Abstract
Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping.
Highlights
Under natural conditions, peatlands constitute carbon sinks
The confidence interval (CI) for peat thickness predictions are much smaller for 4mHCP and 4mHCP plus Digital Elevation Model (DEM) models (Figure 14a,c) than for all coils and all coils plus DEM models (Figure 14b,d). These results suggest that the single-coil predictions, using 4mHCP with or without the DEM as an additional predictor, provided the most reliable results in our case, which can be further used for decision-making; for example, in the case of peatland restoration
The present case study demonstrated that data collected with a single-frequency multi-receiver electromagnetic induction (EMI) instrument enabled the non-invasive and reliable prediction of peat thickness over a 10 ha field
Summary
Peatlands constitute carbon sinks. they have been used both for fuel mining and agriculture for over 1000 years. Several methods have been used to measure peat thickness (or depth to mineral layer) in the field. In different Digital Soil Mapping (DSM) studies, models were derived to predict peat thickness using conventional field observations and environmental data. Considering environmental data, a few studies mostly used terrain attributes, including elevation and slope [2,3], only elevation [4] or the distance to a river [5]. Rudiyanto et al [6] (2016) and Young et al [9] (2018) built models using a relatively limited amount of environmental data (i.e., elevation, slope, aspect, System of Automated Geoscientific Analyses Wetness Index (SAGAWI) and nearest distance to river for the first study, and elevation, slope, aspect, vegetation type and soil for the latter). Rudiyanto et al [8] (2018) and Aitkenhead [7] (2017) used a wide array of environmental covariates: topography (i.e., elevation, vegetation-corrected elevation, and two derived terrain attributes—the Multi-Resolution Index of Valley Bottom Flatness (MRVBF) and SAGAWI—Euclidean distances to rivers, seas and combined rivers and seas, radar images (i.e., Sentinal-1A and ALOS-PALSAR) and vegetation (i.e., seven Landsat raw bands and the normalized difference vegetation index) in Rudiyanto et al [8] (2018), and topography (i.e., elevation and seven derived terrain attributes), climate (i.e., 24 different meteorological layers), soil (i.e., land cover, geology and soil maps) and vegetation (i.e., Landsat raw bands and derived vegetation indices) in Aitkenhead [7] (2017)
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