Forests in drylands are crucial for ecosystems and restoration projects such as the pan-African Great Green Wall Initiative (GGW). Remote sensing-based forest mapping is essential for assessing GGW progress, but substantial uncertainties exist across these data sets, especially in drylands. This study analyzed the spatiotemporal patterns of seven satellite forest data sets in Sahel, including four percent tree cover data sets and three discrete land cover data sets. Consistency among these data sets was evaluated in terms of forest extent, area, and temporal dynamics. The influencing factors contributing to uncertainties were also discussed. Results revealed notable discrepancies in forest distribution and area. Spatial uncertainties were mainly found in transitional zones such as highlands and agroforestry ecosystems. Forest area estimates for 2020 ranged from 6.65 x 104 km2 to 6.90 x 105 km2, with discrete data sets generally overestimating compared to percent tree cover data sets. Temporally, these data sets showed varying trends in both area and spatial distribution. Discrete data sets, compared to percent tree cover data sets, exhibited a more stable state and forest increase. Most data sets detected increases in the Guinea Highlands and southern Chad, but discrepancies were evident in areas such as the Ethiopian Highlands. This discrepancy can primarily be attributed to the inherent challenges of forest mapping in the Sahelian dryland ecosystem, characterized by highly sparse tree distribution. Percent tree cover data sets, by quantifying the areal proportion covered by trees within remote sensing pixels, could improve the ability to characterize sparse tree cover in Sahelian dryland ecosystems, facilitating the assessment of ecological initiatives such as the GGW.