Toure L. auteur aut IRD Mbiafeu A. C. N. auteur aut IRD Hauhouot A. C. auteur aut IRD Barnieh B. A. auteur aut IRD Gluski Pauline auteur aut IRD text journalArticle eng text/pdf born digital access This study proposes an integrated high-resolution optical remote-sensing workflow for monitoring agro-ecosystem dynamics and transition signals in Senegal's Sédhiou region from 2020 to 2025. Sentinel-2 imagery was preprocessed in Google Earth Engine (cloud masking, harmonization, and compositing) and used to derive three spectral indices (NDVI, SAVI, and NDWI). Field data collected in March 2025 were used to train and validate the 2025 land-cover map, while the 2020-2025 component focused on vegetation-index dynamics and transition signals rather than year-by-year validated land-cover conversions. A Random Forest classifier mapped five land-cover classes in 2025, and achieved 94% overall accuracy and kappa 0.98, with class-specific classification errors of 0.0%, 1.0%, 4.2%, 7.0%, and 10.2% for Water, Built-up, Forest, Cropland, and Shrubland, respectively. Monthly time-series analysis revealed marked intra- and interannual variability across land-cover classes and identified transition-signal hotspots (persistent departures in index trajectories) using rolling variability metrics and breakpoint screening. Forests were comparatively stable (mean NDVI = 0.53), while shrublands showed intermediate dynamics (mean NDVI = 0.49). Cropland areas exhibited the highest variability, consistent with crop-fallow cycles and rainfall sensitivity in rainfed systems; this variability alone does not constitute evidence of land-cover transition. The most pronounced break episodes were observed in 2022 and 2024, especially in the Cropland class, and are interpreted as transition signals that may reflect rainfall-driven variability and or land-use dynamics, thus requiring cautious attribution. Overall, the workflow provides an operational framework, including image preprocessing, feature extraction, classification, index time-series extraction, and breakpoint screening, for identifying variability hotspots and prioritizing areas for targeted field verification and sustainable agro-ecosystem management in Sédhiou. specialized Remote sensing Cloud computing Time series Agro-ecosystem dynamics Sédhiou Senegal SENEGAL 021 082 126 [Early access] [24 p.] 2026 2509-9426 https://www.documentation.ird.fr/hor/fdi:010097004 10.1007/s41748-026-01166-8 2509-9426 [F B010097004] https://www.documentation.ird.fr/hor/fdi:010097004 https://www.documentation.ird.fr/intranet/publi/2026-05/010097004.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2026-05-20 2026-05-20 fdi:010097004 fre