全球气候变暖背景下，秦岭山地作为气候变化的敏感区，其独特的气候环境对当地生态系统所产生的影响倍受关注。因此，系统揭示秦岭地区植被返青期对气候变化的响应机制，对区域生态安全建设尤其重要。本研究首先采用多种先进且广泛使用的方法提取秦岭地区植被返青期，通过与地面观测数据对比筛选出适合该地区的方法，并基于该结果分析了秦岭地区植被返青期时空变化分异特征，明确其与海拔分布高度相关的事实，进一步探讨了其在不同植被类型和海拔梯度下差异；然后分析植被返青期与各气候因子的关系，明确气候变化对秦岭植被返青期的最强影响时段以及影响秦岭返青期的主控气候因子；基于此构建顾及不同气候参数的返青期过程模型，分别从气象站点、海拔梯度样点和全域样点三个尺度分析了不同地区模型的适应性以确定区域的最适模型，明晰气候变化对秦岭植被返青期的影响机制。主要研究结论如下：

通过与地面物候观测数据中的优势种观测结果对比得出，利用Double Logistic拟合后再使用比率阈值法的30%阈值提取的秦岭植被返青期更接近观测值。秦岭地区年均返青期整体上呈“南北两侧分别向中心区域逐渐推迟”的海拔地带性分布格局，南坡各植被类型的平均返青期均早于北坡，秦岭北坡的海拔地带性较南坡差异更大。从年际变化趋势来看，近20年来秦岭地区植被返青期的变化趋势不明显，全区仅有8.73%的区域呈现显著提前趋势，其余区域均呈现波动性变化，南坡的平均变化速率快于北坡。

随着季前时段的增长，各气象因子和返青期的相关性呈现出先增强后减弱的趋势。降水和温度约在返青期前两个月对返青影响最强（降水的影响主要在返青期前60-65天最强，温度的影响主要在返青期前50-70天最强）。太阳辐射约在返青期前四个月对返青期影响最强，主要分布在返青期前110-120天。联合三个气候因子综合分析对秦岭地区植被返青期的影响，温度对秦岭地区植被返青期的影响最强，其次是太阳辐射，降水的影响最弱。

基于双温要素的返青期过程模型在秦岭植被区有较好的适应性，尤其是不固定驱动单元起始时间的双温驱动模型，说明冷激条件和热驱动对秦岭地区植被的重要影响。在中部和西部降水量较少的区域更适合热时+降水模型，说明这些区域植被不仅受温度控制，也受到降水的重要影响，升温促进植物解除春季休眠，而干旱会延缓植物的生长发育，使发育的物候期推迟。

In the context of global warming, the unique climate environment of the Qinling Mountains, as a sensitive area to climate change, is of great concern to local ecosystems. Therefore, it is particularly important to systematically reveal the response mechanism of the vegetation in the Qinling Mountains to climate change during the green-up date. In this study, a variety of advanced and widely used methods were used to extract the green-up date of vegetation in Qinling Mountains, and the methods suitable for this area were selected by comparing with the ground observation data. Based on the results, the spatial and temporal variation characteristics of vegetation green-up date in Qinling Mountains were analyzed, and the fact that it was highly correlated with altitude distribution was clarified, and its differences under different vegetation types and altitude gradients were further discussed. Then, the relationship between vegetation green-up date and climatic factors was analyzed, and the strongest influence period of climate change on vegetation green-up date in Qinling Mountains and the main controlling climatic factors affecting vegetation green-up date in Qinling Mountains were clarified. Based on this, a process model of green-up date considering different climatic parameters was constructed. The adaptability of different regional models was analyzed from three scales : meteorological station, elevation gradient sample point and uniform sample point in study area to determine the optimal model of the region, and to clarify the impact mechanism of climate change on the green-up date of vegetation in Qinling Mountains. The main conclusions are as follows :

By comparing with the observation results of dominant species in the ground phenological observation data, it is concluded that the results of vegetation green-up date in Qinling Mountains extracted by using the 30 % threshold value of the ratio threshold method after Double Logistic fitting are closer to the observation values. The average annual green-up date in the Qinling Mountains showed an altitude zonal distribution pattern of ' gradually delayed from the north and south sides to the central area '. The average green-up date of each vegetation type on the southern slope was earlier than that on the northern slope, and the altitude zonality on the northern slope of the Qinling Mountains was more different than that on the southern slope. From the perspective of interannual change trend, the change trend of vegetation green-up date in Qinling area in the past 20 years is not obvious. Only 8.73 % of the region shows a significant advance trend, and the remaining regions show fluctuating changes. The average change rate of the southern slope is faster than that of the northern slope.

