Knowledge of the spatial variation of soil infiltration is necessary for managing water conservation, salinity, and precision agriculture in drylands. In this study, the spatial variation of soil infiltration was investigated using digital soil mapping methods in the Sistan plain, an arid, low-relief flood plain in eastern Iran where a large irrigation project is being implemented to irrigate lands. Information about the spatial variation in soil infiltration will assist the planning of this irrigation project. 138 sampling locations were selected using stratified sampling based on existing polygon-based soil maps. Steady state soil infiltration was measured at each sampling location using the double ring infiltrometer method. Twenty-three environmental covariates were derived from digital elevation models and satellite imagery as well as predictive maps of clay, sand, and silt that were derived from kriging the collected soil samples. A simple (multiple linear regression) and a complex (random forests) model were used to link covariates and infiltration measurements. Ten-fold cross-validation was used to determine model accuracy. Measured soil infiltration ranged from 0.29 to 81.7 mm h⁻¹ with a mean of 13.6 mm h⁻¹. RMSE of the infiltration rate predictions were 13.4 mm h⁻¹ for random forest and 13.9 mm h⁻¹ for multiple linear regression. MAE was 10.5 for random forest and 10.9 for multiple linear regression. The most important covariates were channel networks, sand concentration, normalized difference salinity index (NDSI), and elevation in the random forest model and distance-from-river and sand concentration in the multiple linear regression model. Accuracy metrics for both models were comparable, but the random forest predictions were judged to be closer to reality based on visual review, thus random forests was chosen to make predictive maps of soil infiltration.

了解土壤入渗的空间变化对于管理干旱地区的节水，盐分和精细农业十分必要。在这项研究中，使用数字土壤测绘方法调查了伊朗东部干旱，低浮洪平原锡斯坦平原的土壤入渗的空间变化，该地区正在实施大型灌溉工程来灌溉土地。有关土壤渗透的空间变化的信息将有助于该灌溉项目的规划。基于现有的基于多边形的土壤图，使用分层采样选择了138个采样位置。使用双环渗透仪在每个采样位置测量稳态土壤入渗。23个环境协变量来自数字高程模型和卫星图像，以及从克里格采集的土壤样本中推导出的粘土，沙子和粉沙的预测图。一个简单的模型（多元线性回归）和一个复杂的模型（随机森林）被用来链接协变量和入渗量。十倍交叉验证用于确定模型的准确性。测得的土壤入渗量为0.29至81.7 mm h^-1，平均13.6 mm h ^-1。渗透速率的预测RMSE分别为13.4毫米高^-1随机森林和13.9毫米高^-1为多元线性回归。随机森林的MAE为10.5，多元线性回归的MAE为10.9。最重要的协变量是渠道网络，沙浓度，归一化差异盐度指数（NDSI），随机森林模型中的海拔和距河流的距离以及多元线性回归模型中的沙浓度。两种模型的准确度指标均具有可比性，但是根据视觉评估，随机森林的预测被认为更接近于现实，因此选择了随机森林作为土壤入渗的预测图。