Evaluating Corn Nitrogen Variability via Remote-Sensed Data

References

• Wood, C.W., Reeves, D.W., Duffield, R.R., and Edmisten, K.L., 1993. Field chlorophyll measurements for the evaluation of corn nitrogen status, J. Plant Nutr. 15 (1993), pp. 487–500.
   Web of Science ®Google Scholar
• Gausman, H.W., and Allen, W.A., 1973. Optical properties of leaves of 30 plant species, Plant Physiol. 52 (1973), pp. 57–62.
   PubMed Web of Science ®Google Scholar
• Hatfield, J.L., and Pinter, P.J., 1993. Remote sensing for crop protection, Crop Protection 12 (6) (1993), pp. 403–412, [CROSSREF].
   Web of Science ®Google Scholar
• Carlson, R.E., Yarger, D.N., and Shaw, R.H., 1971. Factors affecting the spectral properties of leaves with special emphasis on leaf water status, Agron. J. 63 (1971), pp. 486–489.
   Web of Science ®Google Scholar
• Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ. 8 (1979), pp. 127–150, [CROSSREF].
   Web of Science ®Google Scholar
• Lillesand, T.M., and Kieffer, R.W., 1994. Remote Sensing and Image Interpretation, . New York: Wiley and Sons; 1994.
   Google Scholar
• Osborne, S.L., Schepers, J.S., Francis, D.D., and Schlemmer, M.R., 2002. Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen and water-stressed corn, Crop Sci. 42 (2002), pp. 165–171, [PUBMED][INFOTRIEVE].
   PubMed Web of Science ®Google Scholar
• Bausch, W.C., Duke, H.R., and Heermann, D.F., Presented at First European Conference on Precision Agriculture. Oxford, UK, 7–10, September, 1997.
   Google Scholar
• Tyson, T.W., and Curtis, L.M., 2000. MOISCOT—Moisture Monitoring and Irrigation Scheduling for Cotton: A Microsoft EXCEL 97 Spreadsheet. Auburn, AL: Alabama Cooperative Extension System; 2000.
   Google Scholar
• Teem, D.H., 1986. Procedures Used for Soil and Plant Analysis by the Auburn University Soil Testing Laboratory. Dept. Series No. 106. Auburn, AL: Department of Agronomy and Soil, Alabama Agricultural Experiment Station; 1986.
   Google Scholar
• Birk, J.B., 1992. Commercial applications multispectral sensor system: small satellite, Technol. Appl. II 169 (1992), pp. 49–59.
   Google Scholar
• Rickman, D.L., Schiller, S., and Luvall, C.J., 2000. Physics for the Correction of a Calibrated Airborne Scanner, Visible to Thermal Bands: Workshop on Multi/hyperspectral Sensors, Measurements, Modeling and Simulation. Redstone Arsenal, AL: U.S. Army Aviation and Missile Command; 2000.
   Google Scholar
• Schiller, S., and Luvall, C.J., 2000. Description and Performance Demonstration of the Portable Ground-based Atmospheric Monitoring System (PGAMS) for Optimal Radiative Transfer Modeling, Atmospheric Correction and Vicarious Sensor Calibration, Modeling and Simulation. Redstone Arsenal, AL: U.S. Army Aviation and Missile Command; 2000.
   Google Scholar
• Campbell, C.R., 1992. Plant Analysis and Reference Procedures for the Southern Region of the United States. Athens, GA: The Georgia Agricultural Experiment Stations College of Agricultural and Environmental Sciences; 1992, Southern Coop. Series Bull. 368.
   Google Scholar
• Hanlon, E.A., 1992. Plant Analysis and Reference Procedures for the Southern Region of the United States. Athens, GA: The Georgia Agricultural Experiment Stations College of Agricultural and Environmental Sciences; 1992, Southern Coop. Series Bull. 368.
   Google Scholar
• Isaac, R.A., and Johnson, W.C., 1992. Plant Analysis and Reference Procedures for the Southern Region of the United States. Athens, GA: The Georgia Agricultural Experiment Stations College of Agricultural and Environmental Sciences; 1992, Southern Coop. Series Bull. 368.
   Google Scholar
• Walburg, G.M., Bauer, E., Daughtry, C.S.T., and Housley, T.L., 1982. Effects of Nitrogen Nutrition on Growth, Yield, and Reflectance Characteristics of Corn Canopies, Agron. J. 74 (1982), pp. 677–683.
   Web of Science ®Google Scholar
• Hinzman, L.D., Bauer, M.E., and Daughtry, C.S.T., 1986. Effects of nitrogen fertilization on growth and reflectance characteristics of wheat, Remote Sens. Environ. 19 (1986), pp. 47–61, [CROSSREF].
   Web of Science ®Google Scholar
• Takebe, M., Yoneyama, K., Inada, K., and Murakami, T., 1990. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status, Plant Soil 122 (1990), pp. 295–297.
   Web of Science ®Google Scholar
• Al-Abbas, A.H., Barr, H.R., Hall, J.D., Crane, F.L., and Baumgardner, M.F., 1974. Spectra of normal and nutrient deficient maize leaves, Agron. J. 66 (1974), pp. 16–20.
   Web of Science ®Google Scholar
• Strachan, I.B., Pattey, E., and Boisvert, J.B., 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance, Remote Sens. Environ. 80 (2002), pp. 213–224, [CROSSREF].
   Web of Science ®Google Scholar
• Colwell, J.E., 1974. Vegetation canopy reflectance, Remote Sens. Environ. 3 (1974), pp. 175–183, [CROSSREF].
   Google Scholar
• Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.N., Tringe, J.M., Schlemmer, M.R., and Major, D.J., 2001. Use of remote sensing imagery to estimate corn grain yield, Agron. J. 93 (2001), pp. 583–589.
   Web of Science ®Google Scholar
• Bausch, W.C., and Diker, K., 2001. Innovative remote sensing techniques to increase N use efficiency of corn, Commun. Soil Sci. Plant Anal. 32 (2001), pp. 1371–1390, [CROSSREF].
   Web of Science ®Google Scholar
• Yang, C., Bradford, J.M., and Wiegand, C.L., 2001. Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum and corn, Am. Soc. Agric. Engineers 44 (2001), pp. 1983–1994.
   Google Scholar