. 111–123, 2014. View at Publisher · View at Google Scholar · View at Scopus
  84. N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sensing of Environment, vol. 76, no. 2, pp. 156–172, 2001. View at Publisher · View at Google Scholar · View at Scopus
  85. A. A. Gitelson, G. P. Keydan, and M. N. Merzlyak, “Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves,” Geophysical Research Letters, vol. 33, no. 11, Article ID L11402, 2006. View at Publisher · View at Google Scholar · View at Scopus
  86. C. Buschmann and E. Nagel, “In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation,” International Journal of Remote Sensing, vol. 14, no. 4, pp. 711–722, 1993. View at Publisher · View at Google Scholar · View at Scopus
  87. T. H. Demetriades-Shah, M. D. Steven, and J. A. Clark, “High resolution derivative spectra in remote sensing,” Remote Sensing of Environment, vol. 33, no. 1, pp. 55–64, 1990. View at Publisher · View at Google Scholar · View at Scopus
  88. A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira, “Overview of the radiometric and biophysical performance of the MODIS vegetation indices,” Remote Sensing of Environment, vol. 83, no. 1-2, pp. 195–213, 2002. View at Publisher · View at Google Scholar · View at Scopus
  89. J. Torres-Sánchez, J. M. Peña, A. I. de Castro, and F. López-Granados, “Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV,” Computers and Electronics in Agriculture, vol. 103, pp. 104–113, 2014. View at Publisher · View at Google Scholar · View at Scopus
  90. B. Pinty and M. M. Verstraete, “GEMI: a non-linear index to monitor global vegetation from satellites,” Vegetatio, vol. 101, no. 1, pp. 15–20, 1992. View at Publisher · View at Google Scholar · View at Scopus
  91. M. Louhaichi, M. M. Borman, and D. E. Johnson, “Spatially located platform and aerial photography for documentation of grazing impacts on wheat,” Geocarto International, vol. 16, no. 1, pp. 65–70, 2001. View at Publisher · View at Google Scholar · View at Scopus
  92. A. A. Gitelson and M. N. Merzlyak, “Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll,” Journal of Plant Physiology, vol. 148, no. 3-4, pp. 494–500, 1996. View at Publisher · View at Google Scholar · View at Scopus
  93. E. T. Kanemasu, J. L. Hellman, J. O. Bagley, and W. L. Powers, “Using Landsat data to estimate evapotranspiration of winter wheat,” Environmental Management, vol. 1, no. 6, pp. 515–520, 1977. View at Publisher · View at Google Scholar · View at Scopus
  94. R. C. G. Smith, J. Adams, D. J. Stephens, and P. T. Hick, “Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite,” Australian Journal of Agricultural Research, vol. 46, no. 1, pp. 113–125, 1995. View at Publisher · View at Google Scholar · View at Scopus
  95. G. Badhwar, “The use of parameters to separate and identify spring small grains,” in Proceedings of the Quarterly Technical Interchange Meeting NASA Johnson Space Flight Center, Houston, Tex, USA, 1981.
  96. H. K. Lichtenthaler, M. Lang, M. Sowinska, F. Heisel, and J. A. Miehé, “Detection of vegetation stress via a new high resolution fluorescence imaging system,” Journal of Plant Physiology, vol. 148, no. 5, pp. 599–612, 1996. View at Publisher · View at Google Scholar · View at Scopus
  97. Y. Zhang, Q.-Y. Meng, J.-L. Wu, and F. Zhao, “Study of environmental vegetation index based on environment satellite CCD data and LAI inversion,” Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, vol. 31, no. 10, pp. 2789–2793, 2011. View at Publisher · View at Google Scholar · View at Scopus
  98. A.-K. Mahlein, T. Rumpf, P. Welke et al., “Development of spectral indices for detecting and identifying plant diseases,” Remote Sensing of Environment, vol. 128, pp. 21–30, 2013. View at Publisher · View at Google Scholar · View at Scopus
  99. H. B. Musick and R. E. Pelletier, “Response to soil moisture of spectral indexes derived from bidirectional reflectance in thematic mapper wavebands,” Remote Sensing of Environment, vol. 25, no. 2, pp. 167–184, 1988. View at Publisher · View at Google Scholar · View at Scopus
  100. R. E. Crippen, “Calculating the vegetation index faster,” Remote Sensing of Environment, vol. 34, no. 1, pp. 71–73, 1990. View at Publisher · View at Google Scholar · View at Scopus
  101. D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sensing of Environment, vol. 90, no. 3, pp. 337–352, 2004. View at Publisher · View at Google Scholar · View at Scopus
  102. P. Misra and S. Wheeler, “Landsat data from agricultural sites-crop signature analysis,” in Proceedings of the International Symposium on Remote Sens, pp. 1473–1482, Environ, 1977.
