Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

International Journal of Applied Earth Observation and Geoinformation

Volumn 43, Issue , 2015, Pages 32-42

  Elarab, Manal ^a,b   Ticlavilca, Andres M ^b   Torres Rua, Alfonso F ^b   Maslova, Inga ^c   McKee, Mac ^a,b  

^a Utah State University   (United States)

^b UTAH STATE UNIVERSITY   (United States)

^c AMERICAN UNIVERSITY   (United States)

Author keywords

Chlorophyll concentration; High spatial resolution imagery; Precision agriculture; Relevance vector machine; Remote sensing

Indexed keywords

CHLOROPHYLL; INFRARED IMAGERY; MULTISPECTRAL IMAGE; PRECISION AGRICULTURE; REMOTE SENSING; SPATIAL RESOLUTION; VEGETATION INDEX;

EID: 84943609503     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2015.03.017     Document Type: Article

References (104)

1. Bachour, R., Walker, W., Ticlavilca, A., McKee, M., Maslova, I., 2014. Estimation of spatially distributed evapotranspiration using remote sensing and a relevance vector machine. J. Irrig. Drain. Eng. 140 (8), 4014029.
2. Bauer, M.E., 1985. Spectral inputs to crop identification and condition assessment. Proc. IEEE 73 (6), 1071-1085.
3. Baret, F., Buis, S., 2008. Estimating canopy characteristics from remote sensing observations: review of methods and associated problems. In: Inadvances in Land Remote Sensing. Springer Netherlands, pp. 173-201.
4. Benedetti, R., Rossini, P., 1993. On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 45 (3), 311-326.
5. Bernie, J., Zarco-Tejada, P.J., Suárez, L., Fereres, E., 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47 (3), 722-738.
6. Berry, J., Bjorkman, O., 1980. Photosynthetic response and adaptation to temperature in higher plants. Annu. Rev. Plant Physiol. 31 (1), 491-543.
7. Blackburn, G.A., 1998. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66 (3), 273-285.
8. Blum, A., Mayer, J., Gozlan, G., 1982. Infrared thermal sensing of plant canopies as a screening technique for dehydration avoidance in wheat. Field Crops Res. 5, 137-146.
9. Brisco, B., Brown, R.J., Hirose, T., McNairn, H., Staenz, K., 1998. Precision agriculture and the role of remote sensing: a review. Can. J. Remote Sens. 24 (3), 315-327.
10. Bonge, N.H., Leblanc, E., 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76 (2), 156-172.
11. Camps-Valls, G., Bruzzone, L., Rojo-álvarez, J.L., Melgani, F., 2006. Robust support vector regression for biophysical variable estimation from remotely sensed images. Geosci. Remote Sens. Lett. IEEE 3 (3), 339-343.
12. Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Vila-Francés, J., Amorós-López, J., Calpe-Maravilla, J., 2006b. Retrieval of oceanic chlorophyll concentration with relevance vector machines. Remote Sens. Environ. 105 (1), 23-33.
13. Carter, G.A., Spiering, B.A., 2002. Optical properties of intact leaves for estimating chlorophyll concentration. J. Environ. Qual. 31 (5), 1424-1432.
14. Chaerle, L., Van Der Straeten, D., 2000. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 5 (11), 495-501.
15. Cipollini, P., Corsini, G., Diani, M., Grasso, R., 2001. Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks. IEEE Trans. Geosci. Remote Sens. 39 (7), 1508-1524.
16. Clemens, S.R., (2012). Procedures for Correcting Digital Camera Imagery Acquired by the AggieAir Remote Sensing Platform, Utah State University, 2012.
17. N., Cristianini, J., Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge university press, 2000.
18. Crowther, B.G., (1992). Radiometric calibration of multispectral video imagery (Doctoral dissertation, Utah State University. Department of biological and Irrigation Engineering).
19. Daberkow, S.G., McBride, W.D., Robert, P.C., Rust, R.H., Larson, W.E., 2000. Adoption of precision agriculture technologies by US farmers. Bloomington, Minnesota, USA In: Proceedings of the 5th International Conference on Precision Agriculture, 16-19 July, pp. 1-12, American Society of Agronomy.
20. Datt, B., 1999. A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves. J. Plant Physiol. 154 (1), 30-36.
21. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., De Colstoun, E.B., McMurtrey Iii, J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74 (2), 229-239.
22. Dawson, T.P., North, P.R.J., Plummer, S.E., Curran, P.J., 2003. Forest ecosystem chlorophyll content: implications for remotely sensed estimates of net primary productivity. Int. J. Remote Sens. 24 (3), 611-617.
