Does remote and proximal optical sensing successfully estimate maize variables? A review

European Journal of Agronomy

Volumn 99, Issue , 2018, Pages 37-50

  Corti, Martina ^a   Cavalli, Daniele ^a   Cabassi, Giovanni ^b   Marino Gallina, Pietro ^a   Bechini, Luca ^a  

^a UNIVERSITY OF MILAN   (Italy)

^b ISTITUTO NAZIONALE DELLA NUTRIZIONE   (Italy)

Author keywords

Hyperspectral sensing; Multispectral sensing; Nitrogen; Regression tree; Vegetation index

Indexed keywords

CROP; CROP PRODUCTION; GROWING SEASON; MAIZE; NITROGEN; PRECISION AGRICULTURE; REGRESSION ANALYSIS; REMOTE SENSING; SENSOR; SPECTRAL ANALYSIS; VEGETATION INDEX;

ZEA MAYS;

EID: 85048777158     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2018.06.008     Document Type: Review

References (115)

1. Alchanatis, V., Schmilovitch, Z., Meron, M., In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precis. Agric. 6 (2005), 25–39.
2. Alganci, U., Ozdogan, M., Sertel, E., Ormeci, C., Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs. Field Crops Res. 157 (2014), 8–19.
3. Argenta, G., Silva, P.R.F., da, Sangoi, L., Leaf relative chlorophyll content as an indicator parameter to predict nitrogen fertilization in maize. Ciência Rural 34 (2004), 1379–1387.
4. Bacour, C., Jacquemoud, S., Leroy, M., Hautecø eur, O., Weiss, M., Prévot, L., Bruguier, N., Chauki, H., Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data. Agronomie 22 (2002), 555–565.
5. Báez-González, A.D., Chen, P., Tiscareño-López, M., Srinivasan, R., Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop Sci. 42 (2002), 1943–1949, 10.2135/cropsci2002.1943.
6. Baez-Gonzalez, A.D., Kiniry, J.R., Maas, S.J., Tiscareno, M.L., Macias, C., Mendoza, J.L., Richardson, C.W., Salinas, G., Manjarrez, J.R., others, Large-area maize yield forecasting using leaf area index based yield model. Agron. J. 97 (2005), 418–425.
7. Bagheri, N., Ahmadi, H., Alavipanah, S.K., Omid, M., Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesqui. Agropecu. Bras. 48 (2013), 1394–1401.
8. Baret, F., Buis, S., Estimating canopy characteristics from remote sensing observations: review of methods and associated problems. Advances in Land Remote Sensing, 2008, Springer, 173–201.
9. Baret, F., Houlès, V., Guérif, M., Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J. Exp. Bot. 58 (2007), 869–880, 10.1093/jxb/erl231.
10. Barnes, E.M., Moran, M.S., Pinter, P.J., Clarke, T.R., Multispectral remote sensing and site-specific agriculture: examples of current technology and future possibilities. Precis. Agric., 1996, 845–854.
11. Bauer, M.E., Spectral inputs to crop identification and condition assessment. Proceedings of the IEEE 73 (1985), 1071–1085.
12. Bausch, W.C., Khosla, R., QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precis. Agric. 11 (2010), 274–290.
13. Bausch, W.C., Halvorson, A.D., Cipra, J., Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots. Biosyst. Eng. 101 (2008), 306–315, 10.1016/j.biosystemseng.2008.09.011.
14. Blackmer, T.M., Schepers, J.S., Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. J. Prod. Agric. 8 (1995), 56–60.
15. Blackmer, T.M., Schepers, J.S., Aerial photography to detect nitrogen stress in corn. J. Plant Physiol. 148 (1996), 440–444.
16. Blackmer, T.M., Schepers, J.S., Varvel, G.E., Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron. J. 86 (1994), 934–938.
17. Blackmer, T.M., Schepers, J.S., Varvel, G.E., Walter-Shea, E.A., Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88 (1996), 1–5.
18. Blackmer, T.M., Schepers, J.S., Varvel, G.E., Meyer, G.E., Analysis of aerial photography for nitrogen stress within corn fields. Agron. J. 88 (1996), 729–733.
19. Bouroubi, Y., Tremblay, N., Vigneault, P., Bélec, C., Adamchuk, V., Estimating nitrogen sufficiency index using a natural local reference approach. Agro-Geoinformatics (Agro-Geoinformatics), 2013 Second International Conference On. IEEE, 2013, 71–75.
20. Bragagnolo, J., Amado, T.J.C., Bortolotto, R.P., Use efficiency of variable rate of nitrogen prescribed by optical sensor in corn. Revista Ceres 63 (2016), 103–111.
21. Breiman, L., Friedman, J., Olshen, R., Stone, C., Boca raton. Classification and Regression Trees, 1984, FL: CRC Press.
