A multi-sensor historical climatology of satellite-derived global land surface moisture is presented, combining data from various satellite microwave sensors, including Nimbus-7 SMMR, DMSP-SSM/I, TRMM-TMI, and Aqua-AMSR-E, spanning from November 1978 to the end of 2007. The data are derived using a radiative transfer-based land parameter retrieval model (LPRM), which accounts for the different sensor characteristics and provides consistent soil moisture estimates across the entire period. The model uses brightness temperature data from these sensors to estimate soil moisture, considering factors such as polarization, vegetation, and surface temperature. The data are validated against ground observations and other satellite data, showing good agreement. The data are available for download from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and the Vrije Universiteit Amsterdam. The study highlights the importance of soil moisture in various Earth science disciplines and its role in climate modeling and environmental monitoring. The data provide a valuable resource for understanding long-term changes in land surface moisture and its impact on weather and climate. The retrieval process involves complex modeling and optimization techniques to account for sensor differences and environmental factors. The data are stored in Hierarchical Data Format (HDF) for compatibility with other Earth observation data sets. The study also addresses challenges such as radio frequency interference (RFI) and vegetation effects on soil moisture measurements. The results demonstrate the potential of satellite data for monitoring global land surface moisture and its climatic significance.A multi-sensor historical climatology of satellite-derived global land surface moisture is presented, combining data from various satellite microwave sensors, including Nimbus-7 SMMR, DMSP-SSM/I, TRMM-TMI, and Aqua-AMSR-E, spanning from November 1978 to the end of 2007. The data are derived using a radiative transfer-based land parameter retrieval model (LPRM), which accounts for the different sensor characteristics and provides consistent soil moisture estimates across the entire period. The model uses brightness temperature data from these sensors to estimate soil moisture, considering factors such as polarization, vegetation, and surface temperature. The data are validated against ground observations and other satellite data, showing good agreement. The data are available for download from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and the Vrije Universiteit Amsterdam. The study highlights the importance of soil moisture in various Earth science disciplines and its role in climate modeling and environmental monitoring. The data provide a valuable resource for understanding long-term changes in land surface moisture and its impact on weather and climate. The retrieval process involves complex modeling and optimization techniques to account for sensor differences and environmental factors. The data are stored in Hierarchical Data Format (HDF) for compatibility with other Earth observation data sets. The study also addresses challenges such as radio frequency interference (RFI) and vegetation effects on soil moisture measurements. The results demonstrate the potential of satellite data for monitoring global land surface moisture and its climatic significance.