Exploratory assessment of the SLAKES method to characterize aggregate stability across diverse soil types

Classical soil aggregate stability (AS) methods lack standardized protocols and require long measurement times. However, the fairly new SLAKES method purportedly allows for rapid AS estimation with minimal technical equipment. SLAKES has been tested on fine-textured soils but its suitability for other soil types is unknown. This study investigated SLAKES’ suitability for AS measurements on silty clay, silt loam, and sandy loam soils. For each SLAKES test, three aggregates were immersed in distilled water and imaged for 10 min. SLAKES output includes disaggregation data per aggregate and three coefficients from a Gompertz function that describe slaking dynamics. Four AS descriptors obtained from SLAKES output were investigated: the averaged maximum slaking from a test (aSK), the maximum slaking for each measurement (aggregate) (aFT, from fitting a Gompertz function to SLAKES raw data), the averaged aFT for the measurements in a test (a̅FT), and the slaking index at 10 min per measurement (SI600). The aSK is a direct descriptor included in the SLAKES output, while aFT, a̅FT, and SI600 are indirect descriptors. The SI600 was the most preferred SLAKES AS descriptor since it is a calculated parameter and due to its sensitivity in detecting AS status among all soil types. The sandy loam soil was the most stable from both the raw SLAKES data and fitting, albeit counterintuitive. SLAKES default measurement time was sufficient for the silty clay and silt loam soils but not for the sandy loam soil. Overall, SLAKES was a useful tool for AS measurements on fine-textured soils but was less suitable for AS measurements on the coarse-textured soil

Mapping peat depth using a portable gamma-ray sensor and terrain attributes

Pristine peatlands being excellent storage for terrestrial Carbon (C) play a crucial role in regulating climate and water and provide several important ecosystem services. However, peatlands have been heavily altered (e.g., by draining the water table), increasing greenhouse gas (GHG) emissions. Restoring peatlands requires a comprehensive characterization, including knowledge of peat depth (PD; m). Traditionally, this requires the physical insertion of a push probe, which is time-consuming and labor-intensive. It has been shown that non-invasive proximal sensing techniques such as electromagnetic induction and ground penetrating radar can add value to PD data. In this research, we want to assess the potential of proximally sensed gamma-ray (γ-ray) spectrometry (i.e., potassium [K], thorium [Th], uranium [U], and the count rate [CR]) and terrain attributes data (i.e., elevation, slope, SAGAWI, and MRVBF) to map PD either alone or in combination across a small (10 ha) peatland area in ØBakker, Denmark. Here, the PD varies from 0.1 m in the south to 7.3 m in the north. We use various prediction models including ordinary kriging (OK) of PD, linear regression (LR), multiple LR (MLR), LR kriging (LRK), MLR kriging (MLRK) and empirical Bayesian kriging regression (EBKR). We also determine the minimum calibration sample size required by decreasing sample size in decrements (i.e., n = 100, 90, 80,…, 30). We compare these approaches using prediction agreement (Lin’s concordance correlation coefficient; LCCC) and accuracy (root mean square error; RMSE). The results show that OK using maximum calibration size (n = 108) had near perfect agreement (0.97) and accuracy (0.59 m), compared to LR (ln CR; 0.65 and 0.78 m, respectively) and MLR (ln K, Th, CR and elevation; 0.85 and 0.63 m). Improvements are achieved by adding residuals; LRK (0.95 and 0.71 m) and MLRK (0.96 and 0.51 m). The best results were obtained using EBKR (0.97 and 0.63 m) given all predictions were positive and no significant change in agreement and standard errors with the decrement of calibration sample size (e.g., n = 30). The results have implications towards C stocks assessment and improved land use planning to control GHG emissions and slow down global warming

Assessing the suitability of ground-penetrating radar for peat imaging

Peatland conservation and restoration are prominent in slowing global warming. A thorough comprehension of peat inventory, especially the thickness, bulk density, water table levels, and geological setting, is necessary to plan and initiate rewetting strategies and to calculate emission savings. The conventional mapping methods involving push probes and boreholes are not only cost- and labor-intensive, but they also provide only localized measurements. Among the geophysical sensors, while electromagnetic induction (EMI) and gamma-ray spectrometry have proven to be suitable for mapping specific attributes, ground penetrating radar (GPR) is seen as the industry’s standard recommendation. However, the success rate can be highly variable in reality depending on the peatland type, and ignoring this can lead to the waste of numerous resources. To demonstrate this, in this study, we compare GPR survey transects performed on two different peatland types (a bog vs. a fen) with two different antenna center frequencies (i.e., 160 MHz and 450 MHz). Electrical resistivity tomography was also performed along the same transects to complement and guide our interpretation. Our results suggest that while GPR surveys are suitable in rain-fed oligotrophic bogs, less to no success rate can be anticipated in minerotrophic fens. Forward modeling using gprMax is also shown to substantiate these findings. Thus, knowledge of the peatland type constitutes crucial information for sensor selection. If in doubt, we recommend performing on-the-go EMI surveys before initiating GPR surveys, as electrical conductivity might be sufficient on its own for peatland characterization. Moreover, EMI is also useful for predicting GPR performances

Mapping and monitoring peatland conditions from global to field scale

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

Mapping and monitoring peatland conditions from global to field scale

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