Due to shading of clouds and objects on the ground and due to the performance of sensors, the part region of field inversion data by remote sensing may be incomplete, which makes it harmful to use these data for further study. The way to recover the incomplete field inversion data accurately and quickly seems to be significant. Matrix completion (MC) has been proposed in recent years, which is mainly used for low rank matrix. Because of the quality of low rank, the data of the matrix has high correlation, so the matrix can be high accurately recovered with MC. SVT (Singular Value Thresholding) algorithm is one method of MC, which could recover missing values in the matrix rapidly and accurately. In this paper, we introduced the SVT algorithm for matrix completion and we used this algorithm to complete the missing data points which were selected in different regions. Making the missing data points be the center of a region of square and the size of the square is selected by the criteria of the smallest variance. Use SVT algorithm to complete this square region and name this method local SVT algorithm (LSVT). Comparing with the SVT algorithm for the whole region data that is named WSVT which is based on the whole experiment area, inversing distance weighting method (IDW) and Kriging method respectively, we conclude that the precision for LSVT is higher than WSVT and IDW method. Also, the precision changing trend for LSVT is similar with Kriging method and the precision for LSVT is higher than Kriging method in sea front region.