 This research combines information from two types of microwave sensors, passive and active, to create a more accurate estimate of soil moisture at a global scale. The researchers first rescale the data from each sensor so they have similar ranges and dynamics. They then compare the rescaled data to determine which type of sensor provides the most accurate results. When the correlation coefficient between the two sensors is high, the researchers merge the data together to create a single, more accurate product. This product is especially useful in areas where both sensors provide data, such as transition zones between sparse and moderately vegetated regions. The researchers believe their method could be applied to existing microwave sensors and future missions, creating a long-term global soil moisture dataset that will help us understand how soil moisture affects the water, energy, and carbon cycles. This article was authored by Y.Y. Lu, R.M. Paranusa, W.A. Derigo and others.