Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Precision Agriculture

Volumn 4, Issue 4, 2003, Pages 359-384

  Leon, Chris T ^a,c   Shaw, David R ^a   Cox, Michael S ^a   Abshire, Melinda J ^a   Ward, Brian ^a   Wardlaw III, Milton C ^a   Watson, Clarence ^b  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

^c LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

Global positioning systems (GPS)geographic information systems (GIS)remote sensingsite specific applicationspectral radiancevegetation indices

Indexed keywords

CROP YIELD; GROWTH RATE; NDVI; REMOTE SENSING; SOIL PROPERTY;

GLYCINE MAX; GOSSYPIUM HIRSUTUM;

EID: 0345526487     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026387830942     Document Type: Article

References (60)

1. Addiscott, T. M. and Wagenet, R. J. 1985. A simple method for combining soil properties that show variability. Soil Science Society of America Journal 49, 1365-1369.
2. Baret, F., Guyot, G. and Major, D. J. 1989. TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Proceedings of the 12th Canadian Symposium on Remote Sensing (IGARR'90. Vancouver, BC, Canada, 10-14 July) 3, 1355-1358.
3. Blazquez, C. H. and Edwards, G. J. 1986. Spectral reflectance of healthy and diseased watermelon leaves. Annals of Applied Biology 108, 243-249.
4. Bowerman, B. L. and O'Connell, R. T. 1990. Linear Statistical Models. An Applied Approach, 2nd edn. (PWS-Kent Publishing Co., Boston, MA).
5. Bowers, S. A. and Hanks, R. J. 1965. Reflection of radiant energy from soils. Soil Science 100, 130-138.
6. Bullock, P., Brisco, B. and Hirose, T. 2000. Remote sensing for improving crop management. In: Proceedings of the 2nd International Conference on Geospatial Info. in Agriculture and Forestry (Lake Buena Vista, FL, Jan. 10-12), pp. II-487-II-494.
7. Buscaglia, H. J. and Varco, J. J. 1999. Utilization of spectral reflectance to evaluate N and K nutrition in cotton. 1999. In: Agronomy Abstract (ASA/CSSA/SSSA, Madison, WI), p. 237.
8. Campanella, R. and Seal, M. R. 2000. Patterns among seeding rates, normalized difference vegetation index, and yield on a Mississippi cotton farm. In: Proceedings of 2nd International Conference on Geospatial Info. in Agriculture and Forestry (Lake Buena Vista, FL, Jan. 10-12), pp. I-232-I-239.
9. Carlson, T. N. and Ripley, D. A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing Environment 62, 241-252.
10. 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. Agronomy Journal 63, 486-489.
11. Colwell, J. E. 1974. Vegetation canopy reflectance. Remote Sensing of Environment 3, 175-183.
12. Crist, E. P. 1984. Effects of cultural and environmental factors on corn and soybean spectral development patterns. Remote Sensing of Environment 14, 3-13.
13. Dupont, J. K., Campanella, R. Seal, M. R., Willers, J. L. and Hood, K. B. 2000. Spatially variable insecticide applications through remote sensing. In: Proceedings of the Beltwide Cotton Conference (San Antonio, TX, Jan. 4-8), pp. 426-429.
14. Elvidge, C. D. and Chen, Z. 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment 54, 38-48.
15. Fitzgerald, G. J., Maas, S. J. and DeTar, W. R. 2000. Early detection of spider mites in cotton using multispectral remote sensing. In: Proceedings of the Beltwide Cotton Conference (San Antonio, TX, Jan. 4-8), pp. 1022-1024.
16. Forcella, F. 1993. Value of managing within-field variability. In: Soil Specific Crop Management (ASA/CSSA/SSSA, Madison, WI), pp. 125-131.
17. Hart, W. G. and Myers, V. I. 1968. Infrared aerial color photography for the detection of population of brown soft scale on citrus groves. Journal of Economic Entomology 61, 617-624.
18. Heald, C. M., Thames, W. H. and Wiegand, C. L. 1972. Detection of Rotylenchus reniformis infestations by aerial infrared photography. Journal of Nematology 4, 298-300.
19. Hope, J. R. 1966. Path of heavy rainfall photographed from space. Bulletin of American Meteorological Society 47, 371-373.
20. Huete, A. R. 1987. Soil-dependent spectral response in a developing plant canopy. Agronomy Journal 79, 61-68.
21. Huete, A. R. 1988. A soil adjusted vegetation index (SAVI). Remote Sensing of Environment 17, 37-53.
22. Huete, A. R., Jackson, R. D. and Post, D. F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment 17, 37-53.
23. Idso, B., Pinter, P. J. Jr., Jackson, R. D. and Reginato, R. J. 1980. Estimation of grain yields by remote sensing of crop senescence rates. Remote Sensing of Environment 9, 87-91.
24. Jackson, R. D., Jones, C. A., Uehara, G. and Santo, L. T. 1980. Remote detection of nutrient and water deficiency in sugarcane under variable cloudiness. Remote Sensing of Environment 11, 327-331.
25. Jackson, R. D., Slater, P. N. and Pinter, P. J. Jr., 1983. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmosphere. Remote Sensing of Environment 13, 187-208.
26. Jordan, C. F. 1969. Derivation of leaf area index form quality of light on the forest floor. Ecology 50, 663-666.
27. Lough, J. L. 2000. Effects of varying nitrogen and potassium nutrition on spectral reflectance and physiological properties of cotton. M.S. Thesis., Mississippi State University, Mississippi State.
28. Major, D. J., Baret, F. and Guyot, G. 1990. A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing 11, 727-740.
29. Mallarino, A. P., Oyarzabal, E. S. and Hinz, F. N. 1999. Interpreting within-field relationships between crop yields and plant variables using factor analysis. Precision Agriculture 1, 15-25.
