A double-temperature-difference framework for anthropogenic heat flux estimation accounting for surface energy balance perturbations

Anthropogenic heat flux (AHF) is an important driver of urban climate, but its estimation is still commonly based on the assumption that anthropogenic heat is mainly expressed as sensible heat. This assumption neglects the concurrent perturbations of other surface energy balance (SEB) components and may limit the applicability of AHF estimation under different seasonal conditions. In this study, a Double Temperature Difference (DTD) framework was developed to estimate AHF from land surface temperature and near-surface air temperature responses. The framework represents AHF as the integrated response of multiple SEB perturbation pathways, including sensible heat, latent heat, net radiation, and heat storage. The framework was applied to the central urban area of Beijing using four representative seasonal cases. The estimated AHF showed distinct seasonal variability. Mean AHF reached 102.92 W m −2 in summer and 93.36 W m −2 in winter, compared with 74.57 W m −2 in spring and 60.45 W m −2 in autumn. The results also showed clear intra-urban heterogeneity. Annual mean AHF was highest in LCZ1 (95.63 W m −2 ) and lowest in LCZ3 (71.11 W m −2 ). This indicates that compactness and building intensity are important controls, but deviations among LCZ types also suggest the influence of land-use function, activity intensity, source composition, and intra-class heterogeneity. Compared with the sensible heat separation method, the DTD framework produced broadly consistent spatial patterns, with seasonal correlations ranging from 0.87 to 0.92, while reducing dependence on the sensible-heat-only assumption, particularly under winter conditions when sensible heat flux was weak. These findings indicate that AHF estimation can benefit from considering multiple SEB perturbation pathways. The proposed DTD framework provides a physically constrained and EO-supported approach for city-scale AHF mapping and can support urban climate modeling and thermal-environment assessment.