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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.

Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts.