Abstract: Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the presence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to investigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Correlation analysis found that the S4, S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral reflectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning machine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their determination coefficient (R2), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents before and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculated salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accurate, with its associated R2 and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R2 and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral remote sensing.