22. Bu, H., Sharma, L.K., Denton, A., Franzen, D.W., Comparison of satellite imagery and Ground-based active optical sensors as yield predictors in sugar beet, spring wheat, corn, and sunflower. Agron. J. 109 (2017), 299–308, 10.2134/agronj2016.03.0150.
23. Casa, R., Baret, F., Buis, S., Lopez-Lozano, R., Pascucci, S., Palombo, A., Jones, H.G., Estimation of maize canopy properties from remote sensing by inversion of 1-D and 4-D models. Precis. Agric. 11 (2010), 319–334.
24. Casa, R., Castaldi, F., Pascucci, S., Pignatti, S., Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements. J. Agric. Sci. 153 (2015), 876–890, 10.1017/S0021859614000483.
25. Chang, J., Clay, D.E., Dalsted, K., Clay, S., O'Neill, M., Corn (L.) Yield prediction using multispectral and multidate reflectance. Agron. J. 95 (2003), 1447–1453.
26. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., Colombo, R., Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens. 6 (2014), 6549–6565, 10.3390/rs6076549.
27. Clay, D.E., Kim, K.-I., Chang, J., Clay, S.A., Dalsted, K., Characterizing water and nitrogen stress in corn using remote sensing. Agron. J. 98 (2006), 579–587.
28. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., De Colstoun, E.B., McMurtrey, J.E., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74 (2000), 229–239.
29. De'ath, Glenn, Fabricius, Katharina E., Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:11 (2000), 3178–3192.
30. Dellinger, A.E., Schmidt, J.P., Beegle, D.B., Developing nitrogen fertilizer recommendations for corn using an active sensor. Agron. J. 100 (2008), 1546–1552, 10.2134/agronj2007.0386.
31. Diacono, M., Rubino, P., Montemurro, F., Precision nitrogen management of wheat. A review. Agron. Sustain. Dev. 33 (2013), 219–241, 10.1007/s13593-012-0111-z.
32. Diker, K., Bausch, W.C., Radiometric Field measurements of maize for estimating soil and plant nitrogen. Biosyst. Eng. 86 (2003), 411–420, 10.1016/j.biosystemseng.2003.08.016.
33. Doraiswamy, P.C., Hatfield, J.L., Jackson, T.J., Akhmedov, B., Prueger, J., Stern, A., Crop condition and yield simulations using landsat and MODIS. Remote sensing of environment, 2002. Soil. Moist. Exp. (SMEX02) 92 (2004), 548–559, 10.1016/j.rse.2004.05.017.
34. Dorigo, W.A., Zurita-Milla, R., de Wit, A.J., Brazile, J., Singh, R., Schaepman, M.E., A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Observ. Geoinf. 9 (2007), 165–193.
35. Eitel, J.U.H., Long, D.S., Gessler, P.E., Hunt, E.R., Combined spectral index to improve ground-based estimates of nitrogen status in Dryland wheat. Agron. J., 100, 2008, 1694, 10.2134/agronj2007.0362.
36. Elwadie, M.E., Pierce, F.J., Qi, J., Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan. Agron. J. 97 (2005), 99–105.
37. Francis, D.D., Piekielek, W.P., Assessing Crop Nitrogen Needs With Chlorophyll Meters. Site–Specific Management Guide No. SSMG–12. Norcross. 1999, The Potash and Phosphate Institute, Georgia.
38. Freeman, K.W., Girma, K., Arnall, D.B., Mullen, R.W., Martin, K.L., Teal, R.K., Raun, W.R., By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron. J. 99 (2007), 530–536, 10.2134/agronj2006.0135.
39. Gilabert, M.A., Gandía, S., Melia, J., Analyses of spectral-biophysical relationships for a corn canopy. Remote Sens. Environ. 55 (1996), 11–20.
40. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58 (1996), 289–298.
41. Gitelson, A.A., Vina, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J., Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett., 2005, 32.
42. Hansen, P.M., 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 Sens. Environ. 86 (2003), 542–553, 10.1016/S0034-4257(03)00131-7.
43. Hawkins, J.A., Sawyer, J.E., Barker, D.W., Lundvall, J.P., Using relative chlorophyll meter values to determine nitrogen application rates for corn. Agron. J., 99, 2007, 1034, 10.2134/agronj2006.0309.
44. Holland, K.H., Schepers, J.S., Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agron. J. 102 (2010), 1415–1424.
45. Holland, K.H., Schepers, J.S., Use of a virtual-reference concept to interpret active crop canopy sensor data. Precis. Agric. 14 (2013), 71–85.
