Erratum: Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service [Remote Sens., 9, (2017) (973)] DOI: 10.3390/rs8120973;Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (zea mays l.) from a standard uav service

Remote Sensing

Volumn 8, Issue 12, 2016, Pages

  Maresma, Ángel ^a   Ariza, Mar ^a   Martínez, Elías ^a   Lloveras, Jaume ^a   Martínez Casasnovas, José A ^a  

^a UNIVERSITY OF LLEIDA   (Spain)

Author keywords

Crop height; Maize; Multispectral vegetation indices; Nitrogen; UAV

Indexed keywords

ANTENNAS; COMMERCIAL VEHICLES; CROPS; GRAIN (AGRICULTURAL PRODUCT); NITROGEN; NITROGEN FERTILIZERS; UNMANNED AERIAL VEHICLES (UAV);

CROP HEIGHT; EXPERIMENTAL PLOTS; INORGANIC-ORGANIC; MAIZE; MAIZE (ZEA MAYS L.); SPECTRAL INDICES; VEGETATION INDEX; WIDE DYNAMIC RANGE;

VEGETATION;

EID: 85002557571     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10030368     Document Type: Erratum

References (45)

1. Cardwell, V.B. Fifty years of Minnesota corn production: Sources of yield increase. Agron. J. 1982, 74, 984-990.
2. Stanger, T.F.; Lauer, J.G. Corn grain yield response to crop rotation and nitrogen over 35 years. Agron. J. 2008, 100, 543-650.
3. Berenguer, P.; Santiveri, F.; Boixadera, J.; Lloveras, J. Fertilisation of irrigated maize with pig slurry combined with mineral nitrogen. Eur. J. Agron. 2008, 28, 635-645.
4. Sawyer, J.; Nafziger, E.; Randall, G.; Bundy, L.; Rehm, G.; Joern, G. Concepts and Rationale for Regional Nitrogen Rate Guidelines for Corn. Iowa State University, University Extension, 2006. Available online: https://store.extension.iastate.edu/Product/pm2015-pdf (accessed on 5 March 2016).
5. Hanway, J. Corn growth and composition in relation to soil fertility. II Uptake of N, P, and K, and their distribution in different plant parts during the growing season. Agron. J. 1962, 54, 217-222.
6. Ritchie, S.W.; Hanway, J.J.; Benson, G.O. How a Corn Plant Develops; Special Report No. 48; Iowa State University Cooperative Extension Service: Ames, IA, USA, 1997.
7. Crozier, C.R. Fertilizer and lime management. In North Carolina Corn Production Guide; North Carolina State University: Raleigh, NC, USA, 2000.
8. Bausch, W.C.; Duke, H.R. Remote sensing of plant nitrogen status in corn. Am. Soc. Agric. Biol. Eng. 1996, 39, 1869-1875.
9. Bausch, W.C.; Khosla, R. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precis. Agric. 2010, 11, 274-290.
10. Bagheri, N.; Ahmadi, H.; Alavipanah, S.K.; Omid, M. Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesqui. Agropecu. Bras. 2013, 48, 1394-1401.
11. 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. 2014, 6, 2940-2962.
12. 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. 2007, 99, 1034-1040.
13. Blackmer, T.M.; Schepers, J.S. Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. J. Prod. Agric. 1995, 8, 56-60.
14. Scharf, P.C.; Lory, J.A. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 2002, 94, 397-404.
15. 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. 1996, 88, 1-5.
16. 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 Sen. 2014, 6, 6549-6565.
17. Isla, R.; Quílez, D.; Valentín, F.; Casterad, M.A.; Aibar, J.; Maturano, M. Utilización de imágenes aéreas multiespectrales para evaluar la disponibilidad de nitrógeno en maíz (Use of multispectral airbone images to assess nitrogen availability in maize). In Teledetección, Bosques y Cambio Climático; Recondo, C., Pendás, E., Eds.; Asociación Española de Teledetección: Mieres, Spain, 2011; pp. 9-12.
18. 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. 2008, 100, 1553-1561.
19. 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. 2009, 10, 45-62.
20. 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. 2006, 98, 968-977.
21. 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. 2008, 101, 306-315.
22. Xiang, H.; Tian, L. An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring. Comput. Electron. Agric. 2011, 78, 1-8.
23. Berni, J.A.J.; Zarco-Tejada, P.J.; Suarez, L.; Gonzalez-Dugo, V.; Fereres, E. Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2009, 38, 6.
24. Papadopoulos, A.; Iatrou, M.; Papadopoulos, F.; Metaxa, I.; Theodoridou, S.; Kalogeropoulus, K.; Kiparissi, S. Preliminary results for standardization of NDVI using soil nitrates in corn growing. Fresen. Environ. Bull. 2014, 23, 348-354.
25. McMurtrey, J.E.; Chappelle, E.W.; Kim, M.S.; Meisinger, J.J.; Corp, L.A. Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sens. Environ. 1994, 47, 36-44.
26. Gasparotto, A.C.; Nanni, M.R.; da Silva Junior, C.A.; Cesar, E.; Romagnoli, F.; da Silva, A.A.; Guirado, G.C. Using GNIR and RNIR extracted by digital images to detect different levels of nitrogen in corn. J. Agron. 2015, 14, 62-71.
27. Schlemmer, M.; Gitelson, A.; Schepers, J.; Ferguson, R.; Peng, Y.; Shanahan, J.; Rundquist, D. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. Int. J. Appl. Earth Obs. 2013, 25, 47-54.
28. 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 Crop Res. 2014, 157, 111-123.
29. Gitelson, A.A.; Kaufman, J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289-298.
30. Soil Survey Staff. Keys to Soil Taxonomy. In Proceedings of the 12th ed. USDA-Natural Resources Conservation Service, Washington, DC, USA, 15 May 2014.
31. Biau, A.; Santiveri, F.; Mijangos, I.; Lloveras, J. The impact of organic and mineral fertilizers on soil quality parameters and the productivity of irrigated maize crops in semiarid regions. Eur. J. Soil Biol. 2012, 53, 56-61.
32. Rouse, J.W., Jr.; Haas, R.H.; Deering, D.W.; Schell, J.A.; Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (GreenWave Effect) of Natural Vegetation; NASA/GSFC Type III Final Report; NASA: Greenbelt, MD, USA, 1974.
33. Gitelson, A.A. Wide Dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 2004, 161, 165-173.
34. Duncan, W.G.;Williams, W.A.; Loomis, R.S. Tassels and the productivity of maize. Crop Sci. 1967, 7, 37-39.
35. Bonneville, M.C.; Fyles, J.W. Assessing variations in SPAD-502 chlorophyll meter measurements and their relationships with nutrient content of trembling aspen foliage. Commun. Soil Sci. Plant Anal. 2006, 37, 525-539.
36. Cerrato, M.E.; Blackmer, A.M. Comparison of models for describing; corn yield response to nitrogen fertilizer. Agron. J. 1990, 82, 138-143.
37. Welch, L.F.; Mulvaney, D.L.; Odham, M.G.; Boone, L.V.; Pendleton, J.W. Corn yields with fall, spring, and sidedress nitrogen. Agron. J. 1971, 63, 119-123.
38. Magdoff, F.R.; Ross, D.; Amadon, J. A soil test for nitrogen availability to corn. Soil Sci. Soc. Am. J. 1984, 48, 1301-1304.
39. Cooper, J.E. Nitrification in soils incubated with pig slurry. Soil Biol. Biochem. 1975, 7, 119-124.
40. Rochette, P.; van Bochove, E.; Prévost, D.; Angers, D.A.; Côté, D.; Bertrand, N. Soil carbon and nitrogen dynamics following applications of pig slurry for the 19th consecutive year: II. Nitrous oxide fluxes and mineral nitrogen. Soil Sci. Soc. Am. J. 2000, 64, 1396-1403.
41. Yin, X.; McClure, M.A.; Jaja, N.; Tyler, D.D.; Hayes, R.M. In-season prediction of corn yield using plant height under major production systems. Agron. J. 2011, 103, 923-929.
42. Sakamoto, T.; Gitelson, A.A.; Arkebauer, T.J. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sen. Environ. 2013, 131, 215-231.
43. Sakamoto, T.; Gitelson, A.A.; Arkebauer, T.J. Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sen. Environ. 2014, 147, 19-231.
44. Wang, M.; Tao, F.; Shi, W. Corn yield forecasting in northeast china using remotely sensed spectral indices and crop phenology metrics. J. Integr. Agric. 2014, 13, 1538-1545.
45. Gitelson, A.A.;Wardlow, B.D.; Keydan, G.P.; Leavitt, B. An evaluation of MODIS 250-m data for green LAI estimation in crops. Geophys. Res. Lett. 2007.