Investigating the urban-induced microclimate effects on winter wheat spring phenology using Sentinel-2 time series

Temperature is one of the most important factors controlling the phenology of winter wheat. Rapid urbanization in China dramatically modifies the microclimate, especially temperature, surrounding cities. However, it is unclear whether such urban-induced changes in microclimate can influence the phenology of winter wheat and whether the influence is consistent across cities of different sizes. Here, we investigated the urban-induced microclimate effects on winter wheat spring phenology (i.e., the regreen-up date, RGUD) in three cities spanning a range of sizes in northern China. These three cities include Shijiazhuang (350.98 km²), Baoding (118.95 km²), and Linqing (55.12 km²), and the key data for this study are Sentinel-2 images. Based on the Sentinel-2 images, we first calculated a vegetation index (normalized difference phenology index, NDPI), and then extracted winter wheat RGUD. Finally, we analyzed the distribution of the RGUD along an urban-rural gradient using buffers surrounding the urban areas. Our study has three main results: (1) The RGUD shows a significant increasing trend along the urban-rural gradients in both Shijiazhuang and Baoding, suggesting that urban-induced increases in temperature indeed advance the spring phenology of winter wheat. (2) The maximum influence size of the urban-induced temperature effects on the RGUD is positively correlated with city size, i.e., 27 km for Shijiazhuang, 14 km for Baoding and 7 km for Linqing. (3) The change rate of the RGUD with distance along the urban-rural gradient is significantly higher in the large city (Shijiazhuang: 0.26 day/km) than it is in the middle- and small-scale cities (Baoding: 0.21 day/km and Linqing: 0.11 day/km), which suggests that larger cities spread heat at a faster rate than that of smaller cities. This study suggests that the planting and management of winter wheat surrounding cities should consider the influence of city size to optimize yields.