Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data

Abstract In this paper a spatial downscaling method is explored for generating appropriate farm scale digital soil maps. The digital soil map product to be downscaled is an Australian national extent soil carbon map (100 m grid resolution). Taking into account the associated prediction uncertainties of this map, we used a simulation approach based on Gaussian random fields to generate plausible mapping realisations that were in turn downscaled to 10 m resolution for a farm in North-western NSW, Australia. We were able to derive both a downscaled map of soil carbon and associated prediction variance with this approach. Building further upon this development, we then incorporated a bias correction step into the spatial downscaling procedure which permits the inclusion of field observations as a way to moderate the downscaling results to better reflect actual conditions on the ground. Based on an independent validation dataset, it was found that incorporating field observations increase the concordance correlation coefficient to 0.8 from 0.2. This relatively lower correlation achieved using spatial downscaling alone was due to the national scale mapping for the study area being positively biased in the area of interest. It was found that downscaling that incorporates observational data was marginally better if not comparable to using a point-based digital soil mapping approach. The advantage of spatial downscaling is that it can be implemented in situations of data scarcity. This will be ideal for on farm soil monitoring in situations where detailed soil mapping is initially not available. For example, soil carbon auditing schemes requiring prior soil information for implementation of design-based soil sampling could potentially be universally applied with such a spatial downscaling approach.