Solar-induced chlorophyll fluorescence (SIF) is a crucial variable toward timely and effective monitoring of vegetation productivity, as well as physiological and biochemical parameters, across extensive areas. Among these advances, the Tropospheric Monitoring Instrument (TROPOMI) SIF has significantly increased the spatiotemporal resolution and data coverage compared with previous sensors. However, TROPOMI SIF data suffer from nonuniform sampling, swath gaps, and cloud contamination, resulting in numerous instances of missing data. In this article, we proposed a physical and spatial information-aided gap filling (PSGF) method, which effectively addresses the missing data problem, generating a Spatially Seamless, 0.05°, daily SIF (S ²-SIF) dataset globally at a spatial resolution of 0.05° from 2018 to 2021. Through missing data simulation experiments conducted in six regions worldwide, we demonstrated consistency between the reference SIF and filled SIF, with a correlation coefficient (CC) of 0.659. Furthermore, validation using in situ data from 35 SIF and gross primary productivity (GPP) ground sites yielded a CC of approximately 0.70 for the SIF sites and CC values above 0.60 between the ground GPP and filled SIF. In addition, consistency was observed between the filled SIF datasets and two other SIF products across 11 vegetation types, confirming the reliability of the filled SIF data and the efficacy of the PSGF method. The produced filled SIF data are made publicly available and should greatly increase the applicability of the daily SIF data for a wide range of applications, including quantifying the photosynthesis of vegetation and accurately estimating GPP globally.