  103. Z. Yang, P. Willis, and R. Mueller, “Impact of band-ratio enhanced awifs image on crop classification accuracy,” in Proceedings of the 17th William Pecora Memorial Remote Sensing Symposium, Denver, Colo, USA, 2008.
  104. B. Datt, “A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using Eucalyptus leaves,” Journal of Plant Physiology, vol. 154, no. 1, pp. 30–36, 1999. View at Publisher · View at Google Scholar · View at Scopus
  105. D. A. Sims and J. A. Gamon, “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages,” Remote Sensing of Environment, vol. 81, no. 2-3, pp. 337–354, 2002. View at Publisher · View at Google Scholar · View at Scopus
  106. J. M. Chen, “Evaluation of vegetation indices and a modified simple ratio for boreal applications,” Canadian Journal of Remote Sensing, vol. 22, no. 3, pp. 229–242, 1996. View at Publisher · View at Google Scholar · View at Scopus
  107. E. R. Hunt Jr. and B. N. Rock, “Detection of changes in leaf water content using Near- and Middle-Infrared reflectances,” Remote Sensing of Environment, vol. 30, no. 1, pp. 43–54, 1989. View at Publisher · View at Google Scholar · View at Scopus
  108. H. Mcnairn and R. Protz, “Mapping corn residue cover on agricultural fields in oxford county, ontario, using thematic mapper,” Canadian Journal of Remote Sensing, vol. 19, no. 2, pp. 152–159, 1993. View at Publisher · View at Google Scholar · View at Scopus
  109. B. Datt, “Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves,” International Journal of Remote Sensing, vol. 20, no. 14, pp. 2741–2759, 1999. View at Publisher · View at Google Scholar · View at Scopus
  110. J. E. Vogelmann, B. N. Rock, and D. M. Moss, “Red edge spectral measurements from sugar maple leaves,” International Journal of Remote Sensing, vol. 14, no. 8, pp. 1563–1575, 1993. View at Publisher · View at Google Scholar · View at Scopus
  111. L. Serrano, J. Peñuelas, and S. L. Ustin, “Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals,” Remote Sensing of Environment, vol. 81, no. 2-3, pp. 355–364, 2002. View at Publisher · View at Google Scholar · View at Scopus
  112. S. K. McFeeters, “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features,” International Journal of Remote Sensing, vol. 17, no. 7, pp. 1425–1432, 1996. View at Publisher · View at Google Scholar · View at Scopus
  113. J. Verrelst, M. E. Schaepman, B. Koetz, and M. Kneubühler, “Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data,” Remote Sensing of Environment, vol. 112, no. 5, pp. 2341–2353, 2008. View at Publisher · View at Google Scholar · View at Scopus
  114. C. J. Tucker, “Red and photographic infrared linear combinations for monitoring vegetation,” Remote Sensing of Environment, vol. 8, no. 2, pp. 127–150, 1979. View at Publisher · View at Google Scholar · View at Scopus
  115. L. Wang and J. J. Qu, “NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing,” Geophysical Research Letters, vol. 34, no. 20, Article ID L20405, 2007. View at Publisher · View at Google Scholar · View at Scopus
  116. N. S. Goel and W. Qin, “Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation,” Remote Sensing Reviews, vol. 10, no. 4, pp. 309–347, 1994. View at Publisher · View at Google Scholar · View at Scopus
  117. G. Rondeaux, M. Steven, and F. Baret, “Optimization of soil-adjusted vegetation indices,” Remote Sensing of Environment, vol. 55, no. 2, pp. 95–107, 1996. View at Publisher · View at Google Scholar · View at Scopus
  118. J. A. Gamon, J. Peñuelas, and C. B. Field, “A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency,” Remote Sensing of Environment, vol. 41, no. 1, pp. 35–44, 1992. View at Publisher · View at Google Scholar · View at Scopus
  119. M. N. Merzlyak, A. A. Gitelson, O. B. Chivkunova, and V. Y. Rakitin, “Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening,” Physiologia Plantarum, vol. 106, no. 1, pp. 135–141, 1999. View at Publisher · View at Google Scholar · View at Scopus
