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Multifaceted regime shifts of Earth's surface are ongoing dramatically and--in turn--considerably alter global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems requires an increased understanding of these structurally and functionally heterogeneous multi-component information and their changes, but we remain lack of such records of fractional vegetation and soil information at global scale. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001-2022 using a spatio36 temporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers (photosynthetic vegetation, non-photosynthetic vegetation, bare soil, ice/snow, and dark surface) with high accuracy and low uncertainty, compared to previous vegetation index and vegetation continuous fields product, as well as traditional fully constrained linear spectral mixture models. We also adopt non-parametric seasonal Mann-Kendall tested fractional dynamics to identify shifts based on interactive changes of these fractions. Our results--superior to previous portrayal of the greening planet--not only report a +9.35×105 km2 42 change of photosynthetic vegetation, but also explore decrease of non-photosynthetic vegetation (-2.19×105 km2), bare soil (-5.14×105 km2), and dark surface (-2.27×105 km2 43). Besides, Interactive changes of these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwest China during afforestation activities, an increase of PV in cropland of China and India due to intensive agricultural development, a decrease of PV and increase of BS in tropical zones resulting from deforestation. These advantages highlight that our dataset which provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modelling and effective governance of future terrestrial ecosystems. [ABSTRACT FROM AUTHOR]