machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. We observed that DSM publications continued to increase exponentially however, the majority (74.6%) focused on applications rather than methodology development. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km 2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition.
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