This article studies the influence of the number of satellite remote sensing acquisition dates and their sampling on the performance of a time series reconstruction method developed in Granero-Belinchon et al. 2020. This method initially aimed at monitoring urban London plane (Platanus x acerifolia) trees, and was tested with Sentinel-2 imagery at spatial resolutions of 10 and 20 m and a temporal revisit of 5 days. Due to its higher revisit frequency of 2 days while having a similar spatial resolution of 10 m, Ven$\mu$s imagery was consequently used in the present article to fulfill with the purpose of this study. The strategy relies on the building of different acquisition date configurations based on the Ven$\mu$s time series by considering uniform and non-uniform samplings and with a total number of acquisitions ranging from 45 to 14. Thus, the aim of the article is to examine the number of annual acquisitions needed to describe properly a vegetation phenological cycle and the impact of the annual sampling of these acquisitions on the final reconstructed time series. To this end, this study was carried out by using the widely used Normalized Difference Vegetation Index (NDVI). Results showed that on one hand, applied on an acquisition configuration composed of at least 18 uniformly sampled dates throughout the year, this reconstruction methodology is able to describe correctly the annual NDVI dynamics but leads to inaccuracies in the description of intra-annual ones. Nevertheless, these intra-annual descriptions are improved with the increase of the number of acquisitions. On the other hand, strongly non-uniform acquisition date samplings lead to inaccurate descriptions of the undersampled time periods but correct descriptions of the rest of the time series curve. The study case is London planes located in Toulouse (France) with 45 cloud-free Ven$\mu$s images during the year 2019. Finally, this work emphasizes the main limitations of the studied reconstruction methodology when few acquisitions or very non-uniform acquisition date samplings are available and thus the identification of borderline cases in future applications and other study cases.

本文研究了卫星遥感采集日期的数量及其采样对Granero-Belinchon等人开发的时间序列重建方法的性能的影响。2020年。此方法最初旨在监视伦敦市区的飞机（Platanus x acerifolia）树木，并已通过Sentinel-2图像在10和20 m的空间分辨率下进行了5天的时间重新测试。由于其2天的重访频率较高，同时具有类似的10 m空间分辨率，Ven$\mu$因此，本文中使用了s图像来达到本研究的目的。该策略依赖于基于Ven的不同采集日期配置的构建$\mu$通过考虑均匀和非均匀采样以及采集总数在45到14个范围内的时间序列。因此，本文的目的是研究适当描述植被物候周期及其影响所需的年度采集次数最终重建时间序列上这些采集的年度采样的百分比。为此，这项研究是通过使用广泛使用的归一化植被指数（NDVI）进行的。结果表明，一方面，在一年中至少采用18个均匀采样日期组成的采集配置上，这种重建方法能够正确描述年度NDVI动态，但导致在年内描述方面存在误差。不过，随着收购数量的增加，这些年度内说明也得到了改善。另一方面，严重不一致的采集日期采样导致欠采样时间段的描述不准确，但对时间序列曲线其余部分的描述正确。研究案例是位于法国图卢兹的伦敦飞机，飞机上有45个无云的Ven$\mu$s图像在2019年。最后，这项工作着重说明了在几乎没有采集或采集数据非常不均匀的情况下，所研究的重建方法的主要局限性，因此在将来的应用和其他研究案例中确定了临界情况。