Soil erosion prediction and conservation planning require detailed soil data under different environmental conditions. When such data are needed at the field scale, aspatial and spatial models could be used to predict soil erosion processes. This study was conducted to develop spatial models including geostatistical models (i.e. ordinary kriging (OK) and cokriging (CK)) and hybrid geostatistical models (i.e. multiple linear regression-kriging (MLRK) and artificial neural network-kriging (ANNK)) for estimating WEPP baseline soil sensitivity to erosion parameters for calcareous agricultural soils in northwest Iran. Inter-rill and rill erosion simulation experiments were carried out at 100 locations at the field scale with 3 replications. At each location, the soil properties (organic matter, calcium carbonate equivalent, sand, silt, clay, base infiltration rate) were measured and auxiliary data obtained from attributes derived from digital elevation models (elevation, slope, stream power index, wetness index and sediment transport index) and remote sensing data (three visible bands, NIR, SWIR (5 and 7 bands), NDVI indices). The MLR and ANN models were used to estimate baseline inter-rill soil sensitivity to erosion (K_ib) and rill soil sensitivity to erosion (K_rb and τ_cb) using two types of input data. For the first type of prediction models (type I), the measured soil properties and auxiliary data were used, whereas for the second type of models (type II), principal components (PCs) based on the soil and auxiliary data were used. In comparison to the models that were developed here, the WEPP inter-rill and rill soil sensitivity to erosion models showed a relatively poor performance. The ANN models developed here predicted K_ib, K_rb and τ_cb parameters better than the MLR models using both types of data (type I and II). Moreover, the results indicated that the ANNK model was the most appropriate spatial or hybrid model for predicting soil sensitivity to erosion parameters.

水土流失预测和保护规划需要在不同环境条件下提供详细的土壤数据。当在田间规模上需要此类数据时，可以使用空间和空间模型来预测土壤侵蚀过程。进行这项研究是为了开发空间模型，包括用于估计的地统计学模型（即普通克里金法（OK）和协同克里金法（CK））和混合地统计学模型（即多元线性回归克里金法（MLRK）和人工神经网络克里金法（ANNK））。 WEPP基准土壤对伊朗西北部钙质农业土壤侵蚀参数的敏感性。在田间规模的100个位置进行了钻孔间和钻孔侵蚀模拟实验，重复了3次。在每个位置的土壤特性（有机物，碳酸钙当量，沙子，淤泥，粘土，测量基础渗透率，并从数字高程模型（高程，坡度，水流功率指数，湿度指数和沉积物迁移指数）和遥感数据（三个可见波段，NIR，SWIR（5和7个波段） ），NDVI指标）。使用MLR和ANN模型估算基线间土壤间土壤对侵蚀的敏感性（K_ib）和小溪土壤对侵蚀的敏感性（K _rb和_τcb），使用两种类型的输入数据。对于第一种类型的预测模型（I型），使用了测得的土壤性质和辅助数据，而对于第二种类型的模型（II型），则使用了基于土壤和辅助数据的主成分（PC）。与此处开发的模型相比，WEPP钻孔间和钻孔土壤对侵蚀模型的敏感性表现相对较差。在此开发的ANN模型可预测K _ib，K _rb和_τcb使用两种数据类型（I和II）的参数都比MLR模型更好。此外，结果表明，ANNK模型是最适合预测土壤对侵蚀参数敏感性的空间或混合模型。