Analysis of the changes in spring phenology of vegetation in Beijing City from 2000 to 2022
XIE Yijia1(), YANG Beibei2,3, ZHANG Zhen1, CHEN Jia1, WANG Zhe1, MENG Lingkui1()
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China 2. Changjiang Spatial Information Technology Engineering Co., Ltd. (Wuhan), Wuhan 430010, China 3. Hubei Water Conservancy Information Perception and Big Data Engineering Technology Research Center, Wuhan 430010, China
Investigating spring phenology is critical for understanding the growth and development cycles of vegetation and the response mechanisms to climate and environmental changes. It also provides significant insights for guiding agricultural production and protecting and restoring ecosystems. This study reconstructed the time series of MOD13Q1 data for Beijing City from 2000 to 2022. Based on dynamic thresholding, this study extracted the spring phenology of vegetation in Beijing City over the past 23 years. Furthermore, this study analyzed the spatiotemporal changes in spring phenology in Beijing City using the Mann-Kendall (M-K) trend test. Finally, this study examined the differential responses of spring phenology to climate change through partial correlation analysis. The results of this study indicate that the average spring phenology of vegetation in Beijing City occurred on the 117th day of a year (in late April), advancing at an average rate of approximately 1.14 days per year over the past 23 years. Different duo exhibited distinct hierarchical variations in spring phenology. Forests showed the earliest spring phenology starting from the 107th day, followed by shrubs (the 117th day) and grasslands (the 119th day), with the latest being farmland (the 130th day). The impacts of average annual temperature on spring phenology exhibited significant spatial variations. A positive correlation was observed in water-rich areas such as rivers and reservoirs, whereas a significant negative correlation occurred in eastern Fangshan District. On a monthly scale, temperatures in November, December, January, and February significantly influenced spring phenology. As winter temperatures rose, the spring phenology of vegetation tended to advance. This study explores the response mechanisms of spring phenology of vegetation in Beijing City to temperature and precipitation, providing valuable insights for vegetation management under climate change.
谢宜嘉, 杨倍倍, 张镇, 陈佳, 王喆, 孟令奎. 2000—2022年北京市植被春季物候期变化特征分析[J]. 自然资源遥感, 2025, 37(2): 185-193.
XIE Yijia, YANG Beibei, ZHANG Zhen, CHEN Jia, WANG Zhe, MENG Lingkui. Analysis of the changes in spring phenology of vegetation in Beijing City from 2000 to 2022. Remote Sensing for Natural Resources, 2025, 37(2): 185-193.
Fu Y S, Zhang J, Wu Z F, et al. Vegetation phenology response to climate change in China[J]. Journal of Beijing Normal University (Natural Science), 2022, 58(3):424-433.
[3]
Li S, Xu L, Jing Y, et al. High-quality vegetation index product generation:A review of NDVI time series reconstruction techniques[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 105:102640.
[4]
Viovy N, Arino O, Belward A S. The best index slope extraction (BISE):A method for reducing noise in NDVI time-series[J]. International Journal of Remote Sensing, 1992, 13(8):1585-1590.
[5]
Chen J, Jönsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004, 91(3/4):332-344.
[6]
Roerink G J, Menenti M, Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000, 21(9):1911-1917.
Li J, Zhu H. The reconstruction of MODIS/NDVI time series data in Chongqing[J]. Scientia Geographica Sinica, 2017, 37(3):437-444.
doi: 10.13249/j.cnki.sgs.2017.03.014
Wang M Y, Luo Y, Zhang Z Y, et al. Recent advances in remote sensing of vegetation phenology:Retrieval algorithm and validation strategy[J]. National Remote Sensing Bulletin, 2022, 26(3):431-455.
[9]
Zeng L, Wardlow B D, Xiang D, et al. A review of vegetation phenological metrics extraction using time-series,multispectral satellite data[J]. Remote Sensing of Environment, 2020, 237:111511.
Fan D Q, Zhao X S, Zhu W Q, et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[11]
Berra E F, Gaulton R. Remote sensing of temperate and boreal fo-rest phenology:A review of progress,challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics[J]. Forest Ecology and Management, 2021, 480:118663.
[12]
Han H, Bai J, Ma G, et al. Vegetation phenological changes in multiple landforms and responses to climate change[J]. ISPRS International Journal of Geo-Information, 2020, 9(2):111.
Tan J, Chen Z H, Xiao M. Characteristics and forecast of flowering duration of cherry blossoms in Wuhan University[J]. Acta Ecologica Sinica, 2021, 41(1):38-47.