46. Houborg, R., Anderson, M., Daughtry, C., Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sens. Environ. 113 (2009), 259–274.
47. Houborg, R., McCabe, M., Cescatti, A., Gao, F., Schull, M., Gitelson, A., Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC). Remote Sens. Environ. 159 (2015), 203–221.
48. Hunt, E.R., Doraiswamy, P.C., McMurtrey, J.E., Daughtry, C.S.T., Perry, E.M., Akhmedov, B., A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. Geoinf. 21 (2013), 103–112, 10.1016/j.jag.2012.07.020.
49. Inman, D., Khosla, R., Reich, R.M., Westfall, D.G., Active remote sensing and grain yield in irrigated maize. Precis. Agriculture 8 (2007), 241–252.
50. Jacquemoud, S., Baret, F., PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34 (1990), 75–91.
51. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L., PROSPECT+ SAIL models: a review of use for vegetation characterization. Remote Sens. Environ. 113 (2009), S56–S66.
52. Kim, Y., Reid, J.F., Han, S., On-the-go nitrogen sensing and fertilizer control for site-specific crop management. Intl. J. Agric. Biosystems Engr 7 (2007), 18–26.
53. Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Scharf, P.C., Palm, H.L., Roberts, D.F., Vories, E.D., Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agron. J., 102, 2010, 71, 10.2134/agronj2009.0114.
54. Knipling, E.B., Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1 (1970), 155–159.
55. Koetz, B., Baret, F., Poilvé H., Hill, J., Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens. Environ. 95 (2005), 115–124.
56. Krienke, R., Ferguson, B., Maharjan, Using an unmanned aerial vehicle to evaluate nitrogen variability and distance effect with an active crop canopy sensor. Precision Agriculture’ 15, 2015, Wageningen Academic Publishers, 143–150.
57. Kunapuli, S.S., Rueda-Ayala, V., Benavídez-Gutiérrez, G., Córdova-Cruzatty, A., Cabrera, A., Fernández, C., Maiguashca, J., Yield prediction for precision territorial management in maize using spectral data. Precision Agriculture’15, 2015, Wageningen Academic Publishers, 344–358.
58. Lan, Y., Thomson, S.J., Huang, Y., Hoffmann, W.C., Zhang, H., Current status and future directions of precision aerial application for site-specific crop management in the USA. Comput. Electron. Agric. 74 (2010), 34–38.
59. Lee, W., Searcy, S.W., Kataoka, T., Assessing Nitrogen Stress in Corn Varieties of Varying Color. 1999, Citeseer.
60. Li, Y., Shi, Z., Wu, C., Li, H., Li, F., Determination of potential management zones from soil electrical conductivity, yield and crop data. J. Zhejiang Univ. Sci. B 9 (2008), 68–76, 10.1631/jzus.B071379.
61. Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S.L., Chen, X., Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Res. 157 (2014), 111–123.
62. Lukina, E.V., Freeman, K.W., Wynn, K.J., Thomason, W.E., Mullen, R.W., Stone, M.L., Solie, J.B., Klatt, A.R., Johnson, G.V., Elliott, R.L., Raun, W.R., nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake. J. Plant Nutr. 24 (2001), 885–898, 10.1081/PLN-100103780.
63. Ma, B.L., Morrison, M.J., Dwyer, L.M., Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize. Agron. J. 88 (1996), 915–920.
64. Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., Martínez-Casasnovas, J.A., Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (zea mays l.) from a standard uav service. Remote Sens., 8, 2016, 973.
65. Martin, K.L., Girma, K., Freeman, K.W., Teal, R.K., Tubańa, B., Arnall, D.B., Chung, B., Walsh, O., Solie, J.B., Stone, M.L., Raun, W.R., Expression of variability in corn as influenced by growth stage using optical sensor measurements. Agron. J., 99, 2007, 384, 10.2134/agronj2005.0268.
66. Miao, Y., Mulla, D.J., Randall, G.W., Vetsch, J.A., Vintila, R., Stafford, J.V., others, Predicting chlorophyll meter readings with aerial hyperspectral remote sensing for in-season site-specific nitrogen management of corn. Precision Agriculture’07. Papers Presented at the 6th European Conference on Precision Agriculture, Skiathos, Greece, 3-6 June, 2007, Wageningen Academic Publishers, 2007, 635–641.
67. Miao, Y., Mulla, D.J., Randall, G.W., Vetsch, J.A., Vintila, R., Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precis. Agric. 10 (2009), 45–62.
