Finer-Resolution Long-Term Mapping of Plant Functional Types at 30-m Resolution and Corresponding Leaf Area Index for Earth System Modeling

Abstract. Land surface data describe the heterogeneity of the terrestrial surface and serve as fundamental input for earth system models. Under ongoing climate change and increasing intensity of human activities, land surface data are required to quantify the impact of land use and land cover change (LULCC) and their contribution to regional and global climate. Plant functional type (PFT) and PFT-specific leaf area index (PFT LAI) characterize land surface and vegetation canopy attributes and are essential model inputs. However, uncertainties associated with current derivation methods may limit their application in earth system models. Moreover, long-term, high-resolution datasets of PFT distributions and PFT LAI remain scarce for fine-scale simulations. To address these gaps, we derived a global 30 m PFT map (PFT30) spanning 1985–2020, updated at five-year intervals prior to 2000 and annually thereafter. By integrating multiple high-resolution remote sensing products, we minimized the assumptions typically required for PFT fraction determination. Building on PFT30, we generated a monthly 500 m PFT LAI dataset for 1985–2020 by fusing PFT30 with the reprocessed MODIS C6.1 LAI and GIMMS LAI4g products, using a remote-sensing-derived phenology scheme instead of the empirical approaches commonly adopted in land surface models. Comparisons with three other hundred-meter global PFT products show that all datasets produce broadly consistent tree fractions, while short-vegetation fractions differ substantially; PFT30 shows better agreement with site observations. Compared with empirical schemes, the new PFT LAI dataset can better distinguish short vegetation types such as grasses, shrubs and crops, because it captures realistic phenological variations directly from remote sensing. This long-term, high-resolution PFT map and the associated PFT LAI data provide a finer representation of land surface characteristics and can be applied in land surface and earth system modeling from regional to global scales.