Mapping time-series anthropogenic heat flux using the top-down inventory and temporal downscaling in Beijing-Tianjin-Hebei megaregion of China

Anthropogenic heat could potentially exacerbate heat risk in urban areas. Nighttime light (NTL) data have been widely used in mapping regional-scale anthropogenic heat flux (AHF) due to the close association between NTL and human activities. However, most gridded AHF products are constrained to coarse resolution. This study proposes a novel approach to generate 100 m gridded monthly mean AHF datasets in the Beijing-Tianjin-Hebei megaregion of China from 2012 to 2020. We first estimate the annual mean AHF using the High-Definition Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (VIIRS-HD)-based top-down inventory method. We then downscale the annual AHF into monthly products using ancillary data including the monthly NTL and air temperature data. The results indicate that our 100 m gridded annual mean AHF products outperforms the existing datasets by providing more heterogeneous spatial information, apparent temporal variations, and higher accuracy. Furthermore, the spatio-temporal characteristics of our gridded monthly AHF products reflect well the urbanization trends. Our spatiotemporally explicit AHF products can also be utilized to facilitate investigations of the urban thermal environment and urban climate at the fine scale. The method can potentially be applied to larger areas and longer time series due to its simplicity and effectiveness.