68. Mistele, B., Schmidhalter, U., Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur. J. Agron. 29 (2008), 184–190.
69. Mulla, D.J., Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst. Eng. 114 (2013), 358–371.
70. Muñoz-Huerta, R., Guevara-Gonzalez, R., Contreras-Medina, L., Torres-Pacheco, I., Prado-Olivarez, J., Ocampo-Velazquez, R., A review of methods for sensing the nitrogen Status in plants: advantages, disadvantages and recent advances. Sensors 13 (2013), 10823–10843, 10.3390/s130810823.
71. Osborne, S.L., Schepers, J.S., Francis, D.D., Schlemmer, M.R., Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen-and water-stressed corn. Crop Sci. 42 (2002), 165–171.
72. Osborne, S.L., Schepers, J.S., Schlemmer, M.R., Using multi-spectral imagery to evaluate corn grown under nitrogen and drought stressed conditions. J. Plant Nutr. 27 (2004), 1917–1929, 10.1081/LPLA-200030042.
73. Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save, R., The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 14 (1993), 1887–1905.
74. Piekielek, W.P., Fox, R.H., Toth, J.D., Macneal, K.E., Use of a chlorophyll meter at the early dent stage of corn to evaluate nitrogen sufficiency. Agronomy J. 87:3 (1995), 403–408.
75. Quemada, M., Gabriel, J.L., Zarco-Tejada, P., Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization. Remote Sens. 6 (2014), 2940–2962.
76. Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E., Lukina, E.V., Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94 (2002), 815–820.
77. Raun, W.R., Solie, J.B., Stone, M.L., Martin, K.L., Freeman, K.W., Mullen, R.W., Zhang, H., Schepers, J.S., Johnson, G.V., Optical sensor-based algorithm for crop nitrogen fertilization. Commun. Soil Sci. Plant Anal. 36 (2005), 2759–2781.
78. Ritchie, S.W., Hanway, J.J., Benson, G.O., How a Corn Plant Develops. Rev. Ed. Spec. Rep. 48. 1996, State Univ. Coop. Ext. Serv., Ames, Iowa.
79. Rorie, R.L., Purcell, L.C., Karcher, D.E., King, C.A., The assessment of leaf nitrogen in corn from digital images. Crop Sci. 51 (2011), 2174–2180, 10.2135/cropsci2010.12.0699.
80. Rorie, R.L., Purcell, L.C., Mozaffari, M., Karcher, D.E., King, C.A., Marsh, M.C., Longer, D.E., Association of “Greenness” in corn with yield and leaf nitrogen concentration. Agron. J., 103, 2011, 529, 10.2134/agronj2010.0296.
81. Sabins, F.F., Remote Sensing: Principles and Applications. 2007, Waveland Press.
82. Samborski, S.M., Tremblay, N., Fallon, E., Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agron. J. 101 (2009), 800–816, 10.2134/agronj2008.0162Rx.
83. Scharf, P.C., Lory, J.A., Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J., 94, 2002, 397, 10.2134/agronj2002.3970.
84. Scharf, P.C., Lory, J.A., Calibrating reflectance measurements to predict optimal sidedress nitrogen rate for corn. Agron. J. 101 (2009), 615–625.
85. Scharf, P.C., Brouder, S.M., Hoeft, R.G., Chlorophyll meter readings can predict nitrogen need and yield response of corn in the North-Central USA. Agron. J., 98, 2006, 655, 10.2134/agronj2005.0070.
86. Schlemmer, M.R., Francis, D.D., Shanahan, J.F., Schepers, J.S., Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron. J. 97 (2005), 106–112.
87. Schut, A.G.T., Ketelaars, J., Lokhorst, C., Imaging Spectroscopy for Grassland Management. 2003, Wageningen Academic Publishers, Wageningen.
88. Scotford, I.M., Miller, P.C.H., Applications of spectral reflectance techniques in Northern European cereal production: a review. Biosyst. Eng. 90 (2005), 235–250, 10.1016/j.biosystemseng.2004.11.010.
89. Senay, G.B., Ward, A.D., Lyon, J.G., Fausey, N.R., Nokes, S.E., Manipulation of high spatial resolution aircraft remote sensing data for use in site-specific farming. Trans. ASAE, 41, 1998, 489.
90. Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Schlemmer, M.R., Major, D.J., Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93 (2001), 583–589, 10.2134/agronj2001.933583x.
91. Shaver, T.M., Khosla, R., Westfall, D.G., Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize. Precis. Agric. 12 (2011), 892–904.
