The use of geomorphometrical parameters derived from DEMs to estimate soil properties is
a succes... more The use of geomorphometrical parameters derived from DEMs to estimate soil properties is a successful approach for digital soil mapping. The use of regression kriging is an efficient method, because represent an averaged estimate that best fit the existing soil data (multiple regression), with the correction of residuals kriging. The method gave best results for a medium scale of digital soil mapping and for zonal soils that correlate better with the landforms from the studied area. The regression kriging estimations of Romanian soil data from Iasi County were used to develop the digital soil map using the criteria from the Romanian Soil Taxonomy System. The validity of the method was checked by keeping from the regression, test points and by comparing the results with 5k‐10k soil maps of the area. Although the statistical significance of the regression approach is poor, the comparison with existing soil maps show that the DSM approach can be adequately at some extent, and this comparison can be used to evaluate the DSM approach.
The Romanian Soil Survey System does not imply, up to the present date, the use of digital method... more The Romanian Soil Survey System does not imply, up to the present date, the use of digital methods in representing field campaign results or for mapping soil parameters. The presented study tests several geostatistical methods to model some soil parameters (soil pH and topsoil humus content), mainly in order to observe the differences induced by the scale of the approach and to test existing data. In this respect, three differently dimensioned analysis scales were chosen, all parts of the same larger region, located in Iaşi County. On the chosen areas the main three categories of methods used in pedometrics were tested: methods of the kriging family (ordinary kriging, cokriging), regression methods applied both globally and locally (Geographically Weighted Regression) and the combined approach of regression-kriging respectively. In order to test the results were used crossvalidation and independent sample validation. The root mean square error (RMSE) was used as selection criteria f...
Communications in Soil Science and Plant Analysis, 2013
ABSTRACT This study compares the performance of several statistical methods (multiple linear regr... more ABSTRACT This study compares the performance of several statistical methods (multiple linear regression, analysis of covariance, geographically weighted regression, regression kriging, and ordinary kriging) for deriving spatial models of soil parameters. The applications were carried out within a 186-km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of approximately 180 soil profiles, scattered in the eastern half of the basin. Two independent samples, each of 50 soil profiles, were used for validation inside (interpolation) and outside (extrapolation) the main sampling area. The predictors included X and Y coordinates of soil profiles, geomorphometrical parameters (altitude, slope, aspect, wetness index, terrain curvature), climate parameters (mean annual temperatures, precipitation, global radiation), the normalized difference vegetation index, the main soil types, land use, and surface lithology. For only three soil variables the geostatistical approach proved to be useful: occurrence depth of calcium carbonates, pH, and base saturation. The best spatial models were achieved using analysis of covariance, geographically weighted regression, and ordinary kriging. The most relevant continuous predictor is the mean annual precipitation, whereas the most relevant qualitative factor is the soil type.
The use of geomorphometrical parameters derived from DEMs to estimate soil properties is
a succes... more The use of geomorphometrical parameters derived from DEMs to estimate soil properties is a successful approach for digital soil mapping. The use of regression kriging is an efficient method, because represent an averaged estimate that best fit the existing soil data (multiple regression), with the correction of residuals kriging. The method gave best results for a medium scale of digital soil mapping and for zonal soils that correlate better with the landforms from the studied area. The regression kriging estimations of Romanian soil data from Iasi County were used to develop the digital soil map using the criteria from the Romanian Soil Taxonomy System. The validity of the method was checked by keeping from the regression, test points and by comparing the results with 5k‐10k soil maps of the area. Although the statistical significance of the regression approach is poor, the comparison with existing soil maps show that the DSM approach can be adequately at some extent, and this comparison can be used to evaluate the DSM approach.
The Romanian Soil Survey System does not imply, up to the present date, the use of digital method... more The Romanian Soil Survey System does not imply, up to the present date, the use of digital methods in representing field campaign results or for mapping soil parameters. The presented study tests several geostatistical methods to model some soil parameters (soil pH and topsoil humus content), mainly in order to observe the differences induced by the scale of the approach and to test existing data. In this respect, three differently dimensioned analysis scales were chosen, all parts of the same larger region, located in Iaşi County. On the chosen areas the main three categories of methods used in pedometrics were tested: methods of the kriging family (ordinary kriging, cokriging), regression methods applied both globally and locally (Geographically Weighted Regression) and the combined approach of regression-kriging respectively. In order to test the results were used crossvalidation and independent sample validation. The root mean square error (RMSE) was used as selection criteria f...
Communications in Soil Science and Plant Analysis, 2013
ABSTRACT This study compares the performance of several statistical methods (multiple linear regr... more ABSTRACT This study compares the performance of several statistical methods (multiple linear regression, analysis of covariance, geographically weighted regression, regression kriging, and ordinary kriging) for deriving spatial models of soil parameters. The applications were carried out within a 186-km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of approximately 180 soil profiles, scattered in the eastern half of the basin. Two independent samples, each of 50 soil profiles, were used for validation inside (interpolation) and outside (extrapolation) the main sampling area. The predictors included X and Y coordinates of soil profiles, geomorphometrical parameters (altitude, slope, aspect, wetness index, terrain curvature), climate parameters (mean annual temperatures, precipitation, global radiation), the normalized difference vegetation index, the main soil types, land use, and surface lithology. For only three soil variables the geostatistical approach proved to be useful: occurrence depth of calcium carbonates, pH, and base saturation. The best spatial models were achieved using analysis of covariance, geographically weighted regression, and ordinary kriging. The most relevant continuous predictor is the mean annual precipitation, whereas the most relevant qualitative factor is the soil type.
Uploads
a successful approach for digital soil mapping. The use of regression kriging is an efficient
method, because represent an averaged estimate that best fit the existing soil data (multiple
regression), with the correction of residuals kriging. The method gave best results for a medium
scale of digital soil mapping and for zonal soils that correlate better with the landforms from
the studied area. The regression kriging estimations of Romanian soil data from Iasi County
were used to develop the digital soil map using the criteria from the Romanian Soil Taxonomy
System. The validity of the method was checked by keeping from the regression, test points and
by comparing the results with 5k‐10k soil maps of the area. Although the statistical significance
of the regression approach is poor, the comparison with existing soil maps show that the DSM
approach can be adequately at some extent, and this comparison can be used to evaluate the
DSM approach.
a successful approach for digital soil mapping. The use of regression kriging is an efficient
method, because represent an averaged estimate that best fit the existing soil data (multiple
regression), with the correction of residuals kriging. The method gave best results for a medium
scale of digital soil mapping and for zonal soils that correlate better with the landforms from
the studied area. The regression kriging estimations of Romanian soil data from Iasi County
were used to develop the digital soil map using the criteria from the Romanian Soil Taxonomy
System. The validity of the method was checked by keeping from the regression, test points and
by comparing the results with 5k‐10k soil maps of the area. Although the statistical significance
of the regression approach is poor, the comparison with existing soil maps show that the DSM
approach can be adequately at some extent, and this comparison can be used to evaluate the
DSM approach.