With the increase of pre-season period, the correlation between meteorological factors and green-up date showed a trend of increasing first and then decreasing. Precipitation and temperature had the strongest influence on green-up date about two months before green-up date ( the influence of precipitation was the strongest in the 60-65 days before the green-up date, and the influence of temperature was the strongest in the 50-70 days before the green-up date ). Solar radiation has the strongest influence on the green-up date about four months before the green-up date, mainly distributed in 110-120 days before the green-up date. Combined with the three climatic factors, the influence of temperature on the green-up date of vegetation in Qinling Mountains is the strongest, followed by solar radiation, and the influence of precipitation is the weakest.

The regreening process model based on two-temperature elements has good adaptability in the vegetation area of Qinling Mountains, especially the two-temperature driving model without fixed starting time of driving unit, indicating the important influence of cold shock conditions and thermal driving on vegetation in Qinling Mountains. In the central and western regions with less precipitation, it is more suitable for the thermal + precipitation model, indicating that the vegetation in these areas is not only controlled by temperature, but also affected by precipitation. Warming up promotes plants to break the spring dormancy, while drought will delay the growth and development of plants and delay the phenological period of development.

[1] Piao S, Liu Q, Chen A, et al. Plant phenology and global climate change: Current progresses and challenges[J]. Global Change Biology, 2019, 25(6): 1922-1940.

[2] Nord E A, Lynch J P. Plant phenology: a critical controller of soil resource acquisition[J]. Journal of Experimental Botany, 2009, 60(7): 1927-1937.

[3] Xia J, Niu S, Ciais P, et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology[J]. Proceedings of the National Academy of Sciences, 2015, 112(9): 2788-2793.

[4] Lian X, Piao S, Li L, et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation[J]. Science Advance, 2020, 6(1): x255.

[5] Piao S, Liu Z, Wang T, et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands[J]. Nature Climate Change, 2017, 7(5): 359-363.

[6] Fu Y H, Piao S, Delpierre N, et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology[J]. Global Change Biology, 2018, 24(5): 2159-2168.

[7] Fu Y H, Geng X, Hao F, et al. Shortened temperature‐relevant period of spring leaf‐out in temperate‐zone trees[J]. Global Change Biology, 2019, 25(12): 4282-4290.

[8] Li J, Guan J, Han W, et al. Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China[J]. Frontiers in Plant Science, 2023, 14.

[9] Fan D, Zhao X, Zhu W, et al. Species differences in the green-up date of typical vegetation in Inner Mongolia and climate-driven mechanism based on process-based phenology models[J]. Science of The Total Environment, 2022, 834: 155260.

[10] Jiang S, Chen X, Huang R, et al. Effect of the altitudinal climate change on growing season length for deciduous broadleaved forest in southwest China[J]. Science of The Total Environment, 2022, 828: 154306.

[11] Richardson A D, Keenan T F, Migliavacca M, et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system[J]. Agricultural and forest meteorology, 2013, 169: 156-173.

[12] Geng X, Fu Y H, Fanghua, et al. Climate warming increases spring phenological differences among temperate trees[J]. Global Change Biology, 2020, 26(10): 5979-5987.

[13] Fu Y, Li X, Zhou X, et al. Progress in plant phenology modeling under global climate change[J]. Science China Earth Sciences, 2020, 63(9): 1237-1247.

[14] Zhu W, Jiang N, Chen G, et al. Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2017, 239: 166-175.

[15] Delpierre N, Vitasse Y, Chuine I, et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models[J]. Annals of forest science, 2016, 73(1): 5-25.

[16] Eyshi Rezaei E, Siebert S, Ewert F. Climate and management interaction cause diverse crop phenology trends[J]. Agricultural and Forest Meteorology, 2017, 233: 55-70.

[17] Fu Y H, Zhao H, Piao S, et al. Declining global warming effects on the phenology of spring leaf unfolding[J]. Nature, 2015, 526(7571): 104-107.

[18] 李双双, 延军平, 万佳. 全球气候变化下秦岭南北气温变化特征[J]. 地理科学, 2012, 32(07): 853-858.

[19] Liang L, Wang Q, Guan Q, et al. Assessing vegetation restoration prospects under different environmental elements in cold and arid mountainous region of China[J]. Catena, 2023, 226: 107055.

