The changes in surface elevation, soil moisture, and snow depth are fundamental variables for studying activity dynamics and permafrost. The GPS Interferometer Reflector (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost regions. However, its applicability in estimating soil moisture in permafrost has not yet been evaluated in the region. In addition, these variables are usually measured separately at different locations. Integrate their estimates into a site to promote the comprehensive utilization of GPS-IR in permafrost. In this study, we ran simulations to demonstrate that the commonly used GPS-IR algorithm for estimating soil moisture content cannot be directly applied to permafrost regions, as it does not take into account seasonal surface elevation changes caused by active layers that introduce thawing bias. We propose a solution to improve this default method by referencing the surface elevation changes in the model. We use GPS data and in-situ observations in permafrost fields to study the northeastern part of the Qinghai Tibet Plateau. The correlation coefficient between root mean square error and GPS-IR estimation of soil moisture content and in-situ content increased from 1.85% to 1.51%, and from 0.71 to 0.82, respectively. We also proposed an integrated GPS-IR framework to estimate these three variables at a location, using the same site in QTP as an example. This study emphasizes the use of default algorithms to enable GPS-IR to effectively estimate the moisture content in permafrost regions when estimating soil. The three in one framework can fully utilize GPS-IR in permafrost regions and can be extended to other locations such as the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in QTP, which fill spatial gaps and provide supplementary measurements of ground temperature and activity layer thickness.