2025-09-17, 15:50–16:10 (UTC), Aula Magna
OpenLandMap-soildb (https://doi.org/10.5194/essd-2025-336) contains global dynamic predictions of soil organic carbon content, soil organic carbon density, bulk density, soil pH in H2O, soil texture fractions (clay, sand and slit) and USDA subgroup soil types (USDA soil taxonomy subgroups) at 30 m spatial resolution based on spatiotemporal Machine Learning (Quantile Regression Random Forest with output predictions showing the mean plus the lower and upper prediction intervals of 68 % probability). Predictions are provided at 3 standard depth intervals 0-30, 30-60 and 60-100 cm and for 5-year intervals. Data is available via STAC.OpenLandMap.org and via Google Earth Engine under the CC-BY license. This is the first ever global 30-m spatial resolution soildb that can be used to serve various land monitoring projects and was specifically created to support the UNCCD's Land Degradation Neutrality programme and similar international programmes where focus is on improving soil health, increasing SOC and decreasing soil degradation (soil erosion, loss of soil biodiversity, compaction, salinization and similar).
The most important variables for predicting soil organic carbon density (kg m-3) were: soil depth, Landsat-based uncalibrated Gross Primary Productivity (GPP), Normalized Difference Vegetation Index (NDVI) and CHELSA bioclimatic indices. The global distribution of soil pH can be primarily explained by the CHELSA Aridity Index (long-term), annual precipitation, and salinity grade. Detecting key variables controlling dynamics of soil properties helps improve soil management for the decades to come.
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548)
Tom Hengl is director at OpenGeoHub. He has backgrounds in pedometrics, environmetrics and spatial data science. Tom is the PI of the OEMC project.