With urbanization accelerating globally, the Urban Heat Island (UHI) effect has become a significant challenge, profoundly affecting urban livability. Urban green space (UGS) are recognized as an effective measure for mitigating UHI, catalyzing extensive research into various data acquisition methods combined with diverse urban microclimate simulation models to evaluate the efficacy of UGS and enhance prediction accuracy and assessment. However, existing datasets frequently grapple with issues of mismatched spatial and temporal resolutions, complicating the harmonization of data across different scales and compromising both consistency and accuracy.

This review meticulously examines the past two decades of research on UGS simulations for UHI mitigation. We focus on the collection of multi-source data related to UGS, which includes remote sensing, meteorological monitoring, GIS, and beyond. We address the challenges associated with these datasets, such as their varying spatial and temporal resolutions that often hinder effective data integration, impacting overall simulation consistency and accuracy. Additionally, we explore techniques for data fusion and unification, emphasizing strategies to integrate data from various scales to boost the reliability and precision of simulations. Various modeling approaches used in urban microclimate simulations are evaluated, including physical process-based models, statistical regression models, and artificial intelligence/machine learning methods. We discuss the strengths and limitations of each model in assessing the benefits of UGS.

By pinpointing critical gaps in data and modeling, we offer technical guidance for accurately assessing the effects of UGS in combating UHI, providing essential insights for future urban planning and policy-making aimed at enhancing urban thermal comfort.

Urban green space; Urban heat island; Urban heat mitigation; Data fusion; Urban microclimate simulation