Zone mapping application for precision-farming: A decision support tool for variable rate application

Precision Agriculture

Volumn 11, Issue 2, 2010, Pages 103-114

  Zhang, Xiaodong ^a   Shi, Lijian ^b   Jia, Xinhua ^c   Seielstad, George ^a   Helgason, Craig ^a  

^a UNIVERSITY OF NORTH DAKOTA   (United States)

^b NATIONAL SATELLITE OCEAN APPLICATION SERVICE   (China)

^c North Dakota State University   (United States)

Author keywords

Fuzzy c means; Management zone; Remote sensing; Variable rate application; Zone map

Indexed keywords

DECISION SUPPORT SYSTEM; FERTILIZER APPLICATION; GPS; MANAGEMENT PRACTICE; PRECISION AGRICULTURE; REMOTE SENSING; SATELLITE IMAGERY; SOIL NUTRIENT; SOIL ORGANIC MATTER; VEGETATION MAPPING; ZONING SYSTEM;

GREAT PLAINS;

EID: 77952008588     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-009-9130-4     Document Type: Article

References (27)

1. Bégué, A., Todoroff, P., & Pater, J. (2008). Multi-time scale analysis of sugarcane within-field variability: Improved crop diagnosis using satellite time series. Precision Agriculture, 9, 161-171.
2. Bhatti, A. U., Mulla, D. J., & Frazier, B. E. (1991). Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sensing of Environment, 37, 181-191.
3. Brock, A., Brouder, S. M., Blumhoff, G., & Hofmann, B. S. (2005). Defining yield-based management zones for corn-soybean rotations. Agronomy Journal, 97, 1115-1128. doi: 10. 2134/agronj2004. 0220.
4. Burrough, P. A. (1989). Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science, 40, 477-492.
5. Burrough, P. A., MacMillan, R. A., & Deursen, W. V. (1992). Fuzzy classification methods for determining land suitability from soil profile observations and topography. European Journal of Soil Science, 43, 193-210.
6. Daniel, K. W., Tripathi, N. K., Honda, K., & Apisit, E. (2004). Analysis of VNIR (400-1100 nm) spectral signatures for estimation of soil organic matter in tropical soils of Thailand. International Journal of Remote Sensing, 25, 643-652.
7. Doraiswamy, P. C., Hatfield, J. L., Jackson, T. J., Akhmedov, B., Prueger, J., & Stern, A. (2004). Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92, 548-559.
8. Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering & Remote Sensing, 69, 665-674.
9. Fleming, K. L., Westfall, D. G., Wiens, D. W., & Brodahl, M. C. (2000). Evaluating farmer defined management zone maps for variable rate fertilizer application. Precision Agriculture, 2, 201-215.
10. Franzen, D. W., Hopkins, D. H., Sweeney, M. D., Ulmer, M. K., & Halvorson, A. D. (2002). Evaluation of soil survey scale for zone development of site-specific nitrogen management. Agronomy Journal, 94, 381-389.
11. Fridgen, J. J., Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Wiebold, W. J., & Fraisse, C. W. (2004). Management zone analyst (MZA): Software for subfield management zone delineation. Agronomy Journal, 96, 100-108.
12. GDAL. (2009). http://www. gdal. org/. Accessed July 7, 2009.
13. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289-298.
14. Johnson, C. K., Mortensen, D. A., Wienhold, B. J., Shanahan, J. F., & Doran, J. W. (2003). Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agronomy Journal, 95, 303-315.
15. Lark, R. M., & Stafford, J. V. (1997). Classification as a first step in the interpretation of temporal and spatial variation of crop yield. Annals of Applied Biology, 130, 111-121.
16. Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24, 2855-2877.
17. Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319-346.
18. Moreenthaler, G. W., Khatib, N., & Kim, B. (2003). Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology. Acta Astronautica, 53, 429-437.
19. Odeh, I. O. A., Chittleborough, D. J., & McBratney, A. B. (1992). Soil pattern recognition with fuzzy-c-means: Application to classification and soil-landform interrelationships. Soil Science Society of America Journal, 56, 505-516.
20. Read, J. J., Tarpley, L., McKinion, J. M., & Reddy, K. R. (2002). Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality, 31, 1442-1452.
21. Rogovska, N., & Blackmer, A. (2009). Remote sensing of soybean canopy as a tool to map high pH, calcareous soils at field scale. Precision Agriculture, 10, 175-187.
22. Salisbury, J. W., & D'Aria, D. M. (1992). Infrared (8-14 μm) remote sensing of soil particle size. Remote Sensing of Environment, 42, 157-165.
23. Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003). Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment, 88, 157-169.
24. Sullivan, D. G., Shaw, J. N., & Rickman, D. (2005). IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal, 69, 1789-1798. doi: 10. 2136/sssaj2005. 0071.
25. Tucker, C. J., Holben, B. N., Elgin, J. H., Jr., & McMurtrey, J. E. (1980). Relationship of spectral data to grain yield variation. Photogrammetric Engineering & Remote Sensing, 46, 657-666.
26. Wetterlind, J., Stenberg, B., & Söderström, M. (2008). The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture, 9, 57-69.
27. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture-a worldwide overview. Computers and Electronics in Agriculture, 36, 113-132.