Urbanization and climate change jointly shift land surface phenology in the northern mid-latitude large cities

Abstract

Abstract: Land surface phenology (LSP) has been widely used as the “footprint” of urbanization and global climate change. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing phenological metrics, e.g. start of season (SOS) and end of season (EOS), between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of SOS and EOS within the urban ecosystem. To fill this knowledge gap, we utilized daily two-band enhanced vegetation index, daily meteorological record, and annual land cover dataset to investigate the respective impacts of urbanization and climate change on temporal shifts of LSP between the post- and the pre-urbanization periods over 196 large cities in the northern mid-latitudes. We found 51% of the cities experienced an advanced SOS with an average of −6.39 ± 5.82 days after urbanization has occurred, while the remaining 49% of the cities had a delayed SOS with an average of 7.56 ± 5.63 days. We also found a later EOS at 53% of the cities and an earlier EOS at 47% of the cities with an average of 8.43 ± 7.59 and −5.57 ± 4.99 days between the post- and pre-urbanization periods, respectively. Multiple linear regression analysis indicates that climate variables (i.e. temperature, precipitation, and insolation) play dominant roles in regulating the temporal shifts of LSP. Furthermore, the earlier SOS and later EOS were significantly correlated with the amplitude of urbanization (i.e. increase of impervious surface area) in cities after controlling for effects of climate factors. These patterns were generally consistent across eight climate zones. Our findings provide critical information in modeling natural and anthropogenic effects on urban ecosystem, with important benefits for urban sustainability and biodiversity conservation.