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.
KeywordsSugarcane 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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