IntroductionThe increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface.MethodsThis study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the non-limnic peat thickness, to facilitate soil conservation and management practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models.Results and discussionThe best predictive performance was achieved with OK (Lin’s CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generating more accurate management zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries.

Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable production. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these materials are critical for soil management. Therefore, five drained and cultivated peatlands were studied to estimate their maximum peat thickness (MPT)—a potential key soil property that can help identify management zones for their conservation. MPT can be defined as the depth to the mineral layer (DML) minus the coprogenous layer thickness (CLT). The objective of this study was to estimate DML, CLT, and MPT at a regional scale using environmental covariates derived from remote sensing. Three machine-learning models (Cubist, Random Forest, and k-Nearest Neighbor) were compared to produce maps of DML and CLT, which were combined to generate MPT at a spatial resolution of 10 m. The Cubist model performed the best for predicting both features of interest, yielding Lin’s concordance correlation coefficients of 0.43 and 0.07 for DML and CLT, respectively, using a spatial cross-validation procedure. Interpretation of the drivers of CLT was limited by the poor predictive power of the final model. More precise data on MPT are needed to support soil conservation practices, and more CLT field observations are required to obtain a higher prediction accuracy. Nonetheless, digital soil mapping using open-access geospatial data shows promise for understanding and managing cultivated peatlands.

In the Canadian System of Soil Classification (CSSC), soils of the Organic order are classified at the great group level primarily based on the dominant organic material in the middle tier. The system recognizes four types of organic horizons: fibric (Of), mesic (Om), humic (Oh), and coprogenous earth (Oco), of which only the latter is not recognized at the great group level of the Organic order. Furthermore, at the subgroup level, Limnic subgroups cannot have terric or hydric layers. This is problematic in soils where the middle tier is dominated by limnic materials, and those which have dominantly limnic materials and have a terric layer. We describe 29 soil profiles in Ontario and Quebec, which are either poorly captured in the CSSC or that cannot be classified into the Organic order based on their diagnostic criteria. Based on an analysis of soil survey information in five provinces across Canada, we estimate 32 057 ha of organic soils which potentially contain limnic deposits. In key vegetable-producing areas of Quebec, large organic deposits in agricultural production are subject to peat subsidence and erosion, resulting in shallower depths to underlying coprogenous earth, which is not a suitable medium for crop production. This can potentially have negative effects on crops when mixed with humic materials in the plow layer. Due to these taxonomic and agronomic considerations, we propose the addition of a new great group, Limnisol, and suggest further integration of limnic materials at the subgroup level for the Humisol, Mesisol, and Fibrisol great groups.

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.

Cultivated Organic soils in Montreal's southwest plain are the most productive soils in the province of Quebec. After their initial drainage to enable farming, Organic soils are susceptible to many forms of degradation and soil loss. In this study, we characterized the physical, chemical, and pedological properties of 114 sites from five peatlands to form soil conservation management zones. We attempted to use the maximum peat thickness (MPT) as a soil degradation proxy. The MPT can be defined as the thickness of the layer of peat until coprogenous or mineral materials are reached. The latter are undesired growing media and are not considered in MPT calculation. A series of multivariate analysis of variance indicated that MPT was moderately related to soil degradation (optimal model's Pillai's trace = 0.495). Three soil degradation groups were defined, separated by two MPT thresholds: 60 and 100 cm. When looking at 17 different depth-property combinations, shallower sites (MPT < 60 cm) showed signs of soil degradation significantly higher than sites with an MPT above 60 cm. The second threshold was proposed for practical purposes. Then, these thresholds were used to separate the study area into spatially distinct management zones. Important spatial contrasts were found. This supports the theory that precision agriculture techniques are needed to target fields to optimize soil conservation interventions. The relationship between the MPT and soil degradation should be further explored to account for other degradation factors, and to better identify degraded soils and soils at risk.