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In 2023, the European Commission released a legislative proposal for a Directive on Soil Monitoring and Resilience which aims to define a legal framework to achieve healthy soils across the European Union (EU) by 2050. A key component of the initial Directive is the mandate for Member States to establish basic geographic soil governance units, referred to as soil districts, and appoint a district-specific authority to oversee the implementation of soil health assessments. This paper proposes an operational definition of the districts following the conditions outlined in the proposal for the Directive and discusses various attention points for their implementation. Tentative districts were developed for seven EU countries, considering soil type, climate, topography, and land cover factors, starting from the smallest existing administrative unit (i.e. municipalities). Experts were asked to report on the applicability of the proposed districts within well-known pedo-ecological regions and discuss the relevance of the districts for establishing an EU-wide monitoring network and reporting on soil health and degradation. The outcomes highlight the need for detailed soil maps to account for specific soil types when stratifying countries into soil districts. The soilscape approach allows for a consistent method to defining soil districts across Member States. This enables contrasting soils within a district to be managed in a similar manner, with soil degradation/health thresholds applied to each district based on land cover. However, it is unclear whether soil districts as currently formulated in the Directive are in fact the right tool to support local soil management and monitoring of soil health. Districts can help ensure that all soil conditions are covered in a monitoring system, but they may not provide support for soil management or monitoring at a local scale due to short-scale soil variability and threats affecting soil management within the same soilscape. Beyond the use of districts for designing a European/national scale monitoring system, the districts can help create animations and other educational tools to promote soil literacy and connectivity of users to soils locally.

Various stakeholders, such as modelers, policy makers, farmers, and environmental regulators need reliable soil bulk density and coarse fragment content data. These two soil parameters are necessary to calculate soil carbon and nutrients stocks, to estimate water availability for plants, or to assess soil compaction. However, measuring these two parameters is labor intensive and time consuming. Therefore, many agricultural and environmental studies often miss these two soil parameters. Here, we provide four datasets, one with bulk density and coarse fragment contents of topsoil and subsoil, measured in two campaigns of the French Soil Quality Monitoring Network (RMQS for its acronym in French), a second one with the average values for bulk density and coarse fragments of the two campaigns at 0–30 cm and 30–50 cm. The third and the fourth ones are the raw data needed to calculate the two first datasets divided by campaign. In addition, the R script for calculating the depth-weighted values per soil layer is provided

A recent assessment states that 60–70% of soils in Europe are considered degraded. Protecting such valuable resource require knowledge on soil status through monitoring systems. In Europe, different types of monitoring networks currently exist in parallel. Many EU Member states (MS) developed their own national soil information monitoring system (N-SIMS), some being in place for decades. In parallel in 2009, the European Commission extended the periodic Land Use/Land Cover Area Frame Survey (LUCAS) led by EUROSTAT to sample and analyse the main properties of topsoil in EU in order to develop a homogeneous dataset for EU.Both sources of information are needed to support European policies on soil health evaluation. However, a question remains whether the assessment obtained by using soil properties from both monitoring programs (N-SIMS and LUCAS Soil) are comparable, and what could be the limitations of using either one dataset or the other.Conducted in the context of European Joint Programme (EJP) SOIL, this study shows the results of a comparison between N-SIMS and LUCAS Soil programs among 12 different EU member states including BE, DE, DK, EE, ES, FR, DE, HU, IT, NL, PL, SE and SK. The comparison was done on: (i) the sampling strategies including site densities, land cover and soil type distribution; (ii) the statistical distribution of three soil properties (organic carbon, pH and clay content); (iii) two potential indicators of soil quality (i.e. OC/Clay ratio and pH classes). The results underlined substantial differences in soil properties statistical distributions between N-SIMS and LUCAS Soil in many member states, particularly for woodland and grassland soils, affecting the evaluation of soil health using indicators. Such differences might be explained by both the monitoring strategy and sampling or analytical protocols exposing the potential effect of data source on European and national policies. The results demonstrate the need to work towards data harmonization and in the light of the Soil Monitoring Law, to carefully design the future of soil monitoring in Europe taking into account both LUCAS Soil and N-SIMS considering the significant impact of the monitoring strategies and protocols on soil health indicators.

We provide six short building blocks to devise adaptable soil definitions. These buiding blocks start from a simple and straightforward metaphor and progressively evolve when adopting geophysical, analytical, biogeochemical, cultural, and services-driven points of view. These progressive changes end up with a straightforward, yet most important message for a broad audience. We discuss these building blocks by comparing them to recently proposed soil definitions. Our aims are to initiate a debate on i) what should be, or should not be named soil, ii) what a soil definition should be, iii) the need for both a generic definition and a set of adaptable definitions, based on building blocks, commonly accepted by soil scientists, and iv) the need to adapt our language to diverse audiences. Finally, we propose a definition in one sentence.

