In order to quickly invert higher precision soil moisture, this paper used the genetic algorithm optimized neural network and multi-source remote sensing data to invert the surface soil moisture. First, a four-layer neural network was established and the network was optimized by genetic algorithm. Then, we used the backscattering coefficient of radar data with different polarizations (VV, VH, VH/VV), radar incident angle, normalized vegetation index (NDVI) of optical data, and elevation data as input to neural network, the soil moisture data as the output to train and simulate the network. Finally, the inversion data was verified by the actual measured data of the surface. The results showed that the correlation between the inversion data and the measured data is high (R2=0.79). The method of genetic algorithm optimized neural network is feasible to calculate soil moisture content, and the soil water inversion effect is better after adding auxiliary data such as optical data. This study provides a new idea for the collaborative inversion of soil moisture in the multi-source remote sensing.