Soil is one of the most important natural resources of any country and is the most important element of the Earthʹs biosphere. Soil water erosion causes the release of surface soil nutrients and reduces soil fertility, as well as, reducing the water holding capacity of the soil and the instability of the soil structure and blockage of their surface. Therefore, recognizing the factors affecting soil erosion, especially water erosion, and measuring its amount in watersheds is essential to use it in sustainable development. This research has been done by studying the factors affecting the destruction of production resources, especially the soil that is the bedrock of production. In this study, the capability of Geographic Information System (GIS) and Remote Sensing (RS) to eva‎luate water erosion risk and determine the amount of soil loss using ICONA and RUSLE erosion models in a case study in Armand region in Chaharmahal and Bakhtiari province was investigated. First, satellite images were preprocessed using remote sensing techniques, information layers were prepared from field data using GIS, and then the information layers attributed to the models were prepared and then the factors were eva‎luated. Finally, by preparing the basin erosion map, the amount of soil erosion was estimated using the modified global soil loss model. For ICONA model, water erosion risk assessment was performed using Landsat 8 satellite data related to 2020 to determine the effect of various factors on the change in erosion rates. In the final stage, the erosion risk map obtained from the ICONA model was compared with the erosion class map of the RUSLE model. Based on the results during this study, 45.36% of the area was classified as very low erosion risk, 35.57% as low erosion, 15.62% as moderate erosion risk, 4.1% as high erosion and 1.35% as very high risk areas. The comparison of the two models showed almost similar results in estimating soil erosion, although, it was observed that the estimation of soil erosion in high slope areas and also regions with poor vegetation cover was different between the models. This study showed that RS and GIS technologies play an important role in erosion risk assessment studies and play an important role in accelerating the conservation and management process by eliminating time-consuming field activities and obtaining multi-year metrics. Keywords: Soil erosion, GIS, remote sensing, ICONA erosion model, RUSLE erosion model

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