30. Manzer, F. E. and Cooper, G. R. 1967. Aerial photographic methods of potato disease detection. Maine Agriculture Experiment Station Bulletin 646, 1-14.
31. 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.
32. Menges, R. M., Nixon, P. R. and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Science 33, 569-581.
33. Penny, D. C., Nolan, S. C., McKenzie, R. C., Goddard, T. W. and Kryzanowski, L. 1996. Yield and nutrient mapping for site specific fertilizer management. Communications in Soil Science Plant Analysis 27, 1265-1279.
34. Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field, C. B. 1994. Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment 48, 135-146.
35. Pettiet, J. V. 1973. An evaluation of potassium fertilizer needs for cotton in the Yazoo-Mississippi Delta. Mississippi Agricultural and Forestry Experiment Station Bullettin 66 (Mississippi State University, Mississippi, MS).
36. Pettry, D. E., 1983. Soils on the black belt branch station of the Mississippi agricultural and forestry experiment station. Mississippi Agriculture and Forestry Experiment Station.
37. Planet, W. G. 1970. Some comments on reflectance measurements of wet soils. Remote Sensing of Environment 1: 127-129.
38. Plant, R. E. and Munk, D. S. 1998. Application of remote sensing to irrigation management in California cotton. In: International Conference on Precision Agriculture, Fourth (St. Paul, MN, July 19-22) (ASA/CSSA/SSSA Madison, WI: ASA-CSSA-SSSA), pp. 1511-1521.
39. Richardson, A. J. and Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogramm. Engineering Remote Sensing 43, 1541-1552.
40. Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. In: ERTS Symposium, Third (NASA SP-351 Washington, DC) 1, 309-317.
41. Safir, G. R., Svits, G. H. and Ellingboe, A. H. 1972. Spectral reflectance and transmittance of corn leaves infected with Helminthosporium maydis. Phytopathology 62, 1210-1213.
42. Sassenrath-Cole, G. F., Hood, K. B., Alarcon, V. J., Olson, R., Tarpley, L., Varco, J. and Seal, M. 1998. Remote sensing of crop physiological function: Applications for development of variable rate inputs. In: Proceedings of the 1st International Conference on Geospatial Information in Agriculture and Forestry (Lake Buena Vista, FL, June 1-3), pp. II-604-II-608.
43. Shibayama, R. and Akiyama, T. 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing of Environment 36, 45-53.
44. Sudbrink, Jr., D. L., Harris, F. A., Robbins, J. T. Snodgrass, G. L. and Thomson, S. J. 2000. Remote sensing of late-season pest damage to cotton & wild host plants of tarnished plant bug in the Mississippi Delta. In: Proceedings of the Beltwide Cotton Conference (San Antonio, TX. Jan. 4-8), pp. 1220-1223.
45. Thenkabail, P. S., Ward, A. D. and Lyon, J. G. 1995. Landsat-5 thematic mapper models of soybean and corn crop characteristics. International Journal of Remote Sensing 15, 49-61.
46. Thenkabail, P. S., Smith, R. B. and Pauw, E. D. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71, 158-182.
47. Thomas, J. R., Wiegand, C. L. and Myers, V. I. 1967. Reflectance of cotton leaves and its relation to yield. Agronomy Journal 59, 551-554.
48. Thomasson, J. A., Chen, J., Wooten, J. R., Shearer, S. A. and Pennington, D. A. 2000. Cotton yield prediction improvement with remote sensing. In: Proceedings of the Beltwide Cotton Conference (San Antonio, TX. Jan. 4-8), pp. 419-421.
49. Thompson, W. and Robert, P. C. 1994. Valuation of mapping strategies for variable rate applications. In Proceedings of the Site-Specific Management for Agriculture Systems (ASA/CSSA/SSSA Madison, WI), pp. 303-323.
50. Tucker, C. J. 1977. Spectral estimation of grass canopy variables. Remote Sensing of Environment 6, 11-26.
51. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127-150.
52. Tucker, C. J. 1980. Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment 10, 23-32.
53. Varvel, G. E., Schlemmer, M. R. and Schepers, J. S. 1999. Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels. Precision Agriculture 1, 291-300.
54. Walburg, G., Bauer, M. E., Daughtry, C. S. T. and Housley, T. L. 1982. Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agronomy Journal 74, 677-683.
55. Wiegand, C. L., Mass, S. J., Aase, J. K., Hatfield, J. L., Pinter, P. J., Jr., Jackson, R. D., Kanemasu, E. T. and Lapitan, R. L. 1992. Multisite analysis of spectral-biophysical data for wheat. Remote Sensing of Environment 42, 1-21.
56. Wiegand, C. L. and Richardson, A. J. 1990. Use of spectral vegetation indices to infer leaf area, evapotranspiration, and yield: I. Rationale. Agronomy Journal 86, 623-629.
57. Wiegand, C. L., Richardson, A. J., Escobar, D. E. and Gerbermann, A. H. 1991. Vegetation indices in crop assessments. Remote Sensing of Environment 35, 105-119.
58. 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 the Cotton Science 3, 160-170.
59. Yang, C. and Anderson, G. L. 1999. Airborne videography to identify spatial plant growth variability for grain sorghum. Precision Agriculture 1, 67-79.
60. Zhang, M., Hendley, P., Drost, D., O'Neill, M. and Ustin, S. 1999. Corn and soybean yield indicators using remotely sensed vegetation index. In: International Conference on Precision Agriculture Fourth (St. Paul, MN, July 19-22) 1998 (ASA/CSSA/SSSA Madison, WI, 1999), pp. 1475-1481.