  120. G. A. Blackburn, “Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches,” Remote Sensing of Environment, vol. 66, no. 3, pp. 273–285, 1998. View at Publisher · View at Google Scholar · View at Scopus
  121. J.-L. Roujean and F.-M. Breon, “Estimating PAR absorbed by vegetation from bidirectional reflectance measurements,” Remote Sensing of Environment, vol. 51, no. 3, pp. 375–384, 1995. View at Publisher · View at Google Scholar · View at Scopus
  122. A. Gitelson and M. N. Merzlyak, “Spectral relfectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation,” Journal of Plant Physiology, vol. 143, no. 3, pp. 286–292, 1994. View at Publisher · View at Google Scholar · View at Scopus
  123. J. A. Gamon and J. S. Surfus, “Assessing leaf pigment content and activity with a reflectometer,” New Phytologist, vol. 143, no. 1, pp. 105–117, 1999. View at Publisher · View at Google Scholar · View at Scopus
  124. R. Escadafal and A. Huete, “Etude des propriétés spectrales des sols arides appliquée à l'amélioration des indices de végétation obtenus par télédétection,” Comptes Rendus de l'Académie des Sciences, vol. 312, no. 2, pp. 1385–1391, 1991. View at Google Scholar
  125. R. L. Pearson and L. D. Miller, “Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie,” Remote Sensing of Environme, vol. 8, p. 1355, 1972. View at Google Scholar
  126. D. Haboudane, J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze, “Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture,” Remote Sensing of Environment, vol. 81, no. 2-3, pp. 416–426, 2002. View at Publisher · View at Google Scholar · View at Scopus
  127. R. Pu and P. Gong, Advances in Environmental Remote Sensing, Higher Education, Beijing, 2011.
  128. G. S. Birth and G. R. McVey, “Measuring the color of growing turf with a reflectance spectrophotometer,” Agronomy Journal, vol. 60, no. 6, p. 640, 1968. View at Publisher · View at Google Scholar
  129. J. E. McMurtrey III, E. W. Chappelle, M. S. Kim, J. J. Meisinger, and L. A. Corp, “Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements,” Remote Sensing of Environment, vol. 47, no. 1, pp. 36–44, 1994. View at Publisher · View at Google Scholar · View at Scopus
  130. E. W. Chappelle, M. S. Kim, and J. E. McMurtrey, “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,” Remote Sensing of Environment, vol. 39, no. 3, pp. 239–247, 1992. View at Publisher · View at Google Scholar · View at Scopus
  131. B. Datt, “Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves,” Remote Sensing of Environment, vol. 66, no. 2, pp. 111–121, 1998. View at Publisher · View at Google Scholar · View at Scopus
  132. A. Bannari, H. Asalhi, and P. M. Teillet, “Transformed difference vegetation index (TDVI) for vegetation cover mapping,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS '02, pp. 3053–3055, Toronto, Canada, 2002. View at Scopus
  133. F. Baret, G. Guyot, and D. J. Major, “TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation,” in Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, pp. 1355–1358, IEEE, 1989.
  134. J. Rouse Jr., “Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation,” NASA Technical Reports Server, Tex, USA, 1974, https://ntrs.nasa.gov/search.jsp?R=19740022555. View at Google Scholar
  135. 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. View at Publisher · View at Google Scholar · View at Scopus
  136. F. N. Kogan, “Application of vegetation index and brightness temperature for drought detection,” Advances in Space Research, vol. 15, no. 11, pp. 91–100, 1995. View at Publisher · View at Google Scholar · View at Scopus
  137. A. F. Wolf, “Using WorldView-2 Vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios,” in Proceedings of the 18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, Baltimore, Md, USA, 2012. View at Publisher · View at Google Scholar · View at Scopus
  138. C. D. Elvidge and Z. Chen, “Comparison of broad-band and narrow-band red and near-infrared vegetation indices,” Remote Sensing of Environment, vol. 54, no. 1, pp. 38–48, 1995. View at Publisher · View at Google Scholar · View at Scopus