Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.

The paper describes production steps and accuracy assessment of an analysis-ready (complete, consistent, correct and current) open environmental data cube (2000–2021+) for continental Europe; at working resolutions from 10 m to 30 m and with quarterly to annual estimates. The data cube is based on processing and harmonizing earth observation (EO) images: Landsat GLAD ARD (2000–2020+), Sentinel-2 images (2017–2021+) and Digital Elevation data. These datasets were created with accessibility, user-friendliness, interoperability and synthesis in mind. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. To ensure a missing value percentage below 1%, the EO data was first aggregated into four quarterly periods approximating the four seasons common in Europe (winter, spring, summer and autumn), and then split into three percentiles (25th, 50th and 75th). Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. The accuracy assessment shows TMWM gap-filling achieves higher performance in Southern Europe, and lower performance in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. The intended uses of the EcoDataCube platform include vegetation, soil, land cover and land use mapping projects, environmental monitoring and automated generation of data for statistical offices including Eurostat. Results further show that combining all four datasets produced in this work (DTM, Landsat 30 m, Sentinel-2 30 m and Sentinel-2 10 m) yields the highest land cover classification accuracy, with different datasets improving the results for different land cover classes. The Environmental data cube for Europe is available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through STAC and the EcoDataCube data portal.