Abstract
Temperature affects many soil biochemical and geochemical processes. The growth of plants, seed germination, circulations of carbon and nitrogen are all significantly influenced by soil temperature, thus it is important to estimate the spatial pattern of soil temperature. This paper shows the results of spatial patterns of mean annual soil temperature interpolated from the measurements of 698 meteorological stations in China. Four geostatistical methods, ordinary kriging (OK), regression kriging with mean annual air temperature (RK-1), regression kriging with latitude, longitude and elevation (RK-2) and regression kriging with multi-auxiliary predictors (RK-3), were compared. Ordinary kriging (OK) directly interpolated the mean annual soil temperature data extracted from meteorological stations to obtain the spatial patterns of the mean annual soil temperature. For the three regression kriging methods, intensive auxiliary variables (mean annual air temperature, elevation, latitude and longitude), which were correlated with mean annual soil temperature, were used to increase the accuracy of estimation. The results suggested that RK-3 preformed best, followed by RK-1 and RK-2. The intensive data of auxiliary variables used in the regression kriging significantly improved the accuracy of interpolation results.
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