A generic framework for spatial prediction of soil variables based on regression-kriging

Abstract

A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets. D

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

“…This suggests that the other soil variables could have been better predicted by cokriging but only a few covariates had the necessary spatial configurations to meet the assumptions of this method (for example, NDVI derived from RapidEye). These results are in accordance with other studies [45,46] showing that there is no single best prediction method for all soil attributes. Cokriging and RK, despite having potential to improve the predictions, are more complex methods, and thus, the extra effort for their implementation must be taken into consideration.…”

Combining Satellite and Geospatial Technologies for Exploring Rainstorm Hazard Over Mediterranean Central Area

“…In this way, Indicator CoKriging (ICK) is able to take into account both the information to be processed together, and then used in the ordinary cokriging equations (Goovaerts, 1997;Johnston et al, 2001). To account for both categorial (RHI) and continuous (satellite data), we used standardized variables to produce composite indices compatible to indicator cokriging (Johnston et al, 2001;Hengle et al, 2004). So that, both covariance and cross-covariance functions were applied on the above standardized primary and auxiliary variables for incorporating exhaustively sampled satellite data using the indicator datum that is collocated with the location being estimated.…”

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

“…Data mining techniques have been successfully used to model and predict the spatial variability of soil properties (Rossel and Behrens, 2010;Hengl et al, 2017;Shangguan et al, 2017) and generate country-specific SOC maps (Viscarra Rossel et al, 2014;Adhikari et al, 2014). The combination of regression modeling approaches with geostatistics of model residuals (i.e., regression Kriging) is a combined strategy that 30 has been widely used to map SOC (Hengl et al, 2004;Mishra et al, 2009;Marchetti et al, 2012;Kumar et al, 2012;Peng et al, 2013;Adhikari et al, 2014;Yigini and Panagos, 2016;Nussbaum et al, 2014;Mondal et al, 2017). Machine learning algorithms such as random forests or support vector machines have also been used to increase statistical accuracy of soil 3 SOIL Discuss., https://doi.org /10.5194/soil-2017-40 Manuscript under review for journal SOIL Discussion started: 25 January 2018 c Author(s) 2018.…”

“…Appendix A: Brief description of implemented methods RK is a hybrid model with both, a deterministic and a stochastic component (Hengl et al, 2004). The regression part took form of a step-wise (back and forward) multiple linear regression to avoid statistical redundancy among the best prediction factors.…”