Precision Agriculture

, Volume 9, Issue 3, pp 161–171 | Cite as

Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?



Within-field spatial variability is related to multiple factors that can be time-independent or time-dependent. In this study, our working hypothesis is that a multi-time scale analysis of the dynamics of spatial patterns can help establish a diagnosis of crop condition. To test this hypothesis, we analyzed the within-field variability of a sugarcane crop at seasonal and annual time scales, and tried to link this variability to environmental (climate, topography, and soil depth) and cropping (harvest date) factors. The analysis was based on a sugarcane field vegetation index (NDVI) time series of fifteen SPOT images acquired in the French West Indies (Guadeloupe) in 2002 and 2003, and on an original classification method that enabled us to focus on crop spatial variability independently of crop growth stages. We showed that at the seasonal scale, the within-field growth pattern depended on the phenological stage of the crop and on cropping operations. At the annual scale, NDVI maps revealed a stable pattern for the two consecutive years at peak vegetation, despite very different rainfall amounts, but with inverse NDVI values. This inversion is linked with the topography and consequently to the plant water status. We conclude that (1) it is necessary to know the crop growing cycle to correctly interpret the spatial pattern, (2) single-date images may be insufficient for the diagnosis of crop condition or for prediction, and (3) the pattern of vigour occurrence within fields can help diagnose growth anomalies.


Sugarcane Remote sensing Diagnosis Satellite time series Spatio-temporal variability Topography NDVI Growth anomaly 



Most of the results presented here emanate from the SUCRETTE project funded by the French Ministry of Research (RTE Program). Thanks to Stéphanie Catsidonis for helping with data acquisition and to Dominique Tressens for giving access to Gardel fields and databases.


  1. Almeida, T. I. R., De Souza, C. R., & Rossetto, R. (2006). ASTER and Landsat ETM+ images applied to sugarcane yield forecast. International Journal of Remote Sensing, 27(19), 4057–4069.CrossRefGoogle Scholar
  2. Anderson, G. L., & Yang, C. (1996). Multispectral videography and geographic information systems for site-specific farm management. In P. Robert, R. H. Rust, & W. E. Larson (Eds.), Proceedings of 3rd International Conference on Precision Agriculture (pp. 681–692). Madison, WI, USA: ASA, CSSA, SSSA.Google Scholar
  3. Boydell, B., & Mc Bratney, A. B. (2002). Identifying potential within-field management zones from cotton-yield estimates. Precision Agriculture, 3, 9–23.CrossRefGoogle Scholar
  4. Cabidoche, Y. M. (1985). Distribution des sols à argiles gonflantes sur calcaires récifaux (zone Caraïbe)—Utilisation de mesures de résistivité électrique (Distribution of expanding clay soils over reef limestone in the Caribbean—Use of electric resistivity). In ORSTOM (Ed.), Proceedings of Séminaire scientifique de pédologie pour la région de l’Amérique Centrale et des Caraïbes (pp. 187–221), Paris, France.Google Scholar
  5. Eghball, B., & Varvel, G. E. (1997). Fractal analysis of temporal yield variability of crop sequences: Implications for site-specific management. Agronomy Journal, 89(6), 851–855.Google Scholar
  6. Hesse, A. (1966). In P. Dunod (Ed.), Prospections géophysiques à faible profondeur. Applications à l’archéologie (Low depth geophysical prospecting —Application to archaeology) (149 pp), France.Google Scholar
  7. Lamb, J. A., Dowdy, R. H., Anderson, J. L., & Rehm, G. W. (1997). Spatial and temporal stability of corn grain yields. Journal of Production Agriculture, 10(3), 410–414.Google Scholar
  8. Lelong, C. C. D., Pinet, P. C., & Poilvé, H. (1998). Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce (France). Remote Sensing of Environment, 66, 179–191.CrossRefGoogle Scholar
  9. Machado, S., Bynum, E. D., Archer, T. L., Lascano, R. J., Wilson, L. T., Bordovsky, J., Segarra, E., Bronson, K., Nesmith, D. M., & Xu, W. (2002). Spatial and temporal variability of corn growth and grain yield: Implications for site-specific farming. Crop Sciences, 42, 1564–1576.Google Scholar
  10. Marques Da Silva, J. R., & Alexandre, C. (2005). Spatial variability of irrigated corn yield in relation to field topography and soil chemical characteristics. Precision Agriculture, 6(5), 453–466.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664.Google Scholar
  13. Ribbes, F., Bégué, A., Siegmund, B., Todoroff, P., & Autrey, L. J. C. (2002). Potentialités de la télédétection satellitaire pour la filière canne à sucre—Projet SUCRETTE (Potential of satellite remote sensing for the sugarcane industry—SUCRETTE project). In Proceedings of Perspectives de développement de la canne à sucre en milieu insulaire, Réunion (10 pp), France.Google Scholar
  14. Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS, Technical Report (Accession Number: 74N30727; Document ID: 19740022614) (pp. 309–317), NASA Center, USA.Google Scholar
  15. Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96, 195–203.Google Scholar
  16. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment, 8, 127–150.CrossRefGoogle Scholar
  17. Yang, C., & Everitt, J. H. (2002). Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum. Precision Agriculture, 3, 373–388.CrossRefGoogle Scholar
  18. Zarco-Tejada, P. J., Ustin, S. L., & Whiting, M. L. (2005). Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal, 97(3), 641–653.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Agnès Bégué
    • 1
  • Pierre Todoroff
    • 2
  • Johanna Pater
    • 1
  1. 1.CIRAD, UMR TETISMontpellierFrance
  2. 2.CIRAD, UR SCAPetit Bourg, GuadeloupeFrance

Personalised recommendations