[20] 李君轶, 傅伯杰, 孙九林, 等. 新时期秦岭生态文明建设：存在问题与发展路径[J]. 自然资源学报, 2021, 36(10): 2449-2463.

[21] 李双双, 段克勤, 王婷, 等. 1970—2018年秦岭南北冷季降雪量时空变化及其影响因素[J]. 地理科学, 2022, 42(01): 163-173.

[22] 李双双, 汪成博, 延军平, 等. 面向事件过程的秦岭南北极端降水时空变化特征[J]. 地理学报, 2020, 75(05): 989-1007.

[23] 李双双, 延军平, 武亚群, 等. 秦岭—淮河南北供暖格局变化及其影响因素[J]. 地理学报, 2019, 74(09): 1866-1877.

[24] 孟清, 白红英, 赵婷, 等. 秦岭山地气候变化的地形效应[J]. 山地学报, 2020, 38(02): 180-189.

[25] 张扬, 白红英, 苏凯, 等. 1960-2013年秦岭陕西段南北坡极端气温变化空间差异[J]. 地理学报, 2018, 73(07): 1296-1308.

[26] 夏浩铭, 李爱农, 赵伟, 等. 2001-2010年秦岭森林物候时空变化遥感监测[J]. 地理科学进展, 2015, 34(10): 1297-1305.

[27] 李双双, 张玉凤, 张立伟, 等. 2000—2019年秦岭南北实际蒸散发时空变化特征[J]. 地理科学进展, 2021, 40(11): 1900-1910.

[28] 李双双, 延军平, 杨赛霓, 等. 1960-2016年秦岭—淮河地区热浪时空变化特征及其影响因素[J]. 地理科学进展, 2018, 37(04): 504-514.

[29] 夏传福, 李静, 柳钦火. 植被物候遥感监测研究进展[J]. 遥感学报, 2013, 17(1): 1-16.

[30] Liu Y, Wu C, Wang X, et al. Contrasting responses of peak vegetation growth to asymmetric warming: Evidences from FLUXNET and satellite observations[J]. Global Change Biology, 2023, 29(8): 2363-2379.

[31] Deng C, Bai H, Gao S, et al. Differences and variations in the elevation-dependent climatic growing season of the northern and southern slopes of the Qinling Mountains of China from 1985 to 2015[J]. Theoretical and Applied Climatology, 2019, 137(1-2): 1159-1169.

[32] 邓晨晖, 白红英, 高山, 等. 1964-2015年气候因子对秦岭地区植物物候的综合影响效应[J]. 地理学报, 2018, 73(05): 917-931.

[33] Shen M, Piao S, Cong N, et al. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau[J]. Global Change Biology, 2015, 21(10): 3647-3656.

[34] Shen M, Zhang G, Cong N, et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2014, 189-190: 71-80.

[35] Wang X, Piao S, Xu X, et al. Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the last three decades?[J]. Global Ecology and Biogeography, 2015, 24(6): 621-631.

[36] Gao M, Wang X, Meng F, et al. Three-dimensional change in temperature sensitivity of northern vegetation phenology[J]. Global Change Biology, 2020, 26(9): 5189-5201.

[37] Yin C, Yang Y, Yang F, et al. Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China[J]. Ecological Indicators, 2021, 131: 108211.

[38] 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.

[39] Piao S, Fang J, Zhou L, et al. Variations in satellite-derived phenology in China's temperate vegetation[J]. Global Change Biology, 2006, 12(4): 672-685.

[40] Liu Q, Fu Y H, Liu Y, et al. Simulating the onset of spring vegetation growth across the Northern Hemisphere[J]. Global Change Biology, 2017, 24(3): 1342-1356.

[41] Liu Q, Piao S, Janssens I A, et al. Extension of the growing season increases vegetation exposure to frost[J]. Nature Communications, 2018, 9(1): 426.

[42] Chen L, Huang J, Ma Q, et al. Spring phenology at different altitudes is becoming more uniform under global warming in Europe[J]. Global Change Biology, 2018, 24(9): 3969-3975.

[43] 邓晨晖. 气候变化背景下秦岭山地物候时空变化及其响应[D]. 西安: 西北大学, 2018.

[44] 杨琪. 秦岭山地植被时空动态变化特征及其响应因子研究[D]. 西安: 西北大学, 2021.

[45] 李建豪, 陶建斌, 程波, 等. 秦岭山区植被春季物候的海拔敏感性[J]. 应用生态学报, 2021, 32(06): 2089-2097.

