Predicting and delineating soil temperature regimes of China using pedotransfer function

Abstract

Soil temperature regime (STR) is important for soil classification and land use. Generally, STR is delineated by estimating the mean annual soil temperature at a depth of 50 cm (MAST50) according to the Chinese Soil Taxonomy (CST). However, delineating the STR of China remains a challenge due to the difficulties in accurately estimating MAST50. The objectives of this study were to explore environmental factors that influence the spatial variation of MAST50 and generate an STR map for China. Soil temperature measurements at 40 and 80 cm depth were collected from 386 National Meteorological Stations in China during 1971–2000. The MAST50 was calculated as the average mean annual soil temperature (MAST) from 1971–2000 between 40 and 80 cm depths. In addition, 2 048 mean annual air temperature (MAAT) measurements from 1971 to 2000 were collected from the National Meteorological Stations across China. A zonal pedotransfer function (PTF) was developed based on the ensemble linear regression kriging model to predict the MAST50 in three topographic steps of China. The results showed that MAAT was the most important variable related to the variation of MAST50. The zonal PTF was evaluated with a 10% validation dataset with a mean absolute error (MAE) of 0.66°C and root mean square error (RMSE) of 0.78°C, which were smaller than the unified model with MAE of 0.83°C and RMSE of 0.96°C, respectively. This study demonstrated that the zonal PTF helped improve the accuracy of the predicted MAST50 map. Based on the prediction results, an STR map across China was generated to provide a consistent scientific base for the improvement and application of CST and land use support.

Keywords

• soil temperature
• soil temperature regimes
• soil taxonomy
• pedotransfer function