Snow cover is an informative indicator of climate change because it affects local and regional surface energy and water balance, hydrological processes and climate. Passive Microwave (PM) works all weather and round the clock and penetrates clouds and snow. Passive microwave remote sensing data have been widely applied to retrieving snow depth and snow water equivalent in the past few decades. Recently, the snow depth retrieval study has rapidly developed. This paper reviewed the research progress of snow depth and snow water equivalent inversion algorithm using PM data at home and abroad. Firstly, the basic theory of passive microwave remote sensing snow monitoring and passive microwave remote sensing data were introduced. Then, the current snow depth and snow water equivalent inversion algorithm were summarized into four categories: ① A statistically based linear inversion algorithm; ② An inversion algorithm based on microwave transmission snow model; ③ A nonlinear inversion algorithm based on prior knowledge; ④ Data fusion and data assimilation. Afterwards, the commonly used seven kinds of snow data products were introduced, and several factors affecting the snow depth and the snow water inversion accuracy were discussed. Finally, the possible direction of future snow parameter inversion research was prospected.