The Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) are two most widely soil erosion prediction models worldwide. In both of these models, C factor, i. e., the Cover鄄Management Factor, quantifies the effects of vegetation cover and management practices on soil erosion, which is the most important factor in USLE / RUSLE models that can be easily controlled to alleviate soil erosion. The authors reviewed the evolvement of C factor and concluded various estimation methods for C factor at different scales by integrating recent domestic and international developments in this field. Therefore, five methods to quantify C factor were summarized at plot, hillslope and small watershed scales: 1) Obtaining C factor values from USLE / RUSLE handbooks; 2) Estimating C factor values based on the definition of C factor; 3) Computing C factor values by its sub鄄factors; 4) Calculating C factor values using inverse method based on the USLE / RUSLE (C = A / (R•K•L•S•P) ; 5) Assessing C factor with linear and nonlinear models between C factor and vegetation coverage. Currently, estimation of C factor mainly depended on field experiments and observations at plot, hillslope and small watershed scales. Thus, the consistency of experimental conditions among different experiments, especially the consistency of standard runoff plots, is important and a prerequisite for comparability of C factor values. Once again, five methods to quantify C factor at watershed, region scales were summarized as well: 1) Assigning C鄄factors values from reported values in literature according to land鄄cover / land鄄use categories; 2) Estimating C factor values at larger scales by equations based on relationships between C factor values and vegetation coverage acquired at smaller scales; 3) Performing direct regression between image bands ratios / vegetation indices and C factor values determined in the field; 4) http: / / www.ecologica.cn Estimating C factor values by linear Spectral Mixture Analysis; 5) Improving in mapping of C factor values by geostatistical methods with remote sensing images. In general, at watershed and region scales, C factor values were usually estimated by using remote sensing images, The development of remote sensing technology promoted the evolvement of C factor estimation methods, which makes C factor mapping more precise. However, full interpretation of C factor values by using remote sensing date remains a major challenge, requiring further research on C factor. In conclusion,the authors summarized 10 ways to estimate C factor values, their advantages and disadvantages and also their applicable conditions in detail. Furthermore, the authors emphasized the important aspects of further research on C factor in future and wished extensive involvement of more researchers in this field.

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