92. Solari, F., Shanahan, J., Ferguson, R., Schepers, J., Gitelson, A., Active sensor reflectance measurements of corn nitrogen status and yield potential. Agron. J., 100, 2008, 571, 10.2134/agronj2007.0244.
93. Solari, F., Shanahan, J.F., Ferguson, R.B., Adamchuk, V.I., An active sensor algorithm for corn nitrogen recommendations based on a chlorophyll meter algorithm. Agron. J. 102 (2010), 1090–1098.
94. Solie, J.B., Monroe, A.D., Raun, W.R., Stone, M.L., Generalized algorithm for variable-rate nitrogen application in cereal grains. Agron. J. 104 (2012), 378–387.
95. Sripada, R.P., Heiniger, R.W., White, J.G., Weisz, R., Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agron. J. 97 (2005), 1443–1451.
96. Sripada, R.P., Heiniger, R.W., White, J.G., Meijer, A.D., Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron. J. 98 (2006), 968–977, 10.2134/agronj2005.0200.
97. Sripada, R.P., Schmidt, J.P., Dellinger, A.E., Beegle, D.B., Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agron. J. 100 (2008), 1553–1561.
98. Sullivan, D.G., Fulton, J.P., Shaw, J.N., Bland, G., Evaluating the sensitivity of an unmanned thermal infrared aerial system to detect water stress in a cotton canopy. Transactions the ASABE 50 (2007), 1955–1962.
99. Teal, R.K., Tubana, B., Girma, K., Freeman, K.W., Arnall, D.B., Walsh, O., Raun, W.R., In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron. J. 98 (2006), 1488–1494, 10.2134/agronj2006.0103.
100. Thenkabail, P.S., Ward, A.D., Lyon, J.G., Landsat-5 thematic mapper models of soybean and corn crop characteristics. Remote Sens. 15 (1994), 49–61.
101. Thomason, W.E., Phillips, S.B., Raymond, F.D., Defining useful limits for spectral reflectance measures in corn. J. Plant Nutr. 30 (2007), 1263–1277, 10.1080/01904160701555176.
102. Tremblay, N., Wang, Z., Ma, B.-L., Belec, C., Vigneault, P., A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precis. Agric. 10 (2009), 145–161.
103. Varco, J.J., Fox, A.A., Raper, T.B., Hubbard, K.J., Development of sensor based detection of crop nitrogen status for utilization in variable rate nitrogen fertilization. Precision Agriculture’13, 2013, Springer, 145–150.
104. Varvel, G.E., Wilhelm, W.W., Shanahan, J.F., Schepers, J.S., An algorithm for corn nitrogen recommendations using a chlorophyll meter based sufficiency index. Agron. J., 99, 2007, 701, 10.2134/agronj2006.0190.
105. Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 16 (1984), 125–141.
106. Verhoef, W., Earth observation modeling based on layer scattering matrices. Remote Sens. Environ. 17 (1985), 165–178.
107. Vuolo, F., Dini, L., D'urso, G., Retrieval of leaf area index from CHRIS/PROBA data: an analysis of the directional and spectral information content. Int. J. Remote Sens. 29 (2008), 5063–5072.
108. Weiss, M., Baret, F., Myneni, R., Pragnère, A., Knyazikhin, Y., Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 20 (2000), 3–22.
109. Winterhalter, L., Mistele, B., Schmidhalter, U., Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies. Field Crops Res. 129 (2012), 14–20.
110. Winterhalter, L., Mistele, B., Schmidhalter, U., Evaluation of active and passive sensor systems in the field to phenotype maize hybrids with high-throughput. Field Crops Res. 154 (2013), 236–245.
111. Wood, C.W., Reeves, D.W., Duffield, R.R., Edmisten, K.L., Field chlorophyll measurements for evaluation of corn nitrogen status. J. Plant Nutr. 15 (1992), 487–500, 10.1080/01904169209364335.
112. Xia, T., Miao, Y., Mi, G., Khosla, R., Wu, D., Shao, H., Xu, X., In-season estimation of spring maize nitrogen status with GreenSeeker active canopy sensor. 2015 Fourth International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Presented at the 2015 Fourth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2015, 390–395, 10.1109/Agro-Geoinformatics.2015.7248155.
113. Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., Mi, G., Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sens., 8, 2016, 605.
114. Xiang, H., Tian, L., Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst. Eng. 108 (2011), 174–190.
115. Zaman-Allah, M., Vergara, O., Araus, J.L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Albà A.H., Das, B., Craufurd, P., Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 2015, 35.