Clustering of Remotely Sensed Time Series using Functional Principal Component Analysis to Monitor Crops

L. Coviello, Francesco Maria Martini, L. Cesaretti, S. Pesaresi, F. Solfanelli, A. Mancini
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Abstract

The monitoring of cropland areas and in particular the capability to evaluate the performance of a field over space and time is becoming a crucial activity to schedule agronomic operations (e.g., fertilization) properly. In particular, the use of remotely sensed data opened new ways for this kind of analysis. In this work, we present a methodology based on Functional Data Analysis that starting from remotely sensed time-series data gen-erates cluster maps of a cropland area. Starting from vegetation index time-series data, Functional Principal Component Analysis (FPCA) was applied to derive FPCA scores and components. FPCA scores are then clusterized to obtain maps that embed the dynamics of crops over space and time. The derived maps can be used to optimize agronomic tasks such as fertilization also acting as base layers to create management zones and then prescription maps.
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基于功能主成分分析的作物遥感时间序列聚类研究
对耕地面积的监测,特别是在空间和时间上评价一块田地的表现的能力,正成为适当安排农艺作业(例如施肥)的一项关键活动。特别是,遥感数据的使用为这类分析开辟了新的途径。在这项工作中,我们提出了一种基于功能数据分析的方法,该方法从遥感时间序列数据开始生成农田区域的集群图。从植被指数时间序列数据出发,应用功能主成分分析(Functional Principal Component Analysis, FPCA)得到FPCA分数和成分。然后对FPCA分数进行聚类,以获得嵌入作物在空间和时间上的动态的地图。衍生的地图可以用来优化农艺任务,如施肥,也可以作为基础层来创建管理区域,然后是处方地图。
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