[14]
Zhang R, Qi J, Leng S, et al. Long-term vegetation phenology changes and responses to preseason temperature and precipitation in northern China[J]. Remote Sensing, 2022, 14(6):1396.
Zhao X R, Liu J, Yang S K, et al. Spatio-temporal variations of typical woodland and grassland phenology and its response to meteorological factors in Northern China[J]. Acta Ecologica Sinica, 2023, 43(9):3744-3755.
Zhang G D, Bao G, Yuan Z H, et al. Effects of asymmetric warming of daytime and nighttime on the start of growing season on the Mongolian Plateau from 2001 to 2020[J]. Arid Land Geography, 2023, 46(5):700-710.
doi: 10.12118/j.issn.1000-6060.2022.395
[17]
Piao S, Liu Q, Chen A, et al. Plant phenology and global climate change:Current progresses and challenges[J]. Global Change Bio-logy, 2019, 25(6):1922-1940.
Liang C, An J, Fan Y Q, et al. Surface soil carbon density and turnover rate of typical vegetation types in Beijing Songshan national nature reserve[J]. Earth and Environment, 2020, 48(6):672-679.
[19]
Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8):3907-3925.
doi: 10.5194/essd-13-3907-2021
[20]
Vorobiova N, Chernov A. Curve fitting of MODIS NDVI time series in the task of early crops identification by satellite images[J]. Procedia Engineering, 2017, 201:184-195.
[21]
Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8):1824-1832.
Li R, Zhang X, Liu B, et al. Review on methods of remote sensing time-series data reconstruction[J]. National Remote Sensing Bulletin, 2009, 13(2):335-341.
Wang G, Yan D H, Huang Z F, et al. Climatic change characteristics in the latest 52 years in Luan River Basin[J]. Journal of Arid Land Resources and Environment, 2011, 25(7):134-139.
Jiang P, Pan X M, Zeng X Y. Temporal and spatial variation of temperature and precipitation in each agricultural sub-region of China[J]. Research of Soil and Water Conservation, 2020, 27(4):270-278.
Gao Y B, Lu C Y, Zhong L X, et al. Temporal and spatial characteristics of temperature and precipitation in China’s coastal areas from 1951 to 2016[J]. Journal of Forest and Environment, 2019, 39(5):530-539.
Chen H B, Fan X H. Some extreme events of weather,climate and related phenomena in 2006[J]. Climatic and Environmental Research, 2007, 12(1):100-112.
[27]
Xu J, Tang Y, Xu J, et al. Evaluation of vegetation indexes and green-up date extraction methods on the Tibetan Plateau[J]. Remote Sensing, 2022, 14(13):3160.
Xu Y J, Ge Q S, Dai J H, et al. Variations in temperature sensitivity of leaf unfolding date and their influencing factors for typical woody plants in China over the past 50 years[J]. Acta Ecologica Sinica, 2019, 39(21):8135-8143.
[29]
Yuan M, Wang L, Lin A, et al. Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin,China[J]. Ecological Indicators, 2020, 108:105760.
[30]
Jiang N, Shen M, Chen J, et al. Continuous advance in the onset of vegetation green-up in the Northern Hemisphere,during hiatuses in spring warming[J]. NPJ Climate and Atmospheric Science, 2023, 6(1):7.
Bu Y Q, Ding H Y. Spatiotemporal variation of vegetation phenology and its response to urbanization in Beijing[J]. Remote Sensing Information, 2022, 37(2):112-118.
Meng D, Liu X R, Zhang C C. Responses of plant phenology to urban heat island effects in Beijing[J]. Chinese Journal of Ecology, 2021, 40(3):844-854.
doi: 10.13292/j.1000-4890.202103.029
[33]
Kloos S, Yuan Y, Castelli M, et al. Agricultural drought detection with MODIS based vegetation health indices in southeast Germany[J]. Remote Sensing, 2021, 13(19):3907.
Zhang G D, Bao G, Huang X J, et al. Asymmetrical warming in winter and spring and its effect on start of growing season and spring NDVI in Mongolia[J]. Arid Land Geography, 2023, 46(8):1238-1249.
doi: 10.12118/j.issn.1000-6060.2022.686
[35]
Shen X, Jiang M, Lu X. Diverse impacts of day and night temperature on spring phenology in freshwater marshes of the Tibetan Pla-teau[J]. Limnology and Oceanography Letters, 2023, 8(2):323-329.
[36]
Wang H, Wu C, Ciais P, et al. Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling[J]. Nature Communications, 2020, 11(1):4945.
doi: 10.1038/s41467-020-18743-8
pmid: 33009378
2025, Vol. 37 
), 杨倍倍2,3, 张镇1, 陈佳1, 王喆1, 孟令奎1(
