Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy

•Application of RUSLE3D and USPED over a large river basin in the Mediterranean climate•Retrieval of models' factors from ancillary data and remote sensing•Assessment of soil erosion rates and spatial patterns of erosion/deposition•Comparison of the models' spatial patterns of soil erosion

Soil erosion represents one of the most serious land degradation problems, and Mediterranean areas are particularly prone to this phenomenon. In the light of climate change and human-induced variations on the environment, the threat from soil erosion requires considerable attention. Over the last decades, estimation of soil erosion using empirical models has long been an active research topic. Nevertheless, their application over large areas is still a challenge due to data availability and quality. Successful monitoring can be realised with the integration of ancillary data and remote sensing products within a GIS environment.The main purpose of this research was to quantify the amount of soil erosion rate and to identify the spatial patterns of erosion and deposition in the large heterogeneous semi-agricultural Bradano River basin (southern Italy). This study presents the results of application of the Revised Universal Soil Loss Equation for Complex Terrain (RUSLE3D) and the Unit Stream Power-based Erosion Deposition (USPED) models. In order to appreciate the rate of soil loss and the spatial patterns of soil erosion and deposition, the two models were applied and compared through ancillary data and multi-temporal satellite imagery by GIS application.The analysis shows that the mean annual soil erosion rate ranges between 31 and 34 Mg ha− 1 year− 1. According to both models the 37% of the Bradano basin ranges between moderate to extreme soil erosion risk. The total amount of gross soil loss ranges between ~ 1.78 ∗ 106 Mg year− 1, as computed with the RUSLE3D, and ~ 2.10 ∗ 106 Mg year− 1, as computed with the USPED.The analysis and quantification of this phenomenon contribute to an understanding of applicability of those empirical models over large areas.