[46] 邓晨晖, 白红英, 马新萍, 等. 2000-2017年秦岭山地植被物候变化特征及其南北差异[J]. 生态学报, 2021, 41(03): 1068-1080.

[47] Ma P, Zhao J, Zhang H, et al. Increased precipitation leads to earlier green-up and later senescence in Tibetan alpine grassland regardless of warming[J]. Science of The Total Environment, 2023, 871: 162000.

[48] Körner C, Basler D. Phenology under global warming[J]. Science, 2010, 327(5972): 1461-1462.

[49] Gao M, Piao S, Chen A, et al. Divergent changes in the elevational gradient of vegetation activities over the last 30 years[J]. Nature Communications, 2019, 10(1): 2970.

[50] Fu Y H, Zhou X, Li X, et al. Decreasing control of precipitation on grassland spring phenology in temperate China[J]. Global Ecology and Biogeography, 2021, 30(2): 490-499.

[51] Chen X, An S, Inouye D W, et al. Temperature and snowfall trigger alpine vegetation green-up on the world's roof[J]. Global Change Biology, 2015, 21(10): 3635-3646.

[52] Piao S, Tan J, Chen A, et al. Leaf onset in the northern hemisphere triggered by daytime temperature[J]. Nature Communications, 2015, 6(1): 6911.

[53] Fu Y H, Piao S, Zhou X, et al. Short photoperiod reduces the temperature sensitivity of leaf‐out in saplings of Fagus sylvatica but not in horse chestnut[J]. Global Change Biology, 2019, 25(5): 1696-1703.

[54] Fu Y H, Piao S, Zhao H, et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes[J]. Global Change Biology, 2014, 20(12): 3743-3755.

[55] Fu Y H, Piao S, Delpierre N, et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates[J]. Tree Physiology, 2019, 39(8): 1277-1284.

[56] 杨琪, 李书恒, 李家豪, 等. 秦岭森林植被物候及其对气象因子的响应[J]. 干旱区研究, 2021, 38(04): 1065-1074.

[57] Wang X, Xiao J, Li X, et al. No trends in spring and autumn phenology during the global warming hiatus[J]. Nature Communications, 2019, 10(1): 2389.

[58] Shang R, Liu R, Xu M, et al. The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology[J]. Remote Sensing of Environment, 2017, 199: 167-170.

[59] Berra E F, Gaulton R. Remote sensing of temperate and boreal forest 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.

[60] Zhu W, Pan Y, He H, et al. A Changing-Weight Filter Method for Reconstructing a High-Quality NDVI Time Series to Preserve the Integrity of Vegetation Phenology[J]. IEEE transactions on geoscience and remote sensing, 2012, 50(4): 1085-1094.

[61] Cao R, Shen M, Zhou J, et al. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation[J]. Agricultural and forest meteorology, 2018, 249: 176-186.

[62] Chen F, Ding L, Piao S, et al. The Tibetan Plateau as the engine for Asian environmental change: the Tibetan Plateau Earth system research into a new era[J]. Science Bulletin, 2021, 66(13): 1263-1266.

[63] Chen N, Zhang Y, Zu J, et al. The compensation effects of post-drought regrowth on earlier drought loss across the tibetan plateau grasslands[J]. Agricultural and Forest Meteorology, 2020, 281: 107822.

[64] Fu Y H, Zhang X, Piao S, et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time[J]. Global Change Biology, 2019, 25(7): 2410-2418.

[65] Ganjurjav H, Gornish E S, Hu G, et al. Warming and precipitation addition interact to affect plant spring phenology in alpine meadows on the central Qinghai-Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2020, 287: 107943.

[66] Huang M, Piao S, Janssens I A, et al. Velocity of change in vegetation productivity over northern high latitudes[J]. Nature Ecology & Evolution, 2017, 1(11): 1649-1654.

[67] Yao T, Wu F, Ding L, et al. Multispherical interactions and their effects on the Tibetan Plateau's earth system: a review of the recent researches[J]. National Science Review, 2015, 2(4): 468-488.

[68] Wang T, Ottlé C, Peng S, et al. The influence of local spring temperature variance on temperature sensitivity of spring phenology[J]. Global Change Biology, 2014, 20(5): 1473-1480.

[69] Wang C, Cao R, Chen J, et al. Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere[J]. Ecological Indicators, 2015, 50: 62-68.

