Abstract
Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.
Highlights
Soil bulk density and carbon measurements are of relevance for many agricultural and environmental applications
The Landsat imagery was provided by the United States Geological Survey (USGS); Landsat-8 imagery was utilized for imagery corresponding to 2012 and after (i.e. 2017), and Landsat-5 imagery was utilized for previous years (i.e. 1984, 1995 and 2005)
These accuracies in terms of the coefficient of determination are comparable or exceed accuracies for Digital soil mapping (DSM) models reported in recent literature (Nussbaum et al, 2018), even for evaluations based upon model calibration (Mulder et al, 2016)
Summary
Soil bulk density and carbon measurements are of relevance for many agricultural and environmental applications. Diverse biotopes within a region such as a forest could reveal differing concentrations of soil carbon and bulk density; the generation of prediction maps of such properties are of interest. The variability of various soil properties over comparatively small spatial scales within certain environments, such as a forest consisting of different topographies and vegetation species, could render inaccurate results with conventional prediction maps based on kriging methodologies. DSM allows the extrapolation of soil properties for an area by recognizing environmental covariates as soil formation factors (Mulder et al, 2011) that over long time scales can affect the observed soil properties
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