Comparison of a spatial, spatial and hybrid methods for predicting inter-rill and rill soil sensitivity to erosion at the field scale

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 tau(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 tau(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.