Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data
Published in Geoderma 290 (2017) 91-99.
Initiated during 2015, Quentin Styc was a visiting intern student from France to our soil security laboratory for 3 months. I had this idea of spatial downscaling and trying to include observational data into the scheme. Quentin was happy to work on this little problem, and actually presented some preliminary results on this at the 2015 Pedometrics Conference. At the beginning of 2016, i was troubled by the fact that we were not taking into account the uncertainties of the map to be downscaled. Then Budiman Minansy suggested we should use a sequential Gaussian simulation approach, which more-or-less became generating random fields with some predefined spatial structure to add some deviation about the mean. Doing simulations of these random fields with followed up with spatial downscaling, and incorporation of observational data. Overall this paper represents a team effort, and a training opportunity for an up and coming soil scientists.
Read on for the journal 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 realizations 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.