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

机译:甘蔗田间变异性的多时间尺度分析:使用卫星时间序列改进作物诊断

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摘要

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.
机译:场内空间变异性与可能与时间无关或与时间有关的多个因素有关。在这项研究中,我们的工作假设是对空间格局动态进行多时间尺度分析可以帮助确定作物状况。为了检验这一假设,我们分析了甘蔗作物在季节和年度尺度上的田间变异性,并试图将这种变异性与环境(气候,地形和土壤深度)和种植(收获日期)因素联系起来。该分析基于2002年和2003年在法国西印度群岛(瓜德罗普岛)获得的15张SPOT图像的甘蔗田地植被指数(NDVI)时间序列,以及一种原始分类方法,该方法使我们能够专注于作物空间变异性作物生长阶段。我们表明,在季节尺度上,田间生长模式取决于作物的物候期和种植作业。在年尺度上,尽管降雨量差异很大,但NDVI值却连续两年在峰值植被上呈现出稳定的格局,但NDVI值却反了。这种反演与地形有关,因此与植物水的状况有关。我们得出的结论是:(1)必须知道作物生长周期才能正确解释空间格局;(2)单日图像可能不足以诊断作物状况或进行预测;(3)活力格局发生在田间可以帮助诊断生长异常。

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