Forests play a central role in the global carbon cycle by serving as critical carbon sinks for atmospheric CO₂. Yet, the stability and continued capacity of these sinks are increasingly threatened by a growing number of disturbances. Accurately representing the stochastic nature of disturbance remains a major challenge and a key source of uncertainty in our understanding of carbon cycle dynamics. This study presents a novel framework for deriving disturbance regimes characterized by extent (μ), frequency (α), intensity (β), as well as background mortality (K_b) directly from landscape features of high-resolution satellite biomass data. These regimes reflect the characteristics of long term disturbances at the landscape scale rather than the properties of any single event. Our analysis inverts the forward model framework developed by Wang et al. (2024), which used a machine learning model trained on a massive synthetic dataset of over 8 million forward model simulations to link known disturbance regimes to spatial biomass patterns. Instead of predicting patterns from regimes, we use observed satellite biomass patterns to infer the underlying disturbance regimes. To ensure robustness, we first identified the optimal spatial resolution for aggregating both simulation and satellite data, minimizing discrepancies in feature value ranges and reducing extrapolation risk. Using this framework, we produced the first globally consistent, observationally constrained dataset of forest disturbance regime parameters and their associated uncertainties, provided at both a 25 × 25 km² tile level and as a gridded 0.25° global product. Additionally, we used a Dissimilarity Index (DIK) to quantify prediction uncertainty and identify potential extrapolation by measuring observations' divergence from the training set. An empirical evaluation of borderline disturbance regimes supports the assumptions and methodological approach used to build the dataset. Our global maps of disturbance regimes provide a novel, process-based tool for investigating the coupled dynamics of disturbance, vegetation, and the carbon cycle, with potential applications for improving the representation of stochastic disturbances in large-scale ecosystem models.