active andA new semiempirical model is presented for soil moisture content retrieval, using ENVISAT ASAR and LANDSATTM data collaboratively. Firstly, a back propagation(BP) neural network algorithm(GA) is introduced, and a genetic algorithm is applied to optimize the weights of the node of BP neural network. Then the TM bands (TM3, TM4, TM6) and ASAR data(VV, VH, VH/VV) are taken as the input of the GABP neural network, and the output corresponds to the ground soil moisture. The partial field measurements of soil moisture are used as training samples to train the network and to achieve the map of soil moisture distribution. The field measurements are used to test the validity of the BP neural network algorithm and effectiveness of the active and passive remote sensing cooperative inversion. The comparison between the inversion using single data set(TM or ASAR), and the cooperative inversion of active and passive remote sensing data demonstrates that the new algorithm is more effective, and shows considerable potential in soil moisture retrieval by integrating active and passive remote sensing data. passive remote sensing; GABP neural network; soil moisture; inversion