Introduction

Abstract

Soil erosion is a global threat to the natural resources and is particularly responsible for reduction in crop yield due to reduction in land productivity and storage capacity of multipurpose reservoirs due to continuous siltation. Accelerated soil erosion has adverse economic and ecological impacts. The assessment of the risk of soil erosion can be helpful for land evaluation in the region where soil erosion is the major threat to sustained agriculture, as the soil is the basis of agricultural production. Erosion models are used to predict soil erosion. Soil erosion modeling can consider many of the complex interactions that influence rates of erosion by simulating erosion processes in the watershed. Most of these models need information related to soil type, land use, landform, climate and topography to estimate soil loss. One of the biggest problems in testing these models is the generation of input data, which is too spatial, and conventional methods proved to be too costly and time consuming for generating this input data. Due to advances in Remote Sensing technology, deriving the spatial information on input parameters has become more convenient and cost-effective. With the powerful spatial data processing capabilities of Geographic Information System (GIS) and its compatibility with RS data, soil erosion modeling approaches have become more comprehensive and robust.