Moore and Burch (1986) showed a physical basis of the LS factor.

Wischmeier and Smith (1978) derived a statistically-based LS factor;

the slope effect data were from slopes between 0.5% and 25% while

the slope length effect data were from slopes between 9 and 200 m, both from natural and simulated rainfall studies.

DEMs with a proper spatial resolution may be used to calculate the

upslope contributing area and the resulting LS factor.

Choosing the most suitable LS factor calculation method for RUSLE mapping depends on various factors such as the characteristics of the study area, the availability and quality of input data, and the specific research objectives. Therefore, there is no one-size-fits-all answer to this question. However, here are some general considerations that can help you make an informed decision:

Consider the terrain characteristics of the study area: The LS factor calculation methods differ in the way they account for terrain slope and length. Some methods, such as the Wischmeier & Smith (1978) method, consider only slope, while others, such as the Moore & Nieber (1989) and Desmet & Gobers (1996) methods, account for both slope and length. Therefore, you should consider the terrain characteristics of your study area and choose a method that best represents the erosion potential of the terrain.

Evaluate the accuracy of input data: The accuracy of input data, such as digital elevation models (DEMs), is critical to the accuracy of LS factor calculations. Therefore, you should evaluate the accuracy and resolution of DEMs and choose a method that is suitable for the available data.

Review previous studies: Reviewing previous studies that used RUSLE mapping in similar study areas can help you identify the most suitable LS factor calculation method. However, it is essential to note that different studies may use different LS factor calculation methods depending on the specific research objectives and data availability.

Validate the results: After selecting an LS factor calculation method, it is essential to validate the results using ground truth data or other independent sources of information. Validation can help you assess the accuracy of the LS factor calculations and the overall performance of the RUSLE model.

In summary, selecting the most suitable LS factor calculation method for RUSLE mapping requires careful consideration of various factors. Therefore, it is essential to assess the terrain characteristics of the study area, the quality of input data, and previous studies to make an informed decision. It is also critical to validate the results to ensure the accuracy of the RUSLE model.

Muhammad Firdaus

The choice of LS factor calculation method for RUSLE mapping depends on a variety of factors, such as the scale and resolution of your data, the terrain characteristics of your study area, and the specific research question you are trying to answer.

One way to determine which LS factor calculation method is most suitable for your study is to compare the results of different methods and assess their accuracy and reliability. You can do this by validating your results against field data or other independent sources, and by assessing the sensitivity of your results to different input parameters.

In general, the Wischmeier & Smith (1978) method is widely used in RUSLE mapping and is recommended by many researchers as a reliable and accurate method for calculating LS factors. This method is based on the slope gradient and length of overland flow, and takes into account the influence of vegetation cover and surface roughness on runoff and erosion.

However, other methods such as the Moore & Nieber (1989) or Desmet & Gobers (1996) methods may be more suitable for certain types of terrain or environmental conditions. For example, the Moore & Nieber method is better suited for areas with high relief and steep slopes, while the Desmet & Gobers method takes into account the effects of land use and soil texture on runoff and erosion.

Ultimately, the choice of LS factor calculation method will depend on your specific research question, the characteristics of your study area, and the available data and resources. It is important to carefully evaluate and validate your results, and to document your methods and assumptions clearly in order to ensure the reproducibility and transparency of your research.