C-band scatterometers have been collecting radar backscatter data since 1991, providing valuable long-term records for environmental monitoring applications such as soil moisture and vegetation dynamics. However, differences in sensor calibration between missions introduce biases that compromise the continuity of these data records. This paper presents the practical implementation and adaptation of Reimer’s (2014) rainforest-based inter-calibration approach for ESA’s ERS satellites (ESCAT) and Metop/ASCAT instruments. We implement the method as a modern, open-source Python framework and apply it to the newly complete ERS data record (including ERS-1 data not available in the original study). The resulting calibrated backscatter data record will enable improved long-term monitoring of land surface dynamics with reduced mission-to-mission variability in bias and slope response over incidence angle.