The soil organic carbon (SOC) to clay-sized particles ratio (SOC/clay) has recently been selected as an indicator of the soil organic matter status in managed mineral soils within the framework of the European Soil Monitoring Law proposal. This indicator was initially developed to predict soil structural quality in a local study in Switzerland and was subsequently tested at national scales in England and Wales, and in Germany. In this study, we evaluated if the SOC/clay ratio was relevant to assess the structural quality of soils at the national scale in mainland France. We additionally evaluated its variant, SOC/(silt

Digital soil mapping (DSM) is commonly conducted using input soil attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source of soil data that has the potential to enhance DSM predictions. However, they are not widely used due to to concerns about subjectivity and data quality. This study investigates the usefulness of hand-feel soil texture (HFST) data for DSM. We processed HFST data obtained from forest soils in France from two inventory campaigns: (i) HFST determination from systematic 1 km2 grid observations in France utilizing a specialized soil texture triangle, and (ii) HFST observations from soil survey samples, using a different texture triangle. Both sets of HFST data were used as input soil variables, with the same covariates, for predicting topsoil texture In a sizable, forested area through a DSM method. By employing independent sampling and laboratory soil analyses in selected areas, we uncovered measurement bias in one of the datasets. However, intriguingly, these biased observations identified subtle yet highly specific and unexpected patterns of sands in terraces due to alluvial deposits along small rivers. Thus, field soil observations, even if they are biased, should not be dismissed solely based on their overall predictive performance. It is essential to carefully examine predicted maps and covariates to determine whether patterns may have pedological and/or lithological origins and if they are pertinent for enhancing DSM predictions, enhancing soil process understanding, and meeting the requirements of end users. Numerous HFST are available worldwide, these datasets are usually disregarded for DSM. Here we contend that efforts should be put in recovering these data, and their potential for enhancing DSM and deepening our understanding of soil processes.

Soil organic carbon (SOC) plays a crucial role in soil health and global carbon cycling, therefore accurate estimates of its spatial distribution are important for managing soil health and mitigating global climate change. Digital soil mapping shows its potential to provide accurate and high-resolution spatial distribution of SOC across scales. To convert SOC content to SOC density (SOCD), two inference trajectories exist for predicting SOCD in digital soil mapping: the direct approach (calculate-then-model) and indirect approach (model-then-calculate). However, there is a lack of comprehensive exploration regarding the differences in their performance in SOCD estimates, particularly in regions characterized by diverse pedoclimatic conditions. To bridge this knowledge gap, we evaluated the two approaches based on model performance of SOCD in France. Using 916 topsoils (0−20 ​cm) from the LUCAS Soil 2018 and 24 environmental covariates, random forest model and forward recursive feature selection were used to build the spatial predictive models of SOCD using direct and indirect approaches. The results show that, using random forest model and full covariates, both approaches show moderate performance (R2 ​= ​0.28−0.32). By utilizing forward recursive feature selection model, the number of predictors was reduced from 24 to 9, enhancing model performance for direct approach (R2 of 0.35), with no improvement for indirect approach (R2 of 0.28). The mean SOCD of the French topsoil was 5.29 and 6.14 ​kg ​m−2 by direct and indirect approaches, resulting in SOC stock of 2.8 and 3.3 ​Pg, respectively. We found that the direct approach clearly underestimated the high SOCD (>9 ​kg ​m−2), while the indirect approach performed much better for high SOCD. Our findings serve as a valuable reference for SOCD mapping, thereby providing a scientific basis for maintaining soil health.

Soil Security includes dimensions, soil capability, soil condition, soil capital, soil connectivity and soil codification (the “five C's”). This article provides a short review on how soil mapping, digital soil mapping and soil monitoring systems (SM, DSM and SMS) over large areas contribute to these five C's at scales ranging from country to globe. Changes and the evolution in aims of SM, DSM and SMS were driven both by main issues related to policy priorities and associated advances in science and technology. This review shows that SM, DSM and SMS can provide the basis for assessing soil capability and condition over large areas, especially if we assume that capability mainly depends on rather stable soil attributes. Repeated DSM or SMS are appropriated tool to monitor changes in soil condition at these scales. They may even allow mapping changes in soil capability. However, broad-scale SM, DSM and SMS have not yet fully achieved the provision of information concerning the delivery of some soil functions and soil-based ecosystem services. Although significant progress in estimating the capital dimension of soil security has been achieved, there is need to progress monitoring changes in soil capital. Broad-scale SM, DSM and SMS has great potential to increase soil connectivity. The main challenge is adapting our language and our communication to the target audience. There are encouraging initiatives to enhance soil codification. Codification issues are largely driven by the political agenda, there is still an urgent need to increase soil connectivity, especially towards citizens, NGOs and policy-makers.

The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 to predict SOC content over temperate agroecosystems characterized with annual crops. However, because spectral models are only applicable on bare soils, the mapping of SOC is often obtained on limited areas. A possible improvement for increasing the number of pixels on which SOC can be retrieved by inverting bare soil reflectance spectra, consists of using optical images acquired at several dates. This study compares different approaches of Sentinel–2 images temporal mosaicking to produce a composite multi-date bare soil image for predicting SOC content over agricultural topsoils. A first approach for temporal mosaicking was based on a per-pixel selection and was driven by soil surface characteristics: bare soil or dry bare soil with/without removing dry vegetation. A second approach for creating composite images was based on a per-date selection and driven either by the models performance from single-date, or by average soil surface indicators of bare soil or dry bare soil. To characterize soil surface, Sentinel-1 (S1)-derived soil moisture and/or spectral indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio 2 (NBR2), bare soil index (BSI) and a soil surface moisture index (S2WI) were used either separately or in combination. This study highlighted the following results: i) none of the temporal mosaic images improved model performance for SOC prediction compared to the best single-date image; ii) of the per-pixel approaches, temporal mosaics driven by the S1-derived moisture content, and to a lesser extent, by NBR2 index, outperformed the mosaic driven by the BSI index but they did not increase the bare soil area predicted; iii) of the per-date approaches, the best trade-off between predicted area and model performance was achieved from the temporal mosaic driven by the S1-derived moisture content (R2 ~ 0.5, RPD ~ 1.4, RMSE ~ 3.7 g.kg-1) which enabled to more than double (*2.44) the predicted area. This study suggests that a number of bare soil mosaics based on several indicators (moisture, bare soil, roughness…), preferably in combination, might maintain acceptable accuracies for SOC prediction whilst extending over larger areas than single-date images.