Soil organic matter (SOM) is an important element of wetland ecosystem. Quick and wide monitor of SOM content with multispectral remote sensing technique has the vital significance to protect wetland ecosystem. More previous studies on the estimation of SOM content used hyperspectral analysis, while using multispectral was less. The main reason is that the spectral anomaly of multispectral data caused by spectral mixing of different objects affects the inversion accuracy of SOM content in wetland. Therefore, to avoid the spectral anomaly, this paper took the Shanyutan wetlands of Minjiang River Estuary as a survey region, trying to use Linear Spectral Unmixing Model(LSUM) to separate the pixel of original image and reconstruct the soil spectrum. Then, the correlation analyses between 2 different spectra (the raw spectrum and the reconstructed spectrum) and SOM content were done. Finally, according to correlation results, an inversion model for SOM content was established. The result showed that LSUM can effectively eliminate vegetation endmembers of the original image, reducing the reflection interference of most roads and buildings. The reconstructed spectral characteristic curve was closer to the spectral curve of soil under natural condition. It indicated that the effect of spectral reconstruction was remarkable; Compared to the correlation coefficients between 2 different spectra and SOM content, the reconstruct spectrum was more appropriate for reflecting the correlation between the soil spectrum and soil organic matter in the study area; using the reconstructed spectrum to build the predicting model could obtain more robust prediction accuracies than using the raw spectrum. Its values of R2 and F were increased by 0.124 and 2.223 respectively. And RMSE was reduced by 0.106. Moreover, through the 1∶1 line test, model of the reconstructed spectrum had a better fitting between the predicted and the measured. These results suggested that using LSUM has been proven to be effective in removing the spectral anomaly, ensuring a transferrable model for SOM content under natural condition. The study will provide some practical technology to monitor the SOM content in wetland by multispectral data.