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

Abstract : 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.
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Soumis le : lundi 30 janvier 2012 - 18:05:37
Dernière modification le : vendredi 8 octobre 2021 - 16:26:54
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Agnes Begue, Pierre Todoroff, Johanna Pater. Multi-time scale analysis of sugarcane within-field variability: Improved crop diagnosis from satellite time series ?. Precision Agriculture, Springer Verlag, 2008, 9 (3), pp.161-171. ⟨10.1007/s11119-008-9063-3⟩. ⟨cirad-00664059⟩



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