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Regression-kriging (RK) is becoming an important tool in geostatistics because of the availability of many covariates at high spatial resolution with the advancement in proximal and remote-sensing with positioning technologies. This paper presents the application of a new local RK algorithm for prediction of soil properties. The algorithm was tested using 985 observations for prediction of soil pH,...
Spatial transitions between slope positions (landform positions) are often gradual. Various methods have been developed to quantify the transitions using fuzzy slope positions. However, few studies have used the quantitative information on fuzzy slope positions in digital soil mapping or other terrain-related geographic modeling. This paper examines the use of such information for mapping soil organic...
The development of subsoil models represents an important aspect of land resource evaluation, because they can provide an accurate description of the spatial variability in soil properties. Although direct soil sampling provides the best information in terms of soil properties, sample density is rarely adequate to accurately describe the horizontal and vertical variability of the physical properties...
The unique selling point of pedometricians still is close to their cradle: ability to map. Irrespective of the many scientific achievements one can ask if offering mapping ability, allbeit in various contexts, is all a pedometrician can do to bring forward soil science's broader agenda. This paper identifies, within some of the hotter issues on the soil science agenda, activities that need the input...
Soil organic matter (SOM) is one of the most important indicators of the soil quality. Accurate information about the spatial variation of SOM is critical to sustainable soil utilization and management. Although utilizing spatially correlated auxiliary information to improve the prediction accuracy of soil properties has been widely recognized in pedometrics, not all studies have taken account of...
Digital soil mapping can be helpful in providing pedological information over wide areas where existing soil information is limited. The aim of this study was to predict soil properties at a regional scale by parametrizing soil-landscape models using a machine-learning method recently applied to soil science concerns: boosted classification and regression trees. To examine soil properties interdependence,...
Fuzzy c-means clustering (FCM) has been used frequently in digital soil mapping. One of the key issues in applying FCM is the determination of the appropriate classification parameters of the fuzzy exponent (m) and the number of clusters (c). To determine the optimal selection of appropriate m and c values, in this study, we first used two simulated datasets to demonstrate the sensitivity of three...
In low relief areas such as plains, easily obtained soil forming factors generally do not co-vary with soil conditions over space to the level that they can be used effectively in digital soil mapping. Mapping variation of soil properties over such areas remains a challenge. This paper presents an approach to mapping soil texture using environmental covariates derived from temporal responses of the...
In large parts of the western coastal region of the Netherlands peat soils occur. Historic land use resulted in decomposition of organic matter and compaction of these soils and as a consequence the land surface subsided. Along with sea level rise and tectonic subsidence since the end of the Weichselian ice-age, this resulted in a country where one-third of the land lies below mean sea level. Global...
We developed a multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate modelling in China. The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles...
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