This study examines the spatial patterns and accuracies of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging approaches excelled at internal spatial prediction with more densely spaced sample points.
Categories of cartographic scale correspond to the selection of environmental soil predictors used to initially create historical soil maps. Paradigm shifts in soil mapping and classification can be best explained by not only their correlation to historical improvements in scientific understanding, but also by differences in purpose for mapping, and due to advancements in geographic technology. Although the hierarchy of phenomena scales observed in this study is generally known in pedology today, it also represents a new view on the evolution of soil science.
Soil mapping, classification, and pedologic modelling have been important drivers in the advancement of our understanding of soil. Advancement in one of these highly interrelated areas tend to lead to corresponding advances in the others. Traditionally, soil maps have been desirable for purposes of land valuation, agronomic planning, and even in military operations. The expansion of the use of soil knowledge to address issues beyond agronomic production, such as land use planning, environmental concerns, energy security, water security, and human health, to name a few, requires new ways to communicate what we know about the soils we map as well as bringing forth research questions that were not widely considered in earlier soils studies.
The objective of this study was to evaluate the ability of high-resolution, minimally invasive sensor data to predict spatial variation of soil organic carbon stocks within highly degraded peatland soils in northeast Germany. Soil organic carbon density was related to elevation, electrical conductivity, and peat thickness. Modeling peat thickness based on sensor data needs additional research, but seems to be a valuable set of covariates in digital soil mapping.
After soil science became established as a scientific discipline, there has been a continued interplay between geologists and soil scientists, both fields benefiting from advancements made by the other. There is strong agreement between preliminary geology maps created from soil maps and traditional geology maps. Despite the results obtained when using soil maps to create surficial geology maps, there is a need for more quantitative studies to assess the degree of compliment between soil-based maps and traditional geology maps, expansion of the technique into a wider range of geologic and climatic environments, and more research in locations that use classification systems other than Soil Taxonomy.
A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m‾²), covering an area of 122 km², with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.
Classification of elevation rasters with this digital model of hillslope position represent base maps that can be used to (1) improve research on toposequences by providing explicit definitions of each hillslope element’s location, (2) facilitate the disaggregation of soil map unit complexes, and (3) identify map unit inclusions that occur due to subtle topographic variation.
Simulation of late glacial atmospheric conditions with atmospheric general circulation models suggest a strong anticyclone over the Laurentide Ice Sheet and associated easterly winds along the glacial margin. In the upper Midwest of North America, evidence supporting this modeled air flow exists in the orientation of paleospits in northeastern Lower Michigan that formed ∼13 ka in association with glacial Lake Algonquin. Conversely, parabolic dunes that developed between 15 and 10 ka in central Wisconsin, northwestern Indiana, and northwestern Ohio resulted from westerly winds, suggesting that the wind gradient was indeed tight.
Despite the widespread availability of relatively detailed soil maps in the USA, few areas have a surficial geology map published with as much spatial detail. This apparent gap between disciplines calls to question the accuracy of soil maps to represent the spatial distribution of surficial geologic materials. Therefore, the purpose of this research was to test the agreement between maps from these two sources.
Results suggest that models with limited predictor pools can substitute other predictors to compensate for unavailable variables. However, a better performing model was always found by considering predictor variables at multiple scales. Although the scale effect of the modifiable area unit problem is generally well known, this study suggests digital soil mapping efforts would be enhanced by the greater consideration of predictor variables at multiple analysis scales.