Multi-stage soil surveying complementing statistical sampling designs to provide high-resolution soil maps for policy

In Switzerland, detailed soil information is missing for many regions although urgently needed. Soon, authorities will need to legally delineate areas for the high-quality arable land inventory based on high-resolution surveys that require sampling of large numbers of new locations within the next decade. Consequently, cost and time efficient surveying strategies are required which fulfil high quality demands for legally binding decisions. The soil functional evaluation used in these decisions requires soil attributes which cannot only be derived by objective laboratory measurement but are partly based on pedological field description by experts. In our study area of about 1000 hectares, we established first a feature space coverage sampling design to sample 1500 locations based on elevation and land use data, geological information, and expert knowledge of soil scientists. 170 locations were determined by a stratified random sampling design and used for independent validation of mapping results. We predicted a large range of soil attributes (clay, silt, humus, pH, moisture regime, rootable soil depth) in multiple depth (0-20 cm, 0-30 cm, 20-30 cm, 30-50 cm, 50-100 cm), using the first 1200 samples, random forest, and a wide range of environmental covariates. The predicted spatial information showed low to medium accuracy and maps exhibited further deficiencies, particularly poor performance for values at the tail of the soil attribute distributions. By visual inspection of prediction interval maps we found high model uncertainties in some specific areas like geological transition zones and anthropogenic altered zones through drainage and covering layers. To improve the quality of the maps we increased the total number of sampling locations up to 2200 by two in-fill sampling design strategies in two zones: To complement the feature space coverage sampling design potentially based on incomplete environmental factors, experienced surveyors directly added additional sampling locations based on their expert knowledge. Those covered landscape features not contained in the primary sampling design such as local extrema or transition zones. In the second zone a two-level infill sampling design was created. A first general level complemented the feature space further as spanned in the initial sampling design. The second level consisted of additional 250 sampling points within zones of high model uncertainties. Subsequently generated maps showed increased accuracy with increasing sampling density for most attributes, e.g., an increase of 0.1 for the clay content in the topsoil at a sampling density of 1.7 observations per ha compared to a sampling density of 1.1 Our results further displayed increasing accuracy of 0.05 with higher-weighted data collected by experts and simultaneous lowering the sampling density to 1.5 per ha by ignoring data with the lowest quality, collected before the internal calibration and synchronisation of the field survey.To upscale the soil mapping in such high resolution and with expert-based parameters it is crucial to have synchronized data in high and stable quality across different soil formation regions.