Wind scatterometer measurements are collected over an irregular grid, and processing is required to generate backscatter images on an Earth-centered grid. The most common algorithms used for this are “drop in the bucket” (DIB) and variations of the scatterometer image reconstruction (SIR) algorithm. These algorithms are also used for radiometer brightness temperature imaging. The Backus–Gilbert (BG) algorithm has been used for radiometer imaging but has not been applied to scatterometer backscatter imaging. In this paper, the application of BG to scatterometer backscatter imaging is explored and its performance is compared to DIB and SIR. Like SIR, optimally tuned BG is capable of producing higher resolution images than DIB, though its noise performance is slightly inferior to SIR’s. While BG and SIR produce similar results for radiometer data, the higher relative noise level of scatterometer data increases the differences between the SIR and BG algorithm performance, and limits the performance of BG relative to SIR in scatterometer imaging. Comparison of the SIR and BG algorithms in scatterometer imaging offers important insights into the inversion/reconstruction problem.