Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea

Abstract

Accurate estimation of soil loss/deposition forced by rainfall events plays a major role in water resources management and it directly affects the quality of agricultural land and water storage capacity in reservoirs. In this paper, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) was used to quantify soil loss in a small basin located in southern part of Korea. The surface characteristics, such as soil texture, elevation, and vegetation type, are needed to run the RUSLE model. Remotely sensed geospatial data has been successfully used to derive suitable model factors for this purpose. It is, however, difficult to select the grid size of elements for the best fit, which is, often, decided in a subjective and intuitive way. A GIS spatial analysis was performed to investigate the scaling effect for estimating soil loss in the RUSLE model using the remotely sensed geospatial data. The results show that the L- and S- factors are sensitive to the grid size and the optimal resolution to quantify soil loss in the RUSLE model for the study site is 125 m. This approach presents a method to select the suitable scale for estimating soil loss using the remotely sensed geospatial data and eventually improve the prediction of soil loss in a basin scale.