Digital mapping of GlobalSoilMap soil properties at a broad scale: a review

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

Soil carbon stocks, climate and land use changes. Some issues, research needs and the "4 pour mille" French initiative

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A note on knowledge discovery and machine learning in digital soil mapping

In digital soil mapping, machine learning (ML) techniques are being used to infer a relationship between a soil property and the covariates. The information derived from this process is often translated into pedological knowledge. This mechanism is referred to as knowledge discovery. This study shows that knowledge discovery based on ML must be treated with caution. We show how pseudo-covariates can be used to accurately predict soil organic carbon in a hypothetical case study. We demonstrate that ML methods can find relevant patterns even when the covariates are meaningless and not related to soil-forming factors and processes. We argue that pattern recognition for prediction should not be equated with knowledge discovery. Knowledge discovery requires more than the recognition of patterns and successful prediction. It requires the pre-selection and preprocessing of pedologically relevant environmental covariates and the posterior interpretation and evaluation of the recognized patterns. We argue that important ML covariates could serve the purpose of providing elements to postulate hypotheses about soil processes that, once validated through experiments, could result in new pedological knowledge. Highlights: We discuss the rationale of knowledge discovery based on the most important machine learning covariates We use pseudo-covariates to predict topsoil organic carbon with random forest Soil organic carbon was accurately predicted in a hypothetical case study Pattern recognition by random forest should not be equated to knowledge discovery.</p

Soil-landscape controls on the impact of extreme warm and dry events on terrestrial ecosystems within continental Europe and the Mediterranean Basin

Vegetation activity within continental Europe and the Mediterranean Basin is changing and extreme warm and dry events cause substantial reductions in vegetation activity. This region covers a wide variety of climate regimes, soil and land cover types. Distinguishing the response of the vegetation to extreme heat, drought and compound events as a function of land cover type, climate regime and soil type allows to better explain regional and local differences in vegetation response. This research analyses the temporal evolution of the impact of extreme heat, drought and compound events on the observed NDVI during the growing season, aggregated by land cover, soil and climate types. The results show 31% reduction of the median vegetation activity, though highly variable depending on local conditions in climate, soil and vegetation type. The maximum impact was observed for extreme warm and dry compound events, one month after the extreme event occurrence, and recovery took up to 4 months. Furthermore, the impact of extreme heat and drought on vegetation activity increases when moving to colder climates with the largest change occurring in high latitude natural land cover types. The soil properties affecting the impact of extreme droughts on vegetation activity were mainly related to root depth limitations, water regulating properties and ecophysiological properties. Within a temperate climate, water availability and water regulating properties of the soil were found most important; in water limiting conditions those showing limiting root development con- ditions linked to abrupt textural changes (e.g. Luvisol), accumulated salts (e.g. Solonchak) or unfavorable soil structure (compaction, swelling/shrinking) were more impacted and had a slower recovery. Within a hot and dry temperate climate, there were major differences in the overall productivity rather than a different response to extreme events. These differences were mainly related to growth limiting conditions due to accumulated salts in the soil profile (e.g. Calcisol, Solonchak). Furthermore, it was found that vegetation activity of stable forest ecosystems was strongly impacted by extreme climatic events, especially in Northern Europe. The controlling fac- tors defining the impact of extreme warm and dry events were found to be a complex combinatory of soil, climate and ecophysiological properties (e.g. Podzols and mycorrhiza networks in Northern Europe). With the expected increase of extreme climatic events, the current stable and productive sys- tems will be most affected and large reductions in vegetation activity can be expected, partly due to the lack of ecophysiological adaptation of plants towards the change in climate

Vers une base de données mondiale des propriétés des sols ? GlobalSoilMap : Contexte et enjeux, propositions, avancement en France

National audienc

Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France

Soil thickness (ST) plays an important role in regulating soil processes, vegetation growth and land suitability. Therefore, it has been listed as one of twelve basic soil properties to be delivered in GlobalSoilMap project. However, ST prediction has been reported with poor performance in previous studies. Our case study is located in the intensive agriculture Beauce area, central France. In this region, the ST mainly depends on the thickness of loess (TOL) deposits over a calcareous bedrock. We attempted to test the TOL prediction by coupling a large soil dataset (10978 sampling sites) and 117 environmental covariates. After variable selection by recursive feature elimination, quantile regression forests (QRF) was employed for spatial modelling, as it was able to directly provide the 90% prediction intervals (PIs). Averaging a total of 50 models, generated by repeated stratified random sampling, showed a substantial model performance with mean R2 of 0.33, RMSE of 30.48 cm and bias of −1.20 cm. The prediction interval coverage percentage showed that 86.70% of the validation samples fall within the predefined 90% PIs, which also indicated the prediction uncertainty produced by QRF was reasonable. The relative variable importance indicated the importance of airborne gamma-ray radiometric data and Sentinel 2 products in TOL prediction. The produced TOL map with 90% PIs makes sense from a soil science and physiographic point of view. The final product can guide evidence-based decision making for agricultural land management, especially for irrigation in our case study.</p