Digital mapping of GlobalSoilMap soil properties at a broad scale: A review - Chen, Songchao; Arrouays, Dominique; Leatitia Mulder, Vera; Poggio, Laura; Minasny, Budiman; Roudier, Pierre; Libohova, Zamir; Lagacherie, Philippe; Shi, Zhou; Hannam, Jacqueline; Meersmans, Jeroen; Richer-de-Forges, Anne C.; et al. (Geoderma, 2022)
•Digital soil mapping studies at broad scale strongly increased from 2003 to 2019.•Most studies focused on mapping SOC and soil particle size fractions.•Model performance usually decreased in deeper depth intervals.•New studies should explore other covariates, functional soil properties and deeper layers.•Challenges remain for assessing performance, uncertainty and changes with time. 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. 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. 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. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. 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. 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%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.