Soil thermal conductivity is one of the main factors influencing the soil temperature regime. Over the recent years, by developing remote sensing technology, topographic attributes such as elevation, slope and aspect have become accessible from digital elevation models (DEMs). Based on the interactions between soil properties and topographically heterogeneous landscapes, this research investigates the application of topographical attributes (geographical and satellite information), along with soil physical properties, in order to create pedotransfer functions (PTFs) and estimate soil thermal conductivity, not reported so far. The hydrology experiment database of the Southern Great Plains 1997 (SGP97) available online at (http://www.cei.psu.edu/nasa_lsh/), was used to obtain these functions through applying linear regression, artificial neural networks (ANNs) and support vector machine methods. Twelve PTFs (PTF_1-12) were developed to predict the thermal conductivity using different combinations of soil physical properties and topographical attributes. Improvement was attained via a combination of soil physical properties and topographical attributes (PTF_5-11) rather than soil physical properties alone (PTF_2-4). Thermal conductivity was better estimated by use of east-west slope degree, north-south slope degree and saturated water content along with texture fractions and bulk density as predictors in PTF₇. The root mean square error decreased from 0.266 and 0.217 in PTF₁ to 0.217 and 0.157 in PTF₇ in the training and testing steps, respectively. ANNs were the best performing method with the relative improvement ranging from 3.52 to 19.35% and from 14.00 to 28.00% in the training and testing steps, respectively. The results displayed the successful estimation of the thermal conductivity in the training and testing steps, applying a combination of soil physical and topographical attributes.

土壤热导率是影响土壤温度状况的主要因素之一。近年来，通过开发遥感技术，可以从数字高程模型（DEM）中访问诸如高程，坡度和坡度之类的地形属性。基于土壤特性与地形异质性景观之间的相互作用，本研究调查了地形特性（地理和卫星信息）以及土壤物理特性的应用，以便创建pedotransfer函数（PTF）并估算土壤热导率（未报告）至今。可以从（http://www.cei.psu.edu/nasa_lsh/）在线获得的南部大平原1997年水文实验数据库，通过应用线性回归获得这些功能，人工神经网络（ANN）和支持向量机方法。十二个PTF（PTF_（1-12）被开发以使用土壤物理性质和地形属性的不同组合来预测热导率。通过结合土壤物理性质和地形属性（PTF _5-11）而不是仅通过土壤物理性质（PTF _2-4）获得了改善。通过使用东西向倾斜度，南北向倾斜度和饱和水含量以及质地分数和堆积密度作为PTF _7的预测指标，可以更好地估算导热系数。均方根误差从PTF _1中的0.266和0.217降低到PTF _7中的0.217和0.157分别在培训和测试步骤中。人工神经网络是表现最好的方法，在训练和测试步骤中的相对改进分别为3.52至19.35％和14.00至28.00％。结果表明，结合了土壤物理和地形属性，可以成功地在训练和测试步骤中估算热导率。