[Precision of spatial interpolation for forest duff layer depth based on secondary variable].

Based on geostatistical method, three algorithms of spatial interpolation with elevation as a secondary variable, i.e., simple kriging with varying local means (SKlm), kriging with an external drift (KED), and cokriging (COK), were used to calculate the precision of spatial interpolation for the forest duff layer depth, and cross validation was conducted. The results showed that among the three algorithms, KED gave the highest precision because of its taking into account both the spatial variation among variables and the factors affecting local spatial change, SKlm did not yield expected precision because of the weaker correlation between elevation and forest duff layer depth, while COK directly used the variable elevation to estimate forest duff layer depth but many unexpected results yielded for the boundary area due to insufficient samplings. Comparing with the method of inverse distance weighting (IDW), only KED had a higher precision of interpolation, while for SKlm and COK, their interpolation precision was lower, suggesting that when a secondary variable was used for geostatistical interpolation, the correlation between primary and secondary variables was of significance in increasing the precision of interpolation.