Estimation of gridded anthropogenic heat flux at the optimal scale by integrating SDGSAT-1 nighttime lights and geospatial data

Abstract

Diverse human activities in megaregions have generated excellent anthropogenic heat (AH), which disrupts the urban energy balance and affects the urban climate. However, few studies have investigated the spatial scale effect (SSE) of estimating the multi-source AH flux (QF). Hence, this study proposes a method, i.e., a nighttime light index (NTLI)-based top-down inventory model, to investigate the optimal spatial scale (So) for estimating multi-source gridded QF at the county level and mapping gridded products for the central region of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) megaregion in 2018. The method combines the multi-scale (10–500 m) enhanced NTLI with a top-down inventory model. We generated the enhanced NTLI by integrating the Sustainable Development Science Satellite 1 (SDGSAT-1) nighttime lights (NTL), points of interest, and road network data. Then we analyzed the SSE of QF estimation to determine the So and evaluated the accuracy and characteristics of multi-source QF at the So. The results showed that (1) the So of estimation for six heat components varied between 10 m and 450 m, which related to the difference in energy source and spatial pattern; (2) considering the estimation accuracy, numerical characteristics, and spatial detail of critical urban features, the So of QF was 300 m; (3) the proposed method reduced the estimation error of QF more effectively than the NTL-based inventory model. The root mean square error (RMSE) decreased by 2.45–21.20 %, and the goodness of fit (R2) increased by 2.17–13.66 % among six heat components; and (4) our multi-source QF product at 300 m outperformed previous QF products in spatial heterogeneity and numerical accuracy. This study first explored the SSE of QF estimation and captured the spatial and numerical information of multi-source QF at the So, which could provide valuable knowledge for urban micro-climate.