Vegetation coverage is of great significance to regional and global issues in the field of hydrology, meteorology, and ecology, among others. The accuracy of its estimation can profoundly affect research conclusions. With the popularization of high-precision digital cameras, photographs are widely used for the estimation of vegetation coverage in micro-regions in ecological research because of its advantages, such as objectivity and high precision. In arid and semi-arid degraded steppe, and especially in severely disturbed mining areas, the development of biological soil crust (BSC) can affect the photographically measured value of vegetation coverage because its spectral signature is similar to that of vegetation. In this study, the Yimin open-pit mine area was chosen as the research area, and four groups of photographs (before and after sprinkling water) containing moss crust, lichen crust, algae crust and no BSC (control) were taken as samples. Then, vegetation coverage was extracted using the digital photographic method and the data were processed by different methods (maximum likelihood classification and RGB threshold method) to establish a comparative test. The extracted values were compared with the truth-value, acquired by manually outlining the vegetation coverage, to analyze the effect of BSC on the vegetation coverage measurement, evaluate the extent of the effect, and determine whether the effect was related to BSC water content. Moreover, based on the comparison of conventional methods, a more accurate method that would eliminate the effect of BSC on estimated vegetation coverage was proposed by combining texture and color information. The main conclusions were as follows:1) The existence of BSC led to the overestimation of vegetation coverage when using conventional methods, and those of moss crust and lichen crust were more significant after watering, whereas the results for algae crust were opposite. 2) In the three succession stages of BSC, the samples containing moss crust led to a substantial overestimation of vegetation coverage (the results were up to ten times the truth-value using the RGB threshold method and up to six times using the maximum likelihood classification), followed by lichen crust (the result were up to four times the truth-value using the RGB threshold method and up to two times using the maximum likelihood classification), but the algae crust was not significant because the variance was too large. 3) When BSC coverage increased or vegetation coverage decreased, the accuracy of the estimated vegetation coverage decreased, which suggested that the effect of BSC cannot be ignored in low vegetation coverage mining areas on the steppe. 4) In an attempt to improve the estimation method using texture information, the accuracy of texture classification was very low, while combining texture information with RGB color information resulted in high accuracy. 5) For the two conventional classification methods, the RGB threshold method led to vegetation coverage overestimation as large as twice that of the maximum likelihood classification. For the two proposed methods, both effectively increased accuracy and texture classification, although the method based on band stacking was better. Comparing the four estimation methods, the accuracy was ranked as follows:texture combined with RGB method > maximum likelihood classification considering BSC > maximum likelihood classification > RGB threshold method.