Manganese in soil plays an important role in plant growth, and high or low levels of soil manganese will have adverse effects on plants, so it is especially important to monitor soil manganese content quickly. At present, the studies related to the monitoring of soil Mn content by remote sensing technology mainly focus on the estimation of soil Mn content using soil spectra. At the same time, it is difficult to obtain soil spectra from satellite images in the southern region where vegetation covers all year round. Therefore, this paper introduces vegetation spectra to explore the rapid monitoring method of soil Mn elements in vegetation-covered areas. Firstly, 11 vegetation spectral indicators were extracted from Landsat 8 images, and the best vegetation spectral indicators were selected by Pearson correlation coefficient combined with Variance Inflation Factor (VIF); based on this, Partial Least Squares (PLS) regression was used. (PLSR), Multiple Stepwise Regression (MSR) and BP Neural Network (BPNN) algorithms were used to construct the best vegetation spectral indicators. Finally, spatial mapping of soil Mn content was carried out based on the best inversion model. Taking the Nanchuan District of Chongqing City as an example, the results showed that three vegetation spectral indicators (specific vegetation index, normalized vegetation index and visible atmospheric impedance vegetation index) were identified as the best spectral response indicators of soil Mn. The mapping accuracy of soil Mn content (R2 was 0.69, RMSE was 567.64, and RPD was 1.30). The results showed that the inversion of soil Mn content by vegetation spectral indicators is feasible, and this study opens up new ideas for monitoring soil Mn content at the regional scale.