A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights.

Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from 10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021). [ABSTRACT FROM AUTHOR]