[70] Wang S, Wang X, Chen G, et al. Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan Plateau, China[J]. Science of The Total Environment, 2017,593-594:449-461.

[71] Shen M, Piao S, Chen X, et al. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau[J]. Global Change Biology, 2016, 22(9): 3057-3066.

[72] Fan D, Zhao X, Zhu W, et al. An improved phenology model for monitoring green-up date variation in Leymus chinensis steppe in Inner Mongolia during 1962–2017[J]. Agricultural and Forest Meteorology, 2020, 291: 108091.

[73] Li S, Yang S, Liu X, et al. NDVI-Based Analysis on the Influence of Climate Change and Human Activities on Vegetation Restoration in the Shaanxi-Gansu-Ningxia Region, Central China[J]. Remote Sensing, 2015, 7(9): 11163-11182.

[74] Liu Q, Piao S, Fu Y H, et al. Climatic Warming Increases Spatial Synchrony in Spring Vegetation Phenology Across the Northern Hemisphere[J]. Geophysical Research Letters, 2019, 46(3): 1641-1650.

[75] Anav A, Liu Q, De Marco A, et al. The role of plant phenology in stomatal ozone flux modeling[J]. Global Change Biology, 2018, 24(1): 235-248.

[76] Chen X, An S, Inouye D W, et al. Temperature and snowfall trigger alpine vegetation green-up on the world's roof[J]. Global Change Biology, 2015, 21(10): 3635-3646.

[77] Cong N, Shen M, Piao S, et al. Little change in heat requirement for vegetation green-up on the Tibetan Plateau over the warming period of 1998–2012[J]. Agricultural and Forest Meteorology, 2017, 232: 650-658.

[78] Sun Q, Li B, Yuan Y, et al. A prognostic phenology model for alpine meadows on the Qinghai–Tibetan Plateau[J]. Ecological Indicators, 2018, 93: 1089-1100.

[79] Tong X, Brandt M, Yue Y, et al. Increased vegetation growth and carbon stock in China karst via ecological engineering[J]. Nature sustainability, 2018, 1(1): 44-50.

[80] Hay J E. Calculation of monthly mean solar radiation for horizontal and inclined surfaces[J]. Solar Energy, 1979, 23(4): 301-307.

[81] Feng X, Li J, Cheng W, et al. Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China[J]. Remote Sensing of Environment, 2017, 196: 253-264.

[82] Chen X, Yu L, Du Z, et al. Distribution of ecological restoration projects associated with land use and land cover change in China and their ecological impacts[J]. Science of The Total Environment, 2022, 825: 153938.

[83] Xu R, Li Y, Teuling A J, et al. Contrasting impacts of forests on cloud cover based on satellite observations[J]. Nature Communications, 2022, 13(1): 670.

[84] Wang X, Wu J, Liu Y, et al. Driving factors of ecosystem services and their spatiotemporal change assessment based on land use types in the Loess Plateau[J]. Journal of Environmental Management, 2022, 311:114835.

[85] Grewe V, Gangoli Rao A, Grönstedt T, et al. Evaluating the climate impact of aviation emission scenarios towards the Paris agreement including COVID-19 effects[J]. Nature Communications, 2021, 12(1): 3841.

[86] Wei F, Wang S, Fu B, et al. Divergent trends of ecosystem‐scale photosynthetic efficiency between arid and humid lands across the globe[J]. Global Ecology and Biogeography, 2022, 31(9): 1824-1837.

[87] Fu B, Wu X, Wang Z, et al. Coupling human and natural systems for sustainability: experience from China's Loess Plateau[J]. Earth System Dynamics, 2022, 13(2): 795-808.

[88] Maia V A, Santos A, de Aguiar-Campos N, et al. The carbon sink of tropical seasonal forests in southeastern Brazil can be under threat[J]. Science Advances, 2020, 6(51): d4548.

[89] Woolf D, Amonette J E, Street-Perrott F A, et al. Sustainable biochar to mitigate global climate change[J]. Nature Communications, 2010, 1(1): 56.

[90] Liu Y, Wu C, Peng D, et al. Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America[J]. Remote Sensing of Environment, 2016, 176: 152-162.

[91] Zheng Z, Zhu W, Chen G, et al. Continuous but diverse advancement of spring-summer phenology in response to climate warming across the Qinghai-Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2016, 223: 194-202.

[92] Satake A, Nagahama A, Sasaki E. A cross-scale approach to unravel the molecular basis of plant phenology in temperate and tropical climates[J]. New Phytologist, 2021, 233: