Methods to interpolate soil categorical variables from profile observations : lessons fron Iran

Tha paper gives comparison of interpolation methods to produce soil-class maps from profile observations, given the large amount of auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. Four techniques have been considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and kappa statistics. Steps to improve interpolation of osil-class data, by considering the fuzziness of classes directly on the field and by improving the quality of input data, are further discussed.

HENGL Tomislav;  TOOMANIAN Norair;  REUTER Hannes Isaak;  MALAKOUTI Mohammad J.; 

2014-07-31

ELSEVIER SCIENCE BV

JRC31701

0016-7061,   

http://www.sciencedirect.com/science/article/pii/S0016706107001218#,    https://publications.jrc.ec.europa.eu/repository/handle/JRC31701,   

10.1016/j.geoderma.2007.04.022,