Non-active agricultural land (NAAL) mapping in West Africa is essential to accurately assess agricultural systems and its contribution to food security and agro-ecological sustainability of current practices, and yet the available mapping methodologies are not adapted to the environmental and cropping conditions encountered when addressing tropical smallholder agriculture. In this study we present a strategy that makes use of Sentinel-2 image time series, CHIRPS monthly rainfall data and multiple years of in-situ data obtained from the JECAM database to map NAAL in a Soudanian site in Burkina Faso (Koumbia) between the years 2016 and 2021. In a first step we generated annual land use maps in four broad classes (managed, unmanaged, evergreen and non-vegetated) to detect fields being actively cultivated in a given year, and in a second step, we used these annual land use maps to differentiate non-active agricultural land by identifying shifts from one year to another. For the validation part, we analyzed the sensitivity of classification accuracy to in-situ data pre-processing by building 5 experimental validation data sets. The unmanaged classes F1-scores of the land use maps ranged between 0.86 and 0.98, depending on the year, whereas NAAL classes F1-scores ranged from 0.75 to 0.92 when validated against the most restrictive data set (pixels with no missing reference data for the period considered). NAAL represents between 7% to 14% of the study site cropland depending on the year. The higher class probabilities are in areas where data was available, whereas the low probabilities are localized and linked to transition areas on the outskirts of the department. Our results indicate that a multi-annual approach can allow NAAL mapping under challenging environments, yet efforts are to be made to develop more cost-efficient unsupervised solutions.