Global Assessment of Vegetation Ozone Exposure Using a Remote Sensing-Based Index: A New Potential Reference for Critical Levels and Gross Primary Production Simulations

Ozone pollution suppresses plant photosynthesis and reduces crop yields, yet many global assessments still rely on indicators or models that are difficult to compare across regions. We propose a concise and scalable remote sensing index, called ozone vegetation exposure intensity (Formula presented), which combines near-surface ozone concentrations with the normalized difference vegetation index (NDVI) to characterize the spatiotemporal distribution of vegetation ozone exposure. Using global observations from 2003 to 2019, we find pronounced spatial and seasonal contrasts, with higher exposure in industrialized and densely vegetated regions, particularly during spring and summer in the Northern Hemisphere. (Formula presented) is consistently associated with independent vegetation metrics: regions of higher ozone exposure generally correspond to lower solar-induced chlorophyll fluorescence (SIF), indicating ozone-induced stress on vegetation activity. In site-level evaluations using the Community Land Model (CLM5.0) and FLUXNET eddy-covariance data, we identify a new potential critical ozone concentration of 42.38 ppb, which yields improved performance over the traditional AOT40 (accumulated exposure over a threshold of 40 ppb) in broad, noncropland contexts—suggesting that it can complement existing ozone exposure indices. (Formula presented) can be operationalized for ozone risk management: for example, by screening hotspots to prioritize field inspections and expand monitoring networks; by serving alongside AOT40 as a metric for evaluating seasonal mitigation plans and issuing alerts during peak growing seasons; and by coupling with land-use and crop distribution data to support emission-control planning and scenario analysis. Key limitations of our current approach include reliance on monthly ozone and NDVI products (with uneven monitoring coverage), a predominantly linear statistical framework that does not capture time lags or nonlinear dose–response behavior, and the absence of explicit stomatal-flux representation. Priorities for future work include controlled experimental validation, integration of (Formula presented) with stomatal flux schemes in land–surface models, expansion of ground observations in data-sparse regions, and testing of alternative vegetation indices with stricter cloud and reflectance screening. (Formula presented) represents a new remote sensing index for detecting vegetation ozone exposure. The critical levels and model-improvement directions inferred here should be treated as potential References that, with further validation and refinement, can help bridge the gap between observations, models, and policy, ultimately providing a reliable remote sensing-based decision support tool for managing risks to ecosystem productivity and food security.