LAUSR

Abstract In a spatial mapping context, we address in this paper the issue of combining at best soil data coming from legacy soil surveys with soil information that is indirectly obtained from remote sensing (RS). We show first how spatial scale issues need to beproperly addressed for soil mapping in order to increase the predictive performances of spatial prediction models, where the two prediction techniques that are compared here are kriging and random forests (RF). By relying afterwards on a Bayesian data fusion approach, we then emphasize the benefit of combining the output of these two prediction models in order to get a single prediction result that leads to an improved final map. The advocated methodology is illustrated with the mapping of soil saturation percentage (SP) over a 10,480 km2 area located in Iran, were SP is a key soil parameter for sustainable development and land management. A set of 396 soil profiles were obtained from legacy soil surveys and were used to compute vertically averaged SP values. In parallel, a set of RS covariates were obtained from a 90 meters resolution Landsat image and a digital elevation model. Based on the modeling of the spatialdependence both for soil SP data and for RS covariates, it is shown how the relationship between them is improved by using a filtered kriging technique that allows us to focus on their long range component only. Using these filtered SP value and RS covariates, predictions were obtained separately from kriging and from a RF model at the nodes of a 90 meters resolution grid. Even if the performances of these two models are almost identical with R2 values equal to 0.66–0.67, it is shown afterwards that a Bayesian data fusion procedure that combines both predictions yields improved performance with a R2 value equal to 0.86. These results clearly emphasize the importance of properly addressing scale issues and the benefit of data fusion inorder to improve the quality of the final map. Though the study was conducted here for SP values, we believe this methodology and these findings are relevant when it comes to handle other soil properties in a spatial mapping context that involves several data sources.