Relating image-derived vegetation indices to crop yield

American Society for Photogrammetry and Remote Sensing - 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment 2005

Volumn , Issue , 2006, Pages 14-25

  Jang, Gab Sue ^a   Sudduth, Kenneth A ^b   Hong, Suk Young ^c   Kitchen, Newell R ^b   Palm, Harlan L ^a  

^a UNIVERSITY OF MISSOURI   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c National Institute of Animal Science   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

AERIAL SENSORS; CROP GROWTH; CROP YIELD; CROPPING SEASONS; GRAIN CROPS; GROWING SEASON; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; ITERATIVE PROCEDURES; MISSOURIS; NORMALIZED DIFFERENCE VEGETATION INDEX; SOYBEAN FIELDS; STEPWISE MULTIPLE LINEAR REGRESSION; VEGETATION INDEX; VISIBLE AND NEAR INFRARED; WATER AVAILABILITY; YIELD ESTIMATION; YIELD MONITOR;

AERIAL PHOTOGRAPHY; CROPS; INFRARED DEVICES; OILSEEDS; PHOTOGRAPHIC EQUIPMENT; REMOTE SENSING; SPECTROSCOPY; VEGETATION; VIDEO RECORDING;

ESTIMATION;

EID: 84867751396     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Birth, G. S., and G. McVey (1968). Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60: 640-643.
2. Deguise, J. C., M. McGovern, and K. Staenz. (1998). Spatial high resolution crop measurements with airborne hyperspectral remote sensing. In Proc. 4th International Conference on Precision Agriculture, edited by P. C. Robert, R. H. Rust, and W. E. Larson. ASA/CSSA/SSSA, Madison, WI, USA, pp. 1603-1608.
3. Drummond, S. T., and K. A. Sudduth (2005). Analysis of errors affecting yield map accuracy. In: Proc. 7th International Conference on Precision Agriculture, edited by D. J. Mulla. Precision Agriculture Center, University of Minnesota, St. Paul, MN, USA. CDROM.
4. GopalaPillai, S., and L. F. Tian (1999). In-field variability detection and spatial yield modeling for corn using digital aerial imaging. Transactions of the ASAE, 42(6):1911-1920.
5. Hong, S. Y., K. A. Sudduth, N. R. Kitchen, H. L. Palm, and W. J. Wiebold (2001). Using hyperspectral remote sensing data to quantify within-field spatial variability. In: Proc. Third Intl. Conf. on Geospatial Information in Agriculture and Forestry. Veridian, Ann Arbor, MI. CDROM.
6. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25: 295-309.
7. Jensen, J. R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective. Prentice Hall, Inc., Upper Saddle River, NJ, USA, pp. 33-35, 364-365.
8. Kitchen, N. R., K. A. Sudduth, D. B. Myers, R. E. Massey, E. J. Sadler, R. N. Lerch, J. W. Hummel, and H. L. Palm (2005). Development of a conservation-oriented precision agriculture system: II. Crop production assessment and plan implementation. Journal of Soil and Water Conservation (in press).
9. Lerch, R. N., N. R. Kitchen, R. J. Kremer, W. W. Donald, E. E. Alberts, E. J. Sadler, K. A. Sudduth, D. B. Myers, and F. Ghidey (2005). Development of a conservation-oriented precision agriculture system: I. Water and soil quality assessment. Journal of Soil and Water Conservation (in press).
10. Lillesand, T. M., R. W. Kiefer, and J. W. Chipman (2004). Remote Sensing and Image Interpretation. John Wiley & Sons, Inc., Danvers, MA, USA, pp. 330-393.
11. Lorenzen, B., and A. Jensen (1989). Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment, 27:201-209.
12. th Biennial Workshop on Color Photography and Videography in Resource Assessment. American Society for Photogrammetry and Remote Sensing, Bethesda, MD, USA, pp. 31-40.
13. rd Earth Resources Technology Satellite-1 Symposium. SP-351, NASA, Greenbelt, MD, USA, pp. 310-317.
14. Thenkabail, P. S., R. B. Smith, and E. D. Pauw (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71: 158-182.
15. th International Conference on Precision Agriculture, edited by P. C. Robert, R. H. Rust and W. E. Larson. ASA/CSSA/SSSA, Madison, WI, USA, pp. 1413-1422.
16. Yang, C., and J. H. Everitt (2002). Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum. Precision Agriculture, 3: 373-388.
17. Yang, C., J. H. Everitt, and J. M. Bradford (2004). Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE, 47 (3): 915-924.