Parent material is a crucial co-variate in predicting soil properties using digital soil mapping (DSM) methods. This spatial information can be obtained using available lithology maps, or using proxies such as gamma-ray spectroscopic maps. In this study, we used random forests to predict topsoil texture (clay, silt, and sand in grams per kilogram) in a French sub-region using a high density of soil measurements and available co-variates including climate, topography, land use, and satellite data. Then, we tested the value of adding a lithology map at the 1:50,000 scale and/or an airborne gamma-ray spectroscopy map in a French region characterised by a considerable contrast in geology and lithology. We showed that adding airborne gamma-ray spectroscopic data substantially increased the indicators of prediction performance and led to less noisy and more interpretable maps for this region. These results suggest that airborne gamma-ray spectroscopy can be a very useful co-variate to predict these topsoil properties.

母体材料是使用数字土壤映射（DSM）方法预测土壤性质的关键协变量。可以使用可用的岩性图或使用诸如伽马射线光谱图的代理来获得该空间信息。在这项研究中，我们利用高密度的土壤测量数据和可用的协变量（包括气候，地形，土地利用，和卫星数据。然后，我们测试了在法国地区以地质和岩性形成明显对比的方式添加1：50,000比例尺的岩性图和/或机载伽马射线能谱图的价值。我们显示，添加机载伽玛射线光谱数据会大大提高预测性能的指标，并导致该区域的噪点更少且地图的解释性更高。这些结果表明，机载伽马射线能谱法可以作为预测这些表土特性的非常有用的协变量。