This study investigates the reconstruction of climatological patterns and vegetation dynamics in the Horn of Africa region using high temporal resolution (i.e. daily) Normalized Difference Vegetation Index (NDVI) datasets. The analysis compares a straight-forward processing approach to derive a daily vegetation index from a geostationary (SEVIRI) satellite with existing NDVI series from geostationary or polar-orbiting (MODIS, MetOp-AVHRR) satellites, highlighting the impact of cloud contamination on data quality in high temporal resolution datasets. Using a smoothing process designed to reconstruct the upper envelope of the vegetation status series, we obtained a daily vegetation dataset that effectively mitigated cloud-induced fluctuations, outperforming polar-orbiting (e.g. MODIS) satellite-derived dataset in capturing regional climatology. We demonstrated this through statistical analysis, including autocorrelation and mean absolute difference between consecutive observations. We showed that cloud contamination significantly affects high temporal resolution NDVI series, particularly in forest areas, which makes it difficult to identify a suitable dataset to validate our approach. Therefore, we mitigated this problem using a Maximum Value Compositing technique, designed to remove cloud-induced biases and further compared our results with another independent vegetation index at coarser temporal resolution derived from AVHRR. We found that our vegetation index closely relates with MODIS 10-day composites after removing cloud-contaminated pixels. Furthermore, the study evaluates the sensitivity of the selected NDVI datasets to drought events, demonstrating the strength of the proposed SEVIRI dataset in capturing the intensity and persistence of vegetation anomalies. In conclusion, the study presents an innovative strategy for deriving daily-resolution NDVI datasets in cloud-prone regions, validating it with independent datasets at different sub-monthly temporal scales.

中文翻译：

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用于监测非洲之角干旱事件的高时间分辨率 NDVI 时间序列


本研究使用高时间分辨率（即每日）归一化差值植被指数 （NDVI） 数据集调查了非洲之角地区气候模式和植被动态的重建。该分析将来自对地静止轨道 （SEVIRI） 卫星的每日植被指数的直接处理方法与来自对地静止轨道或极轨 （MODIS， MetOp-AVHRR） 卫星的现有 NDVI 系列进行了比较，突出了云污染对高时间分辨率数据集中数据质量的影响。使用旨在重建植被状态序列上包络线的平滑过程，我们获得了一个每日植被数据集，该数据集有效缓解了云引起的波动，在捕获区域气候学方面优于极轨 （例如 MODIS） 卫星衍生数据集。我们通过统计分析证明了这一点，包括自相关和连续观测之间的平均绝对差。我们表明，云污染会显着影响高时间分辨率 NDVI 系列，尤其是在森林地区，这使得很难找到合适的数据集来验证我们的方法。因此，我们使用最大值合成技术缓解了这个问题，该技术旨在消除云引起的偏差，并进一步将我们的结果与来自 AVHRR 的较粗时间分辨率的另一个独立植被指数进行了比较。我们发现，去除云污染像素后，我们的植被指数与 MODIS 10 d 复合材料密切相关。此外，该研究评估了选定的 NDVI 数据集对干旱事件的敏感性，证明了拟议的 SEVIRI 数据集在捕捉植被异常的强度和持久性方面的优势。 总之，该研究提出了一种在云多发地区推导日分辨率 NDVI 数据集的创新策略，并在不同的次月时间尺度上用独立的数据集对其进行验证。