&Emsp; Surface elevation change, soil moisture and snow depth are all essential variables for studying the dynamics of activity in permafrost and permafrost. GPS interferometric reflectometer (GPS-IR) has been used to measure surface elevation change and snow depth in permafrost in the region. However, its applicability in estimating soil moisture in permafrost has not been assessed regionally. In addition, these variables are usually measured separately at different locations. Integrating their estimates into a single site promotes a comprehensive utilization study of GPS-IR in permafrost. In this study, we run simulations to illustrate that the commonly used GPS-IR algorithm for estimating soil water content cannot be directly used in permafrost regions because it does not account for the bias introduced by seasonal surface elevation changes caused by the active layer to thaw. We propose a solution to improve this default method by introducing modeled surface elevation changes. We use GPS data and in situ observations in a permafrost field on the northeastern Tibetan Plateau. The correlation coefficients of the root mean square error and GPS-IR estimates of soil moisture content and in situ content improved from 1.85% to 1.51% and from 0.71 to 0.82, respectively, and we also present a framework for integrating GPS-IR to estimate these three variables at a single site, using the same site in the QTP as an example. This study emphasizes on the default algorithm to make GPS-IR effective in estimating soil moisture content in permafrost areas. The 3-in-1 framework is able to fully utilize GPS-IR in the permafrost region and can be extended to other sites such as the Arctic. This study is also the first to use GPS-IR for estimating environmental variables in the QTP that fill spatial gaps and provide complementary measurements of ground temperature and activity layer thickness.