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The paper evaluates spreading of observations in feature and geographical spaces as a key to sampling optimisation for spatial prediction by correlation with auxiliary maps. Although auxiliary data are commonly used for mapping soil variables, problems associated with the design of sampling strategies are rarely examined. When generalised least-squares estimation is used, the overall prediction error depends upon spreading of points in both feature and geographical space. Allocation of points uniformly over the feature space range proportionally to the distribution of predictor (equal range stratification, or ER design) is suggested as a prudent sampling strategy when the regression model between the soil and auxiliary variables is unknown. An existing 100-observation sample from a 50 by 50 km soil survey in central Croatia was used to illustrate these concepts. It was re-sampled to 25-point datasets using different experimental designs: ER and 2 response surface designs. The designs were compared for their performance in predicting soil organic matter from elevation (univariate example) using the overall prediction error as an evaluation criterion. The ER design gave overall prediction error similar to the minmax design, suggesting that it is a good compromise between accurate model estimation and minimisation of spatial autucorrelation of residuals. In addition, the ER design was extended to the multivariate case. Four predictors (elevation, temperature, wetness index, and NDVI) were transformed to standardised principal components. The sampling points were then assigned to the components in proportion to the variance explained by a principal component analysis and following the ER design. Since stratification of the feature space results in a large number of possible points in each cluster, the spreading in geographical space can also be maximised by selecting the best of several realisations. Additional keywords: response surface design, soil survey, general least squares, feature space, principal components., Introduction A sampling design in soil survey specifies which points, transects, or areas will be visited for field measurements or observations. Sampling incorporates concepts of survey intensity, spatial variability, and [...]

A method to enhance manual landform delineation using photo-interpretation to map a larger area is described. Conventional aerial photo-interpretation (API) maps using a geo-pedological legend of 21 classes were prepared for six sample areas totaling 111 [km.sup.2] in the Baranja region, eastern Croatia. Nine terrain parameters extracted from a digital elevation model (DEM) (ground water depth, slope, plan curvature, profile curvature, viewshed, accumulation flow, wetness index, sediment transport index, and the distance to nearest watercourse) were used to extrapolate photo-interpretation over the entire survey area (1062 [km.sup.2]). The classification accuracy was assessed using the error matrix, calculated by comparing both the whole API maps and point samples, with the results of classification. The first results, using a maximum-likelihood classifier, were 58.2% (hill land), 39.1% (plain), and 45.3% (entire area) reproducibility of the training set. Six classes in the plain were responsible for a large proportion of the misclassifications, due to an insufficiently detailed DEM and the complex nature of landforms (point bar complexes, levees, active channel banks), which cannot be explained with the terrain parameters only. Reproducibility for a simplified legend of 15 classes over the study area was improved to 65.8% (plain), 58.2% (hill land), and 63.4% (entire area) using the whole-API training set. After the simplification of legend (15) and with the iterative (3) selection of pointsample training set, classification was able to reproduce 97.6% (hill land), 86.7% (plain), and 90.2% (entire area) of the training set. The supervised classification showed fine details not achieved by photo-interpretation. The number of manual photo-interpretations that had to be prepared was reduced from 84 to 6. The methodology can be applied by soil survey teams to edit and update current maps and to enhance or replace API for new surveys.