Hyperspectral remote sensing of agriculture

Current Science

Volumn 108, Issue 5, 2015, Pages 848-859

  Sahoo, R N ^a   Ray, S S ^b   Manjunath, K R ^c  

^a INDIAN AGRICULTURAL RESEARCH INSTITUTE   (India)

^b Mahalanobis National Crop Forecast Centre   (India)

^c INDIAN SPACE RESEARCH ORGANISATION   (India)

Author keywords

Agriculture; Biotic and abiotic stress; Hyperspectral remote sensing; Spectral reflectance

Indexed keywords

EID: 84925003685     PISSN: 00113891     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (100)

1. Blackburn, G. A., Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing Environ., 1998, 66, 273-285.
2. Thenkabail, P. S., Smith, R. B. and Pauw, E. D., Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing Environ., 2000, 71, 158-182.
3. Campbell, J. B., Introduction to Remote Sensing, Taylor and Francis, London, 1996, p. 622.
4. Haboudane, D., Miller, J. R., Trembley, N., Zarco-Tejada, P. J. and Dextraze, L., Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing Environ., 2002, 81, 416-426.
5. Champagne, C. M., Staenz, K., Bannari, A., Mcnairn, H. and Deguise, J. C., Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies Remote Sensing Environ., 2003, 87, 148-160.
6. Strachan, I. B., Pattey, E. and Boisvert, J. B., Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing Environ., 2002, 80, 213-224.
7. Wang, F. M., Huang, J. F. and Wang, X. Z., Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 2008, 50(3), 291-299.
8. Pradhan, S., Bandyopadhyay, K. K., Sahoo, R. N., Sehgal, V. K., Singh, R., Gupta, V. K. and Joshi, D. K., Predicting wheat grain and biomass yield using canopy reflectance of booting stage. J. Indian Soc. Remote Sensing, 2014; doi: 10.1007/s12524-014-0372-x.
9. Zhu, Y., Li, Y., Feng, W., Tian, Y., Yao, X. and Cao, W., Monitoring leaf nitrogen in wheat using canopy reflectance spectra. Can. J. Plant Sci., 2006, 86, 1037-1046.
10. Ranjan, R., Chopra, U. K., Sahoo, R. N., Singh, A. K. and Pradhan, S., Assessment of plant nitrogen stress through hyperspectral indices. Int. J. Remote Sensing, 2012, 22(20), 6342-6360.
11. Mahajan, G. R., Sahoo, R. N., Pandey, R. N., Gupta, V. K. and Kumar, D., Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precision Agric., 2014, 15(2), 227-240.
12. Prasannakumar, N. R., Chander, S. and Sahoo, R. N., Characterization of brown plant hopper damage on rice crops through hyperspectral remote sensing under field conditions. Phytoparasitica, 2014, 42, 387-395.
13. Prabhakar, M., Prasad, Y. G., Thirupathi, M., Sreedevi, G., Dharajothi, B. and Venkateswarlu, B., Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput. Electron. Agric., 2011, 79, 189-198.
14. Hunt, J., Ramond, E. and Rock, B. N., Detection in changes in leaf water content using near and mid-infrared reflectance. Remote Sensing Environ., 1989, 30, 45-54.
15. Jacquemoud, S. et al., PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing Environ., 2009, 113, S56-S66.
16. Manjunath, K. R. et al., Developing spectral library of major plant species of Western Himalayas using ground observations. J. Indian Soc. Remote Sensing, 2014, 42(1), 201-216.
17. Asner, G. P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing Environ., 1998, 64, 234-253.
18. Despan, D. and Jacquemoud, S., Optical properties of soil and leaf: necessity and problems of modeling. In Reflection Properties of Vegetation and Soil (eds von Schönermark, M., Geiger, B. and Röser, H. P.), Wissenschaft und TechnikVerlag, Berlin, 2004, pp. 39-70.
19. Ustin, S. L., Roberts, D. A., Green, R. O., Zomer, R. J. and Garcia, M., Remote sensing methods monitor natural resources. Photon. Spectra, 1999, 33(N10), 108-113.
20. Jensen, J. R., Remote Sensing of the Environment: An Erath Resource Perspective, Prentice-Hall, 2000.
21. Guyot, G., Optical properties of vegetation canopies. In Applications of Remote Sensing in Agriculture (eds Steven, M. D. and Clark, J. A.), Butterworths, London, 1990, pp. 19-43.
22. Curran, P. J., Remote sensing of foliar chemistry. Remote Sensing Environ., 1989, 30, 271-278.
23. Breece, H. T. and Holmes, R. A., Bi-directional scattering characteristics of healthy green soybean and corn leaves in vivo. Appl. Opt., 1971, 10, 119-135.