23. De Martino, M., Mantero, P., Serpico, S.B., Carta, E., Corsini, G., Grasso, R., 2002. Water quality estimation by neural networks based on remotely sensed data analysis. Proc. of the International Workshop on Geo-spatial Knowledge Processing for Natural Resource Management, 54-58.
24. Demarez, V., Gastellu-Etchegorry, J.P., 2000. A modeling approach for studying forest chlorophyll content. Remote Sens. Environ. 71 (2), 226-238.
25. Demir, B., Erturk, S., 2007. Hyperspectral image classification using relevance vector machines. Geosci. Remote Sens. Lett. IEEE 4 (4), 586-590.
26. Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Simonneaux, V., 2006. Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration crops coefficients leaf area index and remotely-sensed vegetation indices. Agric. Water Manage. 79 (1), 1-27.
27. Evans, J.R., 1989. Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78 (1), 9-19.
28. Franke, J., Menz, G., 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis. Agric. 8 (3), 161-172.
29. Genton, M.G., 2002. Classes of kernels for machine learning: a statistics perspective. J. Mach. Learn. Res. 2, 299-312.
30. Gitelson, A., Merzlyak, M.N., 1994. Quantitative estimation of chlorophyll a using reflectance spectra: experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B 22 (3), 247-252.
31. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58 (3), 289-298.
32. Gitelson, A.A., Vina, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J., 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32 (8).
33. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Suyker, A.E., 2006. Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res. 1984-2012 (111), D8.
34. Gómez-Chova, L., Muñoz-Marí, J., Laparra, V., Malo-López, J., Camps-Valls, G., 2011. A review of kernel methods in remote sensing data analysis. In: In Optical Remote Sensing, Springer Berlin Heidelberg, pp. 171-206.
35. González Vilas, L., Spyrakos, E., Torres Palenzuela, J.M., 2011. Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of galician rias (NW Spain). Remote Sens. Environ. 115 (2), 524-535.
36. Guyon, I., Elisseeff, A., 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182.
37. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81 (2), 416-426.
38. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90 (3), 337-352.
39. Hassan Esfahani, L., Torres-Rua, A., Jensen, A., McKee, M., (2014). Top soil moisture estimation for precision agriculture using unmmaned aerial vehicle multispectral imagery, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
40. Hassan Esfahani, L., Torres-Rua, A., Jensen, A., McKee, M., 2014b. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens., 2015.
41. Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., Tibshirani, R., 2009. The elements of statistical learning, Vol. 2. Springer, New York (No. 1).
42. Idso, S.B., Jackson, R.D., Reginato, R.J., 1977. Remote-sensing of crop yields. Science 196 (4285), 19-25. Infrared Cameras Incorporated, http://www.infraredcamerasinc.com, 2012.
43. Jackson, Idso, S.B., Reginato, R.J., Pinter, P.J., 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17 (4), 1133-1138.
44. Jackson, R.D., (1984, October). Remote sensing of vegetation characteristics for farm management, In 1984 Technical Symposium East (pp. 81-97), International Society for Optics and Photonics.
45. Jensen, A.M., 2014. Innovative payloads for small unmanned aerial system-based personal remote sensing and applications. PhD thesis. In: Department of Electrical & Computer Engineering. Logan, UT, Utah State University.
46. Jiang, J., Liu, D., Gu, J., Susstrunk, S., (2013, January). What is the space of spectral sensitivity functions for digital color cameras? In Applications of Computer Vision (WACV), 2013 IEEE Workshop on (pp. 168-179). IEEE.
47. Johnson, L.F., Hlavka, C.A., Peterson, D.L., 1994. Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect. Remote Sens. Environ. 47 (2), 216-230.
48. Kalluri, S., Gilruth, P., Bergman, R., Plante, R., (2002, June). Impacts of NASA's remote sensing data on policy and decision making at state and local agencies in the United States, In Geoscience and Remote Sensing Symposium, 2002, IGARSS'02, 2002 IEEE International (Vol. 3, pp. 1691-1693), IEEE.
49. Kim, M.S., 1994. The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR). In: Masters Thesis, Department of Geography. College Park, MD, University of Maryland.
50. Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., McMurtrey III, J.E., Walthall, C.L., 1994. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar). Val D'Isere, France In: Proceedings of the 6th Symposium on Physical Measurements and Signatures in Remote Sensing, January 17-21, pp. 299-306.
51. Knudby, A., LeDrew, E., Brenning, A., 2010. Predictive mapping of reef fish species richness, diversity and biomass in zanzibar using IKONOS imagery and machine-learning techniques. Remote Sens. Environ. 114 (6), 1230-1241.
52. Kohavi, R., 1995 August. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI Vol. 14, 1137-1145, No. 2.
53. Lamb, D.W., Brown, R.B., 2001. PA-precision agriculture: remote-sensing and mapping of weeds in crops. J. Agric. Eng. Res. 78 (2), 117-125.
54. Lambert, D., Lowenberg-De Boer, J., (2000). Precision agriculture profitability review, Purdue University, http://agriculture.purdue.edu/SSMC/Frames/newsoilsX.pdf
55. Le Maire, G., Francois, C., Dufrene, E., 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 89 (1), 1-28.
56. Liaghat, S., Balasundram, S.K., 2010. A review: the role of remote sensing in precision agriculture. Am. J. Agric. Biol. Sci. 5 (1), 50.
57. MacDonald, R.B., Hall, F.G., 1980. Global crop forecasting. Science 208 (4445), 670-679.
58. Mathur, A., Foody, G.M., 2008. Crop classification by support vector machine with intelligently selected training data for an operational application. Int. J. Remote Sens. 29 (8), 2227-2240.
59. Miller, J.R., Hare, E.W., Wu, J., 1990. Quantitative characterization of the vegetation red edge reflectance 1. An inverted-gaussian reflectance model. Remote Sens. 11 (10), 1755-1773.
60. Miura, T., Huete, A.R., 2009. Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data. Sensors 9 (2), 794-813.
61. Moran, M.S., Inoue, Y., Barnes, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61 (3), 319-346.
62. Moran, J.A., Mitchell, A.K., Goodmanson, G., Stockburger, K.A., 2000. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiol. 20 (16), 1113-1120.
63. MosaicMill Oy, EnsoMOSAIC Image Processing User's Guide. Version 7.3. Mosaic Mill Ltd. Finland, 2009.
64. Moser, G., Serpico, S.B., 2009. Automatic parameter optimization for support vector regression for land and sea surface temperature estimation from remote sensing data. IEEE Trans. Geosci. Remote Sens. 47 (3), 909-921.
65. Murakami, Y., Tsuyama, M., Kobayashi, Y., Kodama, H., Iba, K., 2000. Trienoic fatty acids and plant tolerance of high temperature. Science 287 (5452), 476-479.
66. Myneni, R.B., Maggion, S., Iaquinta, J., Privette, J.L., Gobron, N., Pinty, B., Williams, D.L., 1995. Optical remote sensing of vegetation: modeling, caveats, and algorithms. Remote Sens. Environ. 51 (1), 169-188.
67. Neale, C.M., Crowther, B.G., 1994. An airborne multispectral video/radiometer remote sensing system: development and calibration. Remote Sens. Environ. 49 (3), 187-194.
68. Niinemets, Ü., Tenhunen, J.D., 1997. A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum. Plant Cell Environ. 7, 845-866.
69. Pal, M., Mather, P.M., 2005. Support vector machines for classification in remote sensing. Int. J. Remote Sens. 26 (5), 1007-1011.
70. Parry, C., Blonquist, J., Bugbee, B., 2014. In situ measurement of leaf chlorophyll concentration: analysis of the optical/absolute relationship. Plant Cell Environ.
71. Peñuelas, J., Filella, I., 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci. 3 (4), 151-156.
72. Pierce, F.J., Nowak, P., 1999. Aspects of precision agriculture. Adv. Agron. 67, 1-85.
73. Raison, J.K., Roberts, J.K., Berry, J.A., 1982. Correlations between the thermal stability of chloroplast (thylakoid) membranes and the composition and fluidity of their polar lipids upon acclimation of the higher plant, Nerium oleander, to growth temperature. Biochim. Biophys. Acta 688 (1), 218-228.
74. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C., (1974). Monitoring the vernal advancements and retrogradation of natural vegetation in Nasa, Gsfc final report (ed. MD, UG) p, 1-371.
75. Salvucci, M.E., Crafts-Brandner, S.J., 2004. Inhibition of photosynthesis by heat stress: the activation state of rubisco as a limiting factor in photosynthesis. Physiol. Plant. 120 (2), 179-186.
76. Schellberg, J., Hill, M.J., Gerhards, R., Rothmund, M., Braun, M., 2008. Precision agriculture on grassland: applications, perspectives and constraints. Eur. J. Agron. 29 (2), 59-71.
77. Seelan, S.K., Laguette, S., Casady, G.M., Seielstad, G.A., 2003. Remote sensing applications for precision agriculture: a learning community approach. Remote Sens. Environ. 88 (1), 157-169.
78. Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Major, D.J., 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93 (3), 583-589.
79. Shanahan, J.F., Holland, K.H., Schepers, J.S., Francis, D.D., Schlemmer, M.R., Caldwell, R., 2003. Use of a crop canopy reflectance sensor to assess corn leaf chlorophyll content. Digital Imaging Spectral Techn., 135-150.
80. Sharkey, T.D., 2005. Effects of moderate heat stress on photosynthesis: importance of thylakoid reactions, rubisco deactivation, reactive oxygen species, and thermos tolerance provided by isoprene. Plant Cell Environ. 28 (3), 269-277.
81. Sims, D.A., Gamon, J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81 (2), 337-354.
82. Smith, A.M., Bourgeois, G., Teillet, P.M., Freemantle, J., Nadeau, C., 2008. A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat. Can. J. Remote Sens. 34 (6), 539-548.
83. Souza, C.R., Kernel Functions for Machine Learning Applications, 17 Mar. 2010. Web. http://crsouza.blogspot.in/2010/03/kernel-functions-for-machine-learning.html
84. Stone, M.L., Solie, J.B., Raun, W.R., Whitney, R.W., Taylor, S.L., Ringer, J.D., 1996. Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Trans. ASAE 39 (5), 1623-1631.
85. Swinton, S.M., Lowenberg-DeBoer, J., 1998. Evaluating the profitability of site-specific farming. J. Prod. Agric. 11 (4), 439-446.
86. Thayananthan, A., Navaratnam, R., Stenger, B., Torr, P.H., Cipolla, R., 2008. Pose estimation and tracking using multivariate regression. Pattern Recognit. Lett. 29 (9), 1302-1310.
87. Ticlavilca, A.M., McKee, M., Walker, W.R., 2013. Real-time forecasting of short-term irrigation canal demands using a robust multivariate bayesian learning model. Irrigation Sci. 31 (2), 151-167.
88. Tipping, M.E., 2001. Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211-244.
89. Tipping, M.E., 2004. Bayesian inference: an introduction to principles and practice in machine learning. In: In Advanced lectures on machine Learning, Springer Berlin Heidelberg, pp. 41-62.
90. Tipping, M.E., Faul, A.C., 2003 January. Fast marginal likelihood maximisation for sparse Bayesian models. In Proceedings of the ninth international workshop on artificial intelligence and statistics Vol. 1, No. 3.
91. Torres, A.F., Walker, W.R., McKee, M., 2011. Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agric. Water Manag. 98 (4), 553-562.
92. V., Vapnik, The nature of statistical learning theory, Springer Science & Business Media, 2000.
93. Verrelst, J., Schaepman, M.E., Malenovsky, Z., Clevers, J.G., 2010. Effects of woody elements on simulated canopy reflectance: implications for forest chlorophyll content retrieval. Rem. Sens. Environ. 114 (3), 647-656.
94. Verrelst, J., Alonso, L., Camps-Valls, G., Delegido, J., Moreno, J., 2012. Retrieval of vegetation biophysical parameters using gaussian process techniques. IEEE Trans. Geosci. Remote Sens. 50 (5), 1832-1843.
95. Wallace, L., Lucieer, A., Watson, C., Turner, D., 2012. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 4 (6), 1519-1543.
96. Weis, E., 1981. Reversible heat-inactivation of the calvin cycle: a possible mechanism of the temperature regulation of photosynthesis. Planta 151 (1), 33-39.
97. Wood, C.W., Tracy, P.W., Reeves, D.W., Edmisten, K.L., 1992. Determination of cotton nitrogen status with a handheld chlorophyll meter. J. Plant Nutr. 15 (9), 1435-1448.
98. Yadawa, U.L., 1986. A rapid and nondestructive method to determine chlorophyll in intact leaves. HortScience 21 (6), 1449-1450.
99. Yoder, B.J., Pettigrew-Crosby, R.E., 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sens. Environ. 53 (3), 199-211.
100. Yuan, J., Wang, K., Yu, T., Fang, M., 2007. Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process. Eng. Appl. Artif. Intell. 20 (7), 970-979.
101. Zaman, B., Jensen, A., Clemens, S.R., McKee, M., 2014. Retrieval of spectral reflectance of high resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle. Photogramm. Eng. Remote Sens. 80 (12), 1139-1150.
102. Zarco-Tejada, P.J., Miller, J.R., 1999. Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery. J. Geophys. Res. 27, 27921-27933.
103. Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., Mohammed, G.H., Sampson, P.H., 2001. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 39 (7), 1491-1507.
104. Zarco-Tejada, P.J., Miller, J.R., Morales, A., Berjón, A., Agüera, J., 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens. Environ. 90 (4), 463-476.