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Observing a target from different look and incidence angles is one of the key features of the Advanced Scatterometer (ASCAT) on-board the series of Metop satellites. The incidence angle dependency of backscatter plays an important role in extracting useful information for the retrieval of geophysical parameters. The TU Wien change detection algorithm exploits the multiangle measurement capabilities of ASCAT to retrieve relative surface soil moisture content. In the TU Wien algorithm, the incidence angle dependence of backscatter is characterized with a second-order polynomial and its coefficients are estimated from several years of data due to robustness. Recently, however, it has been shown by Melzer [1], that a kernel smoother (KS) holds promise to characterize the polynomial coefficients on an interannual basis. In this study, we tested the performance and robustness of the KS globally, by comparing the results obtained from ASCAT on-board Metop-A and Metop-B independently. Overall, a good agreement has been found between Metop-A and Metop-B confirming a robust interannual estimation of the incidence angle dependence of backscatter using the KS. However, in two cases, the prevailing conditions on the ground complicated the estimation: areas with very low signal variation and sandy deserts. An analysis of Hovmoller diagrams provided insight into seasonal variations, also revealing small remaining biases between the instruments. The dynamic characterization of the incidence angle dependence of backscatter allows to study the temporal evolution in more detail and, at the same time, moving a step further on the vegetation correction in the TU Wien soil moisture algorithm.