24. Sandmeier, S., Muller, C., Hosgood, B. and Andreoli, G., Sensitivity analysis and quality assessment of laboratory BRDF data. Remote Sensing Environ., 1998, 64, 176-191.
25. Tripathi, R., Sahoo, R. N., Sehgal, V. K., Tomar, R. K., Chakraborty, D. and Nagarajan, S., Inversion of PROSAIL model for retrieval of plant biophysical parameters. J. Indian Soc. Remote Sensing, 2012, 40(1), 19-28.
26. Cihlar, J., Manak, D. and Voisin, N., AVHRR bidirectional reflectance effects and compositing. Remote Sensing Environ., 1991, 48, 77-88.
27. Roujean, J. L., Leroy, M., Podaire, A. and Deschamps, P. Y., Evidence of surface reflectance bidirectional effects from a NOAA/AVHRR multi-temporal dataset. Int. J. Remote Sensing, 1992, 13(4), 685-698.
28. Sahoo, R. N., Pargol, S., Verma, R. K., Yadav, S. K., Kumar, V. V. S., Gupta, V. K. and Chinnusamy, V., Combination of spectroscopy and visual imaging for high throughput evaluation of abiotic stress tolerance in rice. In International Plant Phenotyping Symposium on Phenotyping for Agriculture Sustainability by International Plant Phenotyping Network (IPPN), Chennai, 17-19 February 2014, pp. 57-58.
29. Pandya, M. R., Shah, D. B., Trivedi, H. J., Lunagaria, M. M., Pandey, V., Panigrahy, S. and Parihar, J. S., Field measurements of plant emissivity spectra: an experimental study on remote sensing of vegetation in the thermal infrared region. J. Indian Soc. Remote Sensing, 2014; doi: 10.1007/s12524-013-0283-2.
30. Ribeiro da Luz, B. and Crowley, J. K., Spectral emissivity features of broad leaf plants: prospects for remote sensing in the thermal infrared (8.0-14.0 μm). Remote Sensing Environ., 2007, 109, 393-405.
31. Elvidge, C. D., Thermal infrared reflectance of dry plant materials: 2.5-20.0 μm. Remote Sensing Environ., 1988, 26, 265-285.
32. Kirkland, L., Herr, K., Keim, E., Adams, P., Salisbury, J. W., Hackwell, J. and Treiman, A., First use of an airborne thermal infrared hyperspectral scanner for compositional mapping. Remote Sensing Environ., 2002, 80, 447-459.
33. Navalgund, R. R. and Ray, S. S., Hyperspectral Data, Analysis Techniques and Applications, Indian Society of Remote Sensing, 2011, p. 156.
34. Thenkabail, P. S., Optimal hyperspectral narrow bands for discriminating agricultural crops. Remote Sensing Rev., 2002, 20, 257-291.
35. 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 Environ., 2004, 91(3-4), pp. 354-376.
36. Ray, S. S., Das, G., Singh, J. P. and Panigrahy, S., Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. Int. J. Remote Sensing, 2006, 27, 5373-5387.
37. 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, 2002, 68, 607-627.
38. Filippi, A. M. and Jensen, J. R., Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing Environ., 2006, 100, 512-530.
39. 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 Environ., 2008, 112, 576-587.
40. Richards, J. A., Remote Sensing Digital Image Analysis: An Introduction, Springer-Verlag, Berlin, 1993.
41. Miglani, A., Ray, S. S., Pandey, R. and Parihar, J. S., Evaluation of EO-1 Hyperion data for agricultural applications. J. Indian Soc. Remote Sensing, 2008, 36, 255-266.
42. Gamon, J. A., Penuelas, J. and Field, C. B., A narrow-waveband spectral index. Remote Sensing Environ., 1992, 41, 35-44.
43. Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field, C. B., Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing Environ., 1994, 48, 135-146.
44. Thenkabail, P. S., Smith, R. B. and Pauw, E. D., Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing Environ., 2000, 71, 158-182.
45. Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N. and Zucca, C., Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. Int. J. Appl. Earth Obs. Geoinf., 2009, 11(4), 233-243.
46. Elvidge, C. D. and Chen, Z., Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing Environ., 1995, 54, 38-48.
47. Galvao, L. S., Roberts, D. A., Formaggio, A. R., Numata, I. and Breunig, F. M., View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sensing Environ., 2009, 113(4), 846-856.
48. Numata, I., Roberts, D. A., Chadwick, O. A., Schimel, J., Sampaio, F. R., Leonidas, F. C. and Soares, J. V., Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sensing Environ., 2007, 109(3), 314-327.
49. Sahoo, R. N., Bhavanarayana, M., Panda, B. C., Arika, C. N. and Kaur, R., Total information content as an index of soil moisture. J. Indian Soc. Remote Sensing, 2005, 33(1), 17-24.
