Open-Source Reproducible Pipeline for Multitemporal Vegetation Monitoring Using Sentinel-2 L2A in Cloud-Prone Tropical Regions

Monitoring vegetation-index dynamics in tropical regions remains challenging due to persistent cloud contamination, landscape heterogeneity, and the lack of standardised and reproducible analytical workflows. This paper presents an open-source, fully reproducible end-to-end methodology for multitemporal vegetation monitoring using Sentinel-2 Level-2A (L2A) Bottom-of-Atmosphere (BOA) reflectance imagery. The methodology was applied to Military Grid Reference System (MGRS) tile T18NVL in the Colombian Eje Cafetero region (4.43°N–5.43°N, 74.91°W–75.90°W) for the 2017–2025 period. The proposed workflow integrates storage-efficient direct extraction of spectral reflectance from compressed Standard Archive Format for Europe (SAFE) archives using the Geospatial Data Abstraction Library (GDAL) /vsizip/ interface, per-pixel cloud and shadow masking based on the Sentinel-2 Scene Classification Layer (SCL), computation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) spectral indices, a diagnostic Random Forest experiment based on threshold-labelled spectral classes, non-parametric Mann–Kendall trend analysis with Sen’s slope estimation, and external agreement assessment against European Space Agency (ESA) WorldCover 10 m 2020 and Google Earth Pro reference data. The methodology reduced per-scene I/O time by approximately 98% without additional disk overhead while retaining a median of 57.9% valid pixels under a mean scene cloud fraction of 35.4%. Mann–Kendall analysis detected no statistically significant long-term trend in any of the four vegetation indices. Seasonal NDVI peaks during September–November were consistent with the bimodal regional precipitation regime, supporting temporal coherence in the satellite-derived vegetation-index response. External agreement was low, with an Overall Accuracy (OA) of 20.2% against ESA WorldCover and 19.9% against Google Earth Pro, indicating systematic over-prediction of woody and mixed-canopy vegetation classes. These results show that single-date optical spectral indices are insufficient for reliable thematic separation of shade-grown coffee, secondary forest, and dense forest within heterogeneous tropical landscapes. The complete version-controlled codebase is publicly available to support methodological reproducibility and adaptation across data-scarce tropical regions.