Crop stress

Encyclopedia of Earth Sciences Series

Volumn , Issue , 2014, Pages 88-91

  Moran, Susan ^a  

^a SOUTHWEST WATERSHED RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84926386206     PISSN: 13884360     EISSN: 1871756X     Source Type: Book Series    
DOI: 10.1007/978-0-387-36699-9_23     Document Type: Chapter

References (37)

1. Baez, A. D., Kiniry, J., Maas, S. J., Tiscereno, M., Macias, J., Mendoza, J., Richardson, C., Salinas, G. J., and Manjarrez, J. R., 2005. Large-area maize yield forecasting using leaf area index based yield model. Agronomy Journal, 97(2), 418–425.
2. Brown, R. B., Steckler, J.-P. G. A., and Anderson, G. W., 1994. Remote sensing for identification of weeds in no-till corn. Transactions of ASAE, 37, 297–302.
3. Burke, J. J., Mahan, J. R., and Hatfield, J. L., 1988. Crop-specific thermal kinetic windows in relation to wheat and cotton biomass production. Agronomy Journal, 80, 553–556.
4. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., deColstoun, E. B., and McMurtrey, J. E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.
5. Demotes-Mainard, S., Boumaza, R., Meyer, S., and Cerovic, Z. G., 2008. Indicators of nitrogen status for ornamental woody plants based on optical measurements of leaf epidermal polyphenol and chlorophyll contents. Scientia Horticulturae, 115, 377–385.
6. Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2000. Assessing spider mite damage in cotton using multispectral remote sensing. In Proceedings of the Beltwide Cotton Conference, San Antonio, TX, Vol. 2, pp. 1342–1345.
7. Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2001. Spying on spider mites from on high. California-Arizona-Texas Cotton Magazine. Fall, 2001, 13–15.
8. Fitzgerald, G. J., Maas, S. J., Pinter, P. J., Hunsaker, D. J., and Clarke, T. R., 2002. Spider mite damage mapping and cotton canopy biophysical characterization using spectral mixture analysis. In Proceedings Symposium on American-Taiwan Agricultural Cooperative Projects, Taipei, Taiwan, November 15, 2005.
9. Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2004. Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Precision Agriculture, 5, 279–289.
10. Gitelson, A. A., Keydan, G. P., and Merzlyak, M. N., 2006. Three-band model for noninvasive estimation of chlorophyll, caroten-oids and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33, 111402, doi:10.1029/2006G1026457.
11. Austin, R. W., 1980. Gulf of Mexico, ocean-color surface-truth measurements. Boundary-Layer Meteorology, 18(3), 269–285.
12. Hatfield, J. L., Prueger, J. H., Kustas, W., Hatfield, J. L., Prueger, J. H., and Kustas, W. P., 2004. In Ustin, S. (ed.), Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing, 3rd edn. New York: Wiley, Vol. 4, pp. 531–568. Chap. 10.
13. Hatfield, J. L., Gitelson, A. A., Schepers, J. S., Schepers, J. S., and Walthall, C. L., 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, S-117–S-131.
14. Jackson, R. D., Idso, S. B., Reginato, R. J., and Pinter, P. J., Jr., 1981. Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138.
15. Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doraiswamy, P., and Hunt, E. R., 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92, 475–482.
16. Ko, J., Maas, S. J., Lascano, R. J., and Wanjura, D., 2005. Modification of the GRAMI model for cotton. Agronomy Journal, 97(5), 1374–1379.
17. Ko, J., Maas, S. J., Mauget, S., Piccinni, G., and Wanjura, D., 2006. Modeling water-stressed cotton growth using within-season remote sensing data. Agronomy Journal, 98, 1600–1609.
18. LaCapra, V. C., Melack, J. M., Gastil, M., and Valeriano, D., 1996. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry. Remote Sensing of Environment, 55, 50–58.
19. Liu, C., Muchhal, U. S., and Raghothama, K. G., 1997. Differential expression of TPS-11, a phosphate starvation-induced gene in tomato. Plant Molecular Biology, 33, 867–874.
20. Maas, S. J., 1998. Remote sensing: value in a bird’s-eye view. In Wrona, A. F. (ed.), Cotton Physiology Today, 9th edn. Memphis: National Cotton Council, Vol. 2, pp. 13–15.
21. Maas, S. J., 2003. Irrigation scheduling by remote sensing technologies. In Stewart, B. A., and Howell, T. A. (eds.), Encyclopedia of Water Science. New York: Marcel Dekker, pp. 523–527.
22. Maas, S. J., 2005. An overview of interfacing models with remote sensing. In Proceedings of the 35th Biological Systems Simulation Conference. Phoenix, AZ, pp. 31–33.
23. Maas, S. J., and Rajan, N., 2008. Estimating ground cover of field crops using medium-resolution multispectral satellite imagery. Agronomy Journal, 100(2), 320–327.
24. Austin, R. W., 1980. Gulf of Mexico, ocean-color surface-truth measurements. Boundary-Layer Meteorology, 18(3), 269–285.
25. Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E., 2000. Using remote sensing to detect weed infestations in Glycine max. Weed Science, 48, 393–398.
26. Meyer, S., Cerovic, Z. G., Goulas, Y., Montpied, P., Demotes-Mainard, S., Bidel, L. P. R., Moya, I., and Dreyer, E., 2006. Relationships between optically assessed polyphenols and chlorophyll contents, and leaf mass per area ratio in woody plants: a signature of the carbon-nitrogen balance within leaves? Plant, Cell & Environment, 29, 1338–1348.
27. Moran, M. S., Maas, S. J., Vanderbilt, V. C., Barnes, E. M., Miller, S. N., and Clarke, T. R., 2004. Application of image-based remote sensing to irrigated agriculture. In Ustin, S. (ed.), Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing, 3rd edn. New York: Wiley, Vol. 4, pp. 617–676. Chap. 12.
28. Peñuelas, J., and Filella, I., 1998. Visible and near infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4), 151–156.
29. Pérez, A. J., López, F., Benlloch, J. V., and Christensen, S., 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture, 25, 197–212.
30. Rosenthal, W., Arkin, G., Shouse, P., and Jordan, W., 1987. Water deficit effects on transpiration and leaf growth. Agronomy Journal, 79, 1019–1026.
31. Schepers, J. S., Blackmer, T. M., Wilhelm, W. W., and Resende, M., 1996. Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. Journal of Plant Physiology, 148, 523–529.
32. Sikkema, P. H., and Dekker, J., 1987. Use of infrared thermometry in determining critical stress periods induced by quackgrass (Agropyron repens) in Soybeans (Glycine max). Weed Science, 35, 784–791.
33. Thomas, J. R., and Oerther, G. F., 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64, 11–13.
34. Tremblay, N., Wang, Z., and Belec, C., 2007. Evaluation of the Dualex for the assessment of corn nitrogen status. Journal of Plant Nutrition, 30, 1355–1369.
35. Turner, N. C., 1977. Drought resistance and adaptation to water deficits in crop plants. In Mussell, H., and Staples, R. C. (eds.), Stress Physiology in Crop Plants. New York: Wiley, pp. 344–372.
36. Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G., 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Science, 40, 554–557.
37. Willers, J. L., Seal, M. R., and Luttrell, R. G., 1999. Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in mid-South cotton. Journal of Cotton Science, 3, 160–170.