| Vegetation phenology is a highly sensitive indicator of global environmental change,reflecting how Earth’s ecosystems respond to changes in background climate,land cover,geochemical cycles,hydrological cycles,and biodiversity within and between years.As an indicator of the direct interaction between natural and human systems,urban phenology not only affects the community structure,nutrient interactions,and ecosystem functions of urban ecosystems but also has a profound impact on human health.Urbanization is a worldwide historical process.Human activities related to urbanization have significantly changed the growth environment of urban vegetation and profoundly affected urban phenology.Cities have become ideal laboratories for studying how vegetation responds to global environmental change.Due to the low spatial and temporal resolution of the data sources used in the current urban phenology research,and the driving mechanism of urban phenological changes is not yet clear,there is uncertainty in the extraction of urban phenology.This study develops an urban phenology extraction algorithm based on high temporal and spatial resolution datasets to systematically and scientifically study urban phenology on a large scale,combining three driving factors(land surface temperature,precipitation,and nighttime light intensity)to conduct a comprehensive and in-depth analysis of the driving mechanism of urban phenology.In this study,32 major cities in China under different climatic zones were used as the study area,and a high temporal and spatial resolution time series dataset(8 days,30 meters)was constructed,the vegetation phenology along the urban-rural gradient in 32 cities in China was extracted by using the dynamic threshold method,and finally,the urban phenology and driving factors were subjected to multiple linear regression and partial correlation analysis.,in-depth discussion of the impact of different driving factors on urban phenology and their driving mechanisms.Its main conclusions are as follows:(1)As for the total,the average Start of Season(SOS)and End of Season(EOS)of vegetation phenology ranged from 58.62 days to 113.04 days,and 231.71 to 331.84 days,respectively.In different climate zones,the average SOS and EOS of the seven climatic zone agglomerations ranged from 65.32 days to 106.39 days,and 251.71 to 327.42 days,respectively.SOS shows a trend of convergence in the spatial pattern which means the date of SOS occurrence among different cities tends to be at the same level.There are obvious gradient differences in EOS at different latitudes,presenting a slightly complex spatial pattern.(2)Among the 32 cities in China,the farther the vegetation in 23 cities is from the central urban area,the more delayed the SOS.The farther the vegetation in the 20 cities is from the central urban area,the more advanced the EOS,and the more obvious the urbanization effect is on the whole.Across all 32 cities,the average SOS changes in urban vegetation were +0.45,+0.95,+1.25,+1.17 and +1.42 days per 1,2,5,10 and 15 km of central urban expansion;EOS average changes-0.28,-1.11,-1.09,-1.65 and-2.60 days.(3)LST and NLI have a good correlation and strong sensitivity to urban phenology.In cities where vegetation phenology was significantly affected by LST,the average change of SOS was-4.41±4.85 days and the average change of EOS was +4.92±3.94 days for every 1°C increase in LST.In the cities where the vegetation phenology was significantly affected by NLI,the average change of SOS was-0.68±0.40 days and the average change of EOS was +0.67±0.39 days for 1/nano Watts/cm2/sr increase of NLI.The effect of precipitation(P)on urban phenology is weak.In the cities where the vegetation phenology was significantly affected by P,the average change of SOS was +1.33±1.83 days and the average change of EOS was-2.14±2.89 days for 1mm increase of P.(4)P enhancement decreases the sensitivity of SOS and EOS to LST.The sensitivity of SOS to LST and EOS to LST changed by +3.52 ± 0.86 d/ ℃ and-2.38 ± 1.38 d/ ℃ once P increased 1mm.NLI enhancement decreases the sensitivity of SOS to LST and increased the sensitivity of EOS to LST.The sensitivity of SOS to LST and EOS to LST changed by +3.47±0.56 d/℃ and +0.97±0.43 d/℃ once NLI increased 1nano Watts/cm2/ Sr.LST significantly enhanced the sensitivity of EOS to P,and the sensitivity of ΔEOS to ΔP changed by +2.38±1.38 d/mm for every 1°C increase in LST.LST significantly enhanced the sensitivity of vegetation phenology to NLI.The sensitivity of SOS and EOS to NLI changed by-1.83±0.48 d/nano Watts/cm2/ Sr and +0.96±0.36 d/nano Watts/cm2/ Sr once LST increases 1℃. |
