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Topography is an important factor that affects soil erosion, which is usually measured by slope gradient and slope length (LS) in erosion estimation models, and extracted based on digital elevation model (DEM) on a vast area. SRTM, as currently high-quality and easily accessible elevation data on a vast area, has been applied in global soil erosion evaluation. However, the traditional algorithm for topographic factor extraction requires that the unit of elevation identical with cell size (usually meters), which makes SRTM need perform coordinate transformation before extraction. Aiming at the problem of high cost in performing coordinate transformation on SRTM data in a large area, an algorithm for extracting terrain factors directly was proposed based on SRTM (LSA-SRTM). The longitude and latitude information of the geographic coordinate system was used to directly calculate the cell size and the unit slope length. D8 method was used to acquire slope gradient and flow direction matrix. Then, the slope gradient cutoff was calculated according to the slope gradient result, the catchment area was calculated and the channel network cutoff was set, furtherly, the cumulative slope length was obtained by “forward-reverse traversal”. Finally, the LS factor was calculated according to the slope gradient, cumulative slope length and the segmentation formula of CSLE. Using Himmelblau-Orlandini mathematical surface and 1″SRTM of five typical samples in China as the data source, the LSA-SRTM method, the projected coordinate system DEM-based LS extraction algorithm (LSA-DEM) and manual measurement method were compared. On the mathematical surface and the typical sample area, the R2 of the slope length of LSA-SRTM method and the measured value were 0.8552, 0.7788, 0.7269, 0.7024, 0.6909 and 0.7255. The R2 of the LS and the measured value were 0.8907, 0.8209, 0.8213, 0.7142, 0.7145 and 0.8212. In terms of execution time, the LSA-SRTM method had high efficiency. The experiment results showed that the LSA-SRTM had higher accuracy and efficiency, which can provide a support for the study of topographic factor extraction in vast areas.