A long-term gridded dataset of the vegetation–atmosphere decoupling coefficient across the contiguous United States, 1982–2020

Abstract. Terrestrial evapotranspiration (ET) is widely mapped as a flux, but the vegetation–atmosphere coupling regime that regulates it, commonly summarized by the decoupling coefficient (Ω), is rarely represented in gridded data products. Existing Ω estimates are largely restricted to flux-tower studies, leaving a gap in long-term, spatially continuous, observation-constrained Ω data for the contiguous United States (CONUS). Here, we present a gridded Ω dataset for CONUS from 1982 to 2020 at 0.1° spatial and 8-day temporal resolution. The dataset was generated by combining tower-derived Ω estimates from 74 AmeriFlux sites with gridded meteorological, vegetation, and surface predictors in a specific plant-functional-type (PFT) physics-informed machine learning (PIML) framework. Product reliability was assessed using site-level cross-validation, comparison with a direct machine learning benchmark, feature extrapolation diagnostics, Monte Carlo uncertainty propagation, and pixel-level quality-control flags. The PIML model reproduced tower-derived Ω at withheld sites with a pooled coefficient determination (R2) of 0.77 and root mean square error (RMSE) of 0.13, and showed clearer gains over direct ML under high extrapolation risk. The gridded product captured expected differences among PFTs, with higher Ω in croplands and lower Ω in open shrublands and savannas, while retaining substantial within-PFT spatial and seasonal variability. Each annual NetCDF file includes three main variables: Ω estimates (‘omega’), associated uncertainty (‘omega_unc’), and quality-control flags (‘QC_flag’). The dataset is available at https://doi.org/10.5281/zenodo.21670853 (Xiong and Feng, 2026).