50. Kosaka, N., Uto, K. and Kosugi, Y., ICA-aided mixed-pixel analysis of hyperspectral data in agricultural land. IEEE Geosci. Remote Sensing Lett., 2005, 2(2), 220-224.
51. Kokaly, R. F. and Clark, R. N., Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing Environ., 1999, 67, 267-287.
52. Cochrane, M. A., Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sensing, 2000, 21(10), 2075-2087.
53. Schmidt, K. S. and Skidmore, A. K., Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing Environ. 2003, 85(1), 92-108.
54. Vaiphasa, C., Ongsomwangc, S., Vaiphasa, T. and Skidmore, A. K., Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuarine, Coastal Shelf Sci., 2005, 65, 371-379.
55. Manjunath, K. R., Ray, S. S. and Panigrahy, S., Discrimination of spectrally-close crops using ground-based hyperspectral data. J. Indian Soc. Remote Sensing, 2011, 39(4), 599-602.
56. 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, 2013, p. 70.
57. 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. Int. J. Appl. Earth Observ. Geoinf., 2012; http://dx.doi.org/10.1016/j.jag.2012.10.006.
58. Antony, R., Ray, S. S. and Panigrahy, S., Discrimination of wheat crop stage using CHRIS/PROBA multi-angle narrowband data. Remote Sensing Lett., 2011, 2(1), 71-80.
59. Swain, P. H. and Davis, S. M., Remote Sensing: The Quantitative Approach, Tata McGraw Hill, New York, 1978, p. 306.
60. Sahoo, R. N., Sehgal, V. K., Pradhan, S., Gupta, V. K. and Krishna, G., Quantitative assessment of anisotropicity of wheat crop through hyper BRDF. In Annual Report of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, 2013, p. 70.
61. Krishna, G. et al., Quantitative analysis of anisotropic BRDF of crops. In National Symposium on Space Technology for Food and Environmental Security and Annual Conventions of the Indian Society of Remote Sensing and the Indian Society of Geomatics, New Delhi, 5-7 December 2012, p. 214.
62. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing Environ., 2004, 90, 337-352.
63. Broge, N. H. and Leblanc, E., Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2000, 76, 156-172.
64. Casanova, D., Epema, G. F. and Goudriaan, J., Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Res., 1998, 55, 83-92.
65. Diker, K. and Bausch, W. C., Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosyst. Eng., 2003, 85, 437-447.
66. Hansen, P. M. and Schjoerring, J. K., Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing Environ., 2003, 86, 542-553.
67. Cohen, Y. et al., Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENUS satellite. Precision Agric., 2009, 11, 520-537.
68. Herrmann, I., Karnieli, A., Bonfil, D. J., Cohen, Y. and Alchanatis, V., SWIR-based spectral indices for assessing nitrogen content in potato fields. Int. J. Remote Sensing, 2010, 31, 5127-5143.
69. Jain, N., Ray, S. S., Singh, J. P. and Panigrahy, S., Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agric., 2007, 8, 225-239.
70. Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ., 2003, 81, 331-354.
71. Penuelas, J., Filella, I., Biel, C., Serrano, L. and Save, R., The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sensing, 1993, 14, 1887-1905.
72. Gao, B. C., NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environ., 1996, 58, 257-266.
73. Hardinsky, M. A., Klemas, V. and Smart, M., The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm. Eng Remote Sensing, 1983, 49, 1477-1483.
74. Colombo, R., Meroni, M., Marchesi, A., Busetto, L., Rossini, M., Giardin, C. and Panigada, C., Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing Environ., 2008, 112, 1820-1834.
75. Pargal, S., Sahoo, R. N., Verma, R. K., Yadav, S. K., Kumar, S., Chinnusamy, V. and Gupta, V. K., A high throughput nondestructive hyperspectral remote sensing approach for phenotyping of drought tolerance in rice. In National Symposium on Space Technology for Food and Environmental Security, and Annual Conventions of the Indian Society of Remote Sensing and the Indian Society of Geomatics, New Delhi, 5-7 December 2012, p. 26.
76. Bandyopadhyay, K. K., Pradhan, S., Sahoo, R. N., Singh, R., Gupta, V. K., Joshi, D. K. and Sutradhar, A. K., Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices. Agric. Water Manage., 2014, 146, 115-123.
77. Baret, F. and Guyot, G., Potential and limits of vegetation indices for LAI and APAR assessment. Remote Sensing Environ., 1991, 35, 161-173.
78. Gobron, N., Pinty, B., Verstraete, M. M. and Govaerts, Y., A semidiscrete model for the scattering of light by vegetation. J. Geophys. Res., 1997, 102, 9431-9446.
79. Colombo, R., Bellingeri, D., Fasolini, D. and Marino, C. M., Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing Environ., 2003, 86, 120-131.
80. Goel, N., Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In Theory and Applications of Optical Remote Sensing (ed. Asrar, G.), Wiley Interscience, New York, 1989, pp. 205-250.
81. Kimes, D. S., Knyazikhin, Y., Privette, J. L., Abuelgasim, A. A. and Gao, F., Inversion methods of physically based models. Remote Sensing Environ., 2000, 18, 381-439.
82. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and Jaggard, K., Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing Environ., 1995, 52, 163-172.
83. Tripathi, R., Sahoo, R. N., Gupta, V. K., Sehgal, V. K. and Sahoo, P. M., Developing vegetation health index from biophysical variables derived using MODIS satellite data in the Trans Gangetic plains of India. Emirates J. Food Agric., 2013, 25(5), 376-384.
84. Burman, D., Sehgal, V. K., Sahoo, R. N. and Nagarajan, S., Relationship of bidirectional reflectance of wheat with biophysical parameters and its radiative transfer modelling using PROSAIL. J. Indian Soc. Remote Sensing, 2010, 38, 141-151.
85. Walthall, C., Dulaney, W., Anderson, M., Norman, J., Fang, H. L. and Liang, S. L., A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM + imagery. Remote Sensing Environ., 2004, 92(4), 465-474.
86. Mridha, N., Sahoo, R. N., Kumar, D. N., Sehgal, V. K., Krishna, G., Pradhan, S. and Gupta, V. K., Genetic algorithm based inversion modelling of PROSAIL for retrieval of wheat biophysical parameters from the reflectance data. J. Agric. Phys., 2014, 14(1), 87-95.
87. Fanga, H., Lianga, S. and Kuusk, A., Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing Environ., 2003, 85, 257-270.
88. Satpathy, S. and Dadhwal, V. K., Principal component inversion technique for the retrieval of leaf area index. J. Indian Soc. Remote Sensing, 2005, 33(2), 323-330.
89. Durbha, S. S., King, R. L. and Younan, N. H., Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing Environ., 2007, 107, 348-361.
90. Riley, J. R., Remote sensing in entomology. Annu. Rev. Entomol., 1989, 34, 247-271.
91. Nilsson, H. E., Remote sensing and image analysis in plant pathology. Annu. Rev. Phytopathol., 1995, 15, 489-527.
92. Delalieux, S., van Aardt, J., Keulemans, W., Schrevens, E. and Coppin, P., Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications. Eur. J. Agron., 2007, 27, 130-143.
93. Yang, C. M. and Cheng, C. H., Spectral characteristics of rice plants infested by brown planthoppers. Proc. Natl. Sci. Council, 2001, 25(3), 180-186.
94. Yang, L., Stehman, S. V., Smith, J. H. and Wickham, J. D., Thematic accuracy of MRLC land cover for the eastern United States. Remote Sensing Environ., 2001, 76, 418-422.
95. Riedell, W. E. and Blackmer, T. M., Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Sci., 1999, 39, 1835-1840.
96. Kumar, J., Vashisth, A., Sehgal, V. K. and Gupta, V. K., Identification of aphid infestation in mustard by hyperspectral remote sensing. J. Agric. Phys., 2010, 10, 53-60.
97. Ray, S. S., Jain, N., Arora, R. K., Chavan, S. and Panigrahy, S., Utility of hyperspectral data for potato late blight disease detection. J. Indian Society Remote Sensing, 2011, 39(2), 161-169.
98. Das, D. K., Pradhan, S., Sehgal, V. K., Sahoo, R. N., Gupta, V. K. and Singh, R., Spectral reflectance characteristics of healthy and yellow mosaic virus infected soybean (Glycine max L.) leaves in a semiarid environment. J. Agrometeorol., 2013, 15(1), 36-38.
99. Nagaraja, A., Sahoo, R. N., Usha, K., Singh, S. K., Sivaramanae N. and Gupta, V. K., Spectral discrimination of healthy and malformed mango panicles using spectroradiometer. Indian J. Hortic., 2014, 71(1), 40-44.
100. Krishna, G. et al., Assessing wheat yellow rust disease through hyperspectral remote sensing. Int. Arch. Photogramm. Remote Sensing Spatial Inform. Sci., 2014, XL-8, 1413-1416; doi:10.5194/isprsarchives-XL-8-1413-2014.