Relationships between remotely sensed reflectance data and cotton growth and yield

Transactions of the American Society of Agricultural Engineers

Volumn 43, Issue 3, 2000, Pages 535-546

  Plant, R E ^a,b   Munk, D S ^c   Roberts, B R ^c   Vargas, R L ^c   Rains, D W ^b   Travis, R L ^b   Hutmacher, R B ^b  

^a ASAE ^*  (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA COOPERATIVE EXTENSION   (United States)

Author keywords

Cotton; Electromagnetic reflectance; NDVI; Remote sensing; Sensors; Vegetation indices

Indexed keywords

NITROGEN STRESS; NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI); WATER STRESS;

CULTIVATION; ELECTROMAGNETIC WAVE REFLECTION; NITROGEN; REMOTE SENSING; STRESS ANALYSIS; VEGETATION; WATER;

COTTON;

COTTON; GROWTH; REMOTE SENSING; YIELD;

GOSSYPIUM HIRSUTUM;

EID: 0034190698     PISSN: 00012351     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. Ball, S. T., and B. E. Frazier. 1993. Evaluating the association between wheat yield and remotely-sensed data. Cereal Res. Commun. 21(2-3): 213-219.
2. Bausch, W. C., and H. R. Duke. 1996. Remote sensing of plant nitrogen status in com. Transactions of the ASAE 39(5): 1869-1875.
3. Bausch, W. C., and C. M. U. Neale. 1987. Crop coefficients derived from reflected canopy radiation: A concept. Transactions of the ASAE 30(3): 703-709.
4. _. 1989. Spectral inputs improve com crop coefficients and irrigation scheduling. Transactions of the ASAE 32(6): 1901-1908.
5. Benedetti, R., and P. Rossini. 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
6. Bourland, F. M., D. M. Oosterhuis, and N. P. Tugwell. 1992. Concept for monitoring the growth and development of cotton plants using main-stem node counts. J. Prod. Agric. 5(4): 532-538.
7. Campbell, G. S., and J. M. Norman. 1990. Estimation of plant water status from canopy temperature: An analysis of the inverse problem. In Applications of Remote Sensing in Agriculture, eds. M. D. Steven, and J. A. Clark, 255-271. London, England: Butterworths.
8. Denison, R. F., R. O. Miller, D. Bryant, A. Abshahi, and W. E. Wildman. 1996. Image processing extracts more information from color infrared aerial photographs. California Agric. 50(3): 9-13.
9. Egan, W. C. 1985. Photometry and Polarization in Remote Sensing. New York, N.Y.: Elsevier.
10. Gardener, B. R., D. C. Nielsen, and C. C. Shock. 1992. Infrared thermometry and the crop water stress index. II. Sampling procedures and interpretation. J. Prod. Agric. 5(4): 466-476.
11. Hake, S. J., T. A. Kerby, and K. D. Hake. 1996. Cotton Production Manual. Oakland, Calif.: Division of Agriculture and Natural Resources, University of California.
12. Idso, S. B., R. D. Jackson, P. J. Pinter Jr., R. J. Reginato, and J. L. Hatfield. 1981. Normalizing the stress degree-day parameter for environmental variability. Agric. Meteorol. 24(1): 45-56.
13. Jackson, R. D., and C. E. Ezra. 1985. Spectral response of cotton to suddenly induced water stress. Int. J. Remote Sens. 6(1): 177-185.
14. Jenkins, J. N., and J. C. McCarty. 1995. End of season plant maps. Bulletin 1024. Mississippi State, Miss.: Mississippi Agricultural and Forestry Experiment Station.
15. Kerby, T. A., and K. Hake. 1996. Monitoring cotton's growth. In Cotton Production, eds. S. J. Hake, T. A. Kerby, and K. D. Hake, 335-355. Publication 3352. Oakland, Calif.: Division of Agriculture and Natural Resources, University of California.
16. Lillesand, T. M., and R. W. Kiefer. 1994. Remote Sensing and Image Interpretation. New York, N.Y.: John Wiley.
17. Maas, S. J. 1998. Estimating cotton canopy ground cover from remotely sensed scene reflectance. Agron. J. 90(3): 384-388.
18. Moran, M. S., Y. Inoue, and E. M. Barnes. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61(3): 319-346.
19. Muller, E., and M. James. 1994. Seasonal variation and stability of soil spectral patterns in fluvial landscape. Int. J. Remote Sens. 15(9): 1885-1900.
20. Nemani, R. R., and S. W. Running. 1989. Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. J. Appl. Meteor. 28(4): 276-284.
21. Paltridge, G. W., and J. Barber. 1988. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sens. Environ. 25(3): 381-394.
22. Perry, J. R., and L. F. Lautenschlager. 1984. Functional equivalence of spectral vegetation indices. Remote Sens. Environ. 14(1): 169-182.
23. Pettigrove, G. S., S. K. Upadhyaya, M. G. Pelletier, T. K. Hartz, R. E. Plant, and R. F. Denison. 1999. Tomato yield - color infrared photograph relationships. In Proc. 4th Int. Conf. Precision Agriculture, 1483-1492. Madison, Wis.: Am. Soc. Agronomy.
24. Plant, R. E., and M. Keeley. 1999. Relationships among plant growth indices in Acala cotton. J. Prod. Agric. 12(1): 61-68.
25. Plant, R. E., A. Mermer, G. S. Pettygrove, M. P. Vayssieres, J. A. Young, R. O. Miller, L. F. Jackson, R. F. Denison, and K. Phelps. 1999. Factors underlying grain yield spatial variability in three irrigated wheat fields. Transactions of the ASAE 42(5): 1187-1202.
26. Plant, R. E., and D. S. Munk. 1999a. Application of remote sensing to irrigation management in California cotton: Interpretation of vegetation indices. In Proc. 4th Int. Conf. Precision Agriculture, eds. P. C. Robert, R. H. Rust, and W. E. Larson, 1511-1522. Madison, Wis.: Am. Soc. Agronomy.
27. _. 1999b. Using infrared aerial photography to help plan defoliation. California Cotton Review 52: 3-5.
28. Senay, G. B., A. D. Ward, J. G. Lyon, N. R. Fausey, and S. E. Nokes. 1998. Manipulation of high spatial resolution aircraft remote sensing data for use in site-specific farming. Transactions of the ASAE 41(2): 489-495.
29. Silvertooth, J. C., A. F. Wrona, D. S. Guthrie, K. Hake, T. A. Kerby, and J. A. Landivar. 1996. Vigor indices for cotton management. Cotton Physiol. Today 7(3): 1-4.
30. Takebe, M., T. Yoneyama, K. Inada, and T. Murakami. 1990. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant & Soil 122(2): 295-297.
31. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2): 127-150.
32. Wiegand, C. L., and A. J. Richardson. 1987. Spectral components analysis: Rationale and results for three crops. Int. J. Remote Sens. 8(7): 1011-1032.
33. Wiegand, C. L., A. J. Richardson, D. E. Escobar, and A. H. Gerbermann. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35(1): 105-119.
34. Yuzhu, L. 1990. Estimating production of winter wheat by remote sensing and unified ground network. II. Nationwide estimation of wheat yields. In Applications of Remote Sensing in Agriculture, eds. M. D. Steven, and J. A. Clark, 149-158. London, UK: Butterworths.