Geospatial raster datasets of soil quality and modeled soil organic carbon stocks in the Brazilian Atlantic Forest

Data sets supporting the peer-reviewed article “Critical limits of soil quality and modeled soil organic carbon stocks in the Brazilian Atlantic Forest” published in the CATENA Journal (DOI: 10.1016/j.catena.2026.110208). Description This dataset provides raster layers of aboveground biomass carbon stock (AGB), soil organic carbon stock (SOC), and soil quality indices (SQI) for the Brazilian Atlantic Forest. The rasters are derived from modeling and regression-based approaches over the period 1950–2021. All layers have a spatial resolution of 30 arc-seconds (~1 km), and include information about units, depth (when applicable), and statistical type (mean or coefficient of variation). The datasets are intended to support ecological, soil, and carbon stock analyses in tropical forest landscapes. Files description af_agb_stock_mean_30s.tifAboveground biomass carbon stock (Mg C ha⁻¹), Atlantic Forest, mean 1950–2021 | Spatial resolution: 30 arc-seconds | Estimated with the CENTURY model. af_agb_stock_cv_30s.tifCoefficient of variation (%) of aboveground biomass carbon stock, Atlantic Forest, 1950–2021 | Spatial resolution: 30 arc-seconds | Estimated with the CENTURY model. af_soc_stock_0_20cm_mean_30s.tifSoil organic carbon stock (Mg C ha⁻¹) in 0–20 cm soil depth, Atlantic Forest, mean 1950–2021 | Spatial resolution: 30 arc-seconds | Estimated with the CENTURY model. af_soc_stock_0_20cm_cv_30s.tifCoefficient of variation (%) of soil organic carbon stock in 0–20 cm soil depth, Atlantic Forest, 1950–2021 | Spatial resolution: 30 arc-seconds | Estimated with the CENTURY model. af_sqi_value_0_20cm_30s.tifSoil quality index (values from 0 to 1) in 0–20 cm soil depth, Atlantic Forest, 1950–2021 | Spatial resolution: 30 arc-seconds | Derived from regression-based SQI approach. af_sqi_class_0_20cm_30s.tifSoil quality index classes (1–5) in 0–20 cm soil depth, Atlantic Forest, 1950–2021 | Spatial resolution: 30 arc-seconds | Derived from regression-based SQI approach. For additional scientific context and technical details, users are encouraged to consult the peer-reviewed